{ "cells": [ { "cell_type": "markdown", "source": [ "# **Caio Emanoel Serpa Lopes**\n", "---\n", "# **C.E.S.A.R School**\n", "## Análise e Engenharia de Dados - Turma 2021.2\n", "## Disciplina - Modelos Preditivos Conexionistas\n", "## Tutor - **Vitor Casadei**" ], "metadata": { "id": "zR0GfkIL9vZG" } }, { "cell_type": "markdown", "source": [ "# Sobre o Projeto\n" ], "metadata": { "id": "E-l9g2LV9w5a" } }, { "cell_type": "markdown", "source": [ "`Projeto desenvolvido durante a especialização em Análise e Engenharia de Dados da CESAR School.`" ], "metadata": { "id": "ejyyf_pT-Sbb" } }, { "cell_type": "markdown", "source": [ "Para o projeto de RNA, utilizei como modelo o algoritmo de MobileNetV2, devido a dimensão do nosso problema e por ser considerado um problema simples de classificação.\n", "\n", "Ao decorer do projeto, você encontrará etapas de criação do modelo, criação do dataset e treinamento do modelo.\n", "\n", "* Nosso modelo irá classificar imagens entre três diferentes animais (Leão, Cachorro e Gato)." ], "metadata": { "id": "h-AwF9879yly" } }, { "cell_type": "markdown", "source": [ "[Documentação do modelo](https://keras.io/api/applications/mobilenet/)" ], "metadata": { "id": "UzZ8DFLjAg8R" } }, { "cell_type": "markdown", "source": [ "# Importando Bibliotecas" ], "metadata": { "id": "A76OWnJ376xR" } }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "_xwWJkFwFoS2" }, "outputs": [], "source": [ "import os\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import Conv2D,MaxPooling2D,Flatten, Dense, Dropout\n", "from tensorflow.keras.layers import BatchNormalization\n", "from tensorflow.keras.layers import Dense, Dropout, Flatten, GlobalAveragePooling2D\n", "from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2\n", "from tensorflow.keras.preprocessing import image\n", "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", "from tensorflow.keras.optimizers import RMSprop, Adam\n", "from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping, TensorBoard, ModelCheckpoint\n", "from tensorflow.keras.models import load_model" ] }, { "cell_type": "markdown", "source": [ "# Importando Dataset (hospedado no ROBOFLOW)" ], "metadata": { "id": "kTjp2hh47_Nc" } }, { "cell_type": "code", "source": [ "!curl -L \"https://app.roboflow.com/ds/PLCiS5exPa?key=Ut1vk1MeFf\" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip\n", "\n", "\n", "#Movendo pastas para dentro das pastas\n", "%cd /content/\n", "%mkdir images_dir/\n", "# %mkdir images_dir/train\n", "# %mkdir images_dir/test/\n", "# %mkdir images_dir/valid/\n", "%mv train images_dir/\n", "%mv test images_dir/\n", "%mv valid images_dir/ " ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XzfATariFqvR", "outputId": "f1c63b17-9902-495d-eb52-250279ccd15d" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", "100 894 100 894 0 0 2776 0 --:--:-- --:--:-- --:--:-- 2776\n", "100 6752k 100 6752k 0 0 10.5M 0 --:--:-- --:--:-- --:--:-- 24.4M\n", "Archive: roboflow.zip\n", " extracting: README.dataset.txt \n", " extracting: README.roboflow.txt \n", " creating: test/\n", " creating: test/Cachorro/\n", " extracting: test/Cachorro/Cachorro115_jpg.rf.f8788f05b18dcd58b1e147032858aa75.jpg \n", " extracting: test/Cachorro/Cachorro117_jpg.rf.0bbfe83a3237a58cdf202236c871c431.jpg \n", " extracting: test/Cachorro/Cachorro23_jpg.rf.ef6ce37fbcde30ca9fa2131d4850acec.jpg \n", " extracting: test/Cachorro/Cachorro32_jpg.rf.ce18c1fd7b0ef97819bc6f6ee3db61ff.jpg \n", " extracting: test/Cachorro/Cachorro34_jpg.rf.9a3f1c8b6c7930eed620c821ab7e87ed.jpg \n", " extracting: test/Cachorro/Cachorro51_jpg.rf.06b4a97f615e2072b71cf669379e51a1.jpg \n", " extracting: test/Cachorro/Cachorro66_jpg.rf.5906022f1a017015d49c36e22deb9b71.jpg \n", " extracting: test/Cachorro/Cachorro72_jpg.rf.1f37f27288e95c32be7f382fc24388cd.jpg \n", " creating: test/Gato/\n", " extracting: test/Gato/Gato104_jpg.rf.d9205ac11831765ae36f353834f8a2d4.jpg \n", " extracting: test/Gato/Gato112_jpg.rf.9468d3f1c435f8bbb779657014a15ed3.jpg \n", " extracting: test/Gato/Gato114_jpg.rf.173f85f6c199118131a0c2fff1a4d3a9.jpg \n", " extracting: test/Gato/Gato15_jpg.rf.7f4926a3c4ac37944563365a1306f9c2.jpg \n", " extracting: test/Gato/Gato21_jpg.rf.2cd2044b94b4accb680bfdcf9f768c0e.jpg \n", " extracting: test/Gato/Gato34_jpg.rf.8927575abd82537e36c6e361faeaa3db.jpg \n", " extracting: test/Gato/Gato47_jpg.rf.3df5bcb8e030172383fb8105d4ee8418.jpg \n", " extracting: test/Gato/Gato49_jpg.rf.dee0be577e5d5676d6efc26cd0e6580a.jpg \n", " extracting: test/Gato/Gato56_jpg.rf.fe74d10f44df8bb0a272f3b47b2ee5f6.jpg \n", " extracting: test/Gato/Gato64_jpg.rf.0dbd152c64011079bac1027e53a05194.jpg \n", " extracting: test/Gato/Gato83_jpg.rf.bee110046675d797fbd36b9e05e1279a.jpg \n", " extracting: test/Gato/Gato84_jpg.rf.fd1d7e9543d74587ab7f75497d8766fd.jpg \n", " extracting: test/Gato/Gato86_jpg.rf.e24fa6987fde714b6baf259a604b0571.jpg \n", " extracting: test/Gato/Gato89_jpg.rf.1c2b7e95502ee463514461c8a43e3fa2.jpg \n", " extracting: test/Gato/Gato98_jpg.rf.7b92776cc2e78cda6558783561fda99b.jpg \n", " extracting: test/Gato/Gato9_jpg.rf.5afa8d19323ebf32cf27ad749ae99f2f.jpg \n", " creating: test/Leão/\n", " extracting: test/Leão/Leao112_jpg.rf.1aef17a43adcf57ac677591660c05eef.jpg \n", " extracting: test/Leão/Leao202_jpg.rf.266a5b1d5533891ca99ff8017ef325dc.jpg \n", " extracting: test/Leão/Leao34_jpg.rf.6a4984805b0db064218ad335eab27eea.jpg \n", " extracting: test/Leão/Leao49_jpg.rf.79217069a1598181ff65cb629e5f0481.jpg \n", " extracting: test/Leão/Leao64_jpg.rf.14d78a431d3c8fe95350aa4dabb07ece.jpg \n", " extracting: test/Leão/Leao89_jpg.rf.0e7bbbf9c9b7851dd9a51f54177e80aa.jpg \n", " extracting: test/Leão/Leao93_jpg.rf.e56efe8140028c2d32abb9768b098c6a.jpg \n", " extracting: test/Leão/Leao96_jpg.rf.2e2e6f0c702e4d8ef6cbe2ad1e3ec241.jpg \n", " extracting: test/Leão/leao1233_jpg.rf.3f97a56193287c4e0af038e2326be104.jpg \n", " creating: train/\n", " creating: train/Cachorro/\n", " extracting: train/Cachorro/Cachorro100_jpg.rf.e45c82aa6e4a38e65b8d1ea4f70a4007.jpg \n", " extracting: train/Cachorro/Cachorro102_jpg.rf.2c0fd984d3f4aaaa20e2c39e4ba75748.jpg \n", " extracting: train/Cachorro/Cachorro104_jpg.rf.c22b3feb52dbd249c1074b1fbff433db.jpg \n", " extracting: train/Cachorro/Cachorro107_jpg.rf.116e8814256a94ee54d1e85553ddf64a.jpg \n", " extracting: train/Cachorro/Cachorro10_jpg.rf.e5afd752626b5bb04b741ae4a3ec1bb0.jpg \n", " extracting: train/Cachorro/Cachorro112_jpg.rf.0b69ad6c2a45e0e93e2f49fb2f6fb8f3.jpg \n", " extracting: train/Cachorro/Cachorro114_jpg.rf.cdb7785d32c6b2dea48b1dffc50d5738.jpg \n", " extracting: train/Cachorro/Cachorro116_jpg.rf.770d1e6fcf4aec3ae69c2ff9dd12f3fe.jpg \n", " extracting: train/Cachorro/Cachorro118_jpg.rf.772fb425cf97b335b8014c494e0f14b6.jpg \n", " extracting: train/Cachorro/Cachorro120_jpg.rf.75d95cb9df4d62160dee6620d7a061f6.jpg \n", " extracting: train/Cachorro/Cachorro121_jpg.rf.bf4257a9fef96c0eda20928d27f0e385.jpg \n", " extracting: train/Cachorro/Cachorro122_jpg.rf.0e734d7f3176eabb7ee8fb849a823d11.jpg \n", " extracting: train/Cachorro/Cachorro123_jpg.rf.6ac72f8378f7e8c74efecc7dded5e131.jpg \n", " extracting: train/Cachorro/Cachorro127_jpg.rf.ca89bdd27e09c15b7422dfb33150d787.jpg \n", " extracting: train/Cachorro/Cachorro12_jpg.rf.d1ce39c3aaa6fd55399452fcc96b28a4.jpg \n", " extracting: train/Cachorro/Cachorro13_jpg.rf.1c06066d87f974d51201235cfb997be3.jpg \n", " extracting: train/Cachorro/Cachorro14_jpg.rf.2d7af2fc63f9d355cfdb35d64454e564.jpg \n", " extracting: train/Cachorro/Cachorro15_jpg.rf.cc9c0a6ca7e2726d1bc57665c2152ed0.jpg \n", " extracting: train/Cachorro/Cachorro16_jpg.rf.992d3c27fce56b515963561b61302c38.jpg \n", " extracting: train/Cachorro/Cachorro19_jpg.rf.16cc0322e4c797e37641be4a15946cb0.jpg \n", " extracting: train/Cachorro/Cachorro1_jpg.rf.84c9c926a88a50aaceedf890a0925304.jpg \n", " extracting: train/Cachorro/Cachorro20_jpg.rf.1648ddd310628d4f0f95969ff2182c5f.jpg \n", " extracting: train/Cachorro/Cachorro21_jpg.rf.c72d942eee0e187d12f5952eea694dc9.jpg \n", " extracting: train/Cachorro/Cachorro22_jpg.rf.e9561e0d96e7d852b9079415f4ddeb50.jpg \n", " extracting: train/Cachorro/Cachorro26_jpg.rf.74fb80f73436779380f64b3617cec4d4.jpg \n", " extracting: train/Cachorro/Cachorro27_jpg.rf.ab51f490df0c50bca68bb344454fb001.jpg \n", " extracting: train/Cachorro/Cachorro29_jpg.rf.ef24e9817f32bd35ca4d6260c2f76536.jpg \n", " extracting: train/Cachorro/Cachorro2_jpg.rf.1e5969145bff86f60b2aaf04a67a8a75.jpg \n", " extracting: train/Cachorro/Cachorro30_jpg.rf.693aec87f5f6cff3e6929335dd2c4246.jpg \n", " extracting: train/Cachorro/Cachorro33_jpg.rf.e5b539a44c146997123aced6dca84aa9.jpg \n", " extracting: train/Cachorro/Cachorro35_jpg.rf.798512f276c3222233979fa6e9f870d4.jpg \n", " extracting: train/Cachorro/Cachorro36_jpg.rf.51ddd8129c74534fb85d6901ba281d81.jpg \n", " extracting: train/Cachorro/Cachorro37_jpg.rf.cea9027fedb78fe83e74d574097e47c4.jpg \n", " extracting: train/Cachorro/Cachorro38_jpg.rf.0db2ec3315023c1a63578d0c30c37c9c.jpg \n", " extracting: train/Cachorro/Cachorro39_jpg.rf.ce420075d72fc585f38da9f0e9133d4a.jpg \n", " extracting: train/Cachorro/Cachorro44_jpg.rf.5d4c30f6d0e672a88a5d8db8a4778cba.jpg \n", " extracting: train/Cachorro/Cachorro45_jpg.rf.f2961ef87a1eb128a8cb63c298c62077.jpg \n", " extracting: train/Cachorro/Cachorro47_jpg.rf.3053afaf73606251a1137e28374f58d8.jpg \n", " extracting: train/Cachorro/Cachorro48_jpg.rf.2b94f0628d967a149822f5cdfbe78269.jpg \n", " extracting: train/Cachorro/Cachorro49_jpg.rf.179c3e35f3d81a87676e24113c513906.jpg \n", " extracting: train/Cachorro/Cachorro50_jpg.rf.6ad1f764598bf63e3414e8b44b7c56e3.jpg \n", " extracting: train/Cachorro/Cachorro54_jpg.rf.f015afbdb48815609961a214b1a2516b.jpg \n", " extracting: train/Cachorro/Cachorro57_jpg.rf.32af92c7853e90ce30cf140d14bbd035.jpg \n", " extracting: train/Cachorro/Cachorro59_jpg.rf.59979d3c29653732e95cfaeb3060e90f.jpg \n", " extracting: train/Cachorro/Cachorro5_jpg.rf.e47592d53bd75f56b88d89f022093526.jpg \n", " extracting: train/Cachorro/Cachorro62_jpg.rf.16d2260f666c3695bd407d62c16891aa.jpg \n", " extracting: train/Cachorro/Cachorro63_jpg.rf.6fee3288521ee99d0c00c8bad8eb935f.jpg \n", " extracting: train/Cachorro/Cachorro69_jpg.rf.1e7afdaa2d4354ac9ce67a21bb39e381.jpg \n", " extracting: train/Cachorro/Cachorro6_jpg.rf.ac8376f0092c94e2ff2aab8fdf03d8fb.jpg \n", " extracting: train/Cachorro/Cachorro71_jpg.rf.c2cf0b2475b2089cf1be098d9e5bc607.jpg \n", " extracting: train/Cachorro/Cachorro73_jpg.rf.c55d5fa06fce5f7965746b6d4807bfec.jpg \n", " extracting: train/Cachorro/Cachorro74_jpg.rf.780189709e0dcf60df077c3627d5f600.jpg \n", " extracting: train/Cachorro/Cachorro76_jpg.rf.c2d52cb730c920f278559946a4020770.jpg \n", " extracting: train/Cachorro/Cachorro77_jpg.rf.4b381f4e36e86e441c4513b50941cc9f.jpg \n", " extracting: train/Cachorro/Cachorro78_jpg.rf.4bc2c9436a60dd73cf1a7a4bb2ccb00c.jpg \n", " extracting: train/Cachorro/Cachorro79_jpg.rf.e2ffb3e85852327c622742d94d067108.jpg \n", " extracting: train/Cachorro/Cachorro80_jpg.rf.efead1a70a2c042cf1f9df0983f68a5b.jpg \n", " extracting: train/Cachorro/Cachorro81_jpg.rf.14b4d63f7788f6137afb998efd9c5b43.jpg \n", " extracting: train/Cachorro/Cachorro83_jpg.rf.0dfc90d8674a62adfa04b780ce9a5a29.jpg \n", " extracting: train/Cachorro/Cachorro84_jpg.rf.b8b0d2bc12102413495ffda1f282639e.jpg \n", " extracting: train/Cachorro/Cachorro88_jpg.rf.94aabaf9576d6f4412415219120f48a8.jpg \n", " extracting: train/Cachorro/Cachorro8_jpg.rf.6f0a404ebee927a12832059660e9db1e.jpg \n", " extracting: train/Cachorro/Cachorro91_jpg.rf.1a6cab671b83476ecb4e74b99a1543c2.jpg \n", " extracting: train/Cachorro/Cachorro92_jpg.rf.54ce30350cbf1fd214a9a8d2067baed1.jpg \n", " extracting: train/Cachorro/Cachorro93_jpg.rf.42500ae158171b2282d909d83cb2c3fb.jpg \n", " extracting: train/Cachorro/Cachorro94_jpg.rf.e80327503f040847e90fec3dda04918a.jpg \n", " extracting: train/Cachorro/Cachorro95_jpg.rf.c0bcae7a963ec8536a717361c9d06e93.jpg \n", " extracting: train/Cachorro/Cachorro98_jpg.rf.1f35ac3c028c7ed25721685ec3a0126f.jpg \n", " creating: train/Gato/\n", " extracting: train/Gato/Gato101_jpg.rf.0c955c13c731612aa098251b0ddaacb1.jpg \n", " extracting: train/Gato/Gato103_jpg.rf.8c18f4ceb0b2c56ac22667d7f985b691.jpg \n", " extracting: train/Gato/Gato105_jpg.rf.811c082f9a6f384318298d1ffb034aa9.jpg \n", " extracting: train/Gato/Gato106_jpg.rf.a5ecb384fbeef919b8959f0cf56821da.jpg \n", " extracting: train/Gato/Gato10_jpg.rf.3ebfd61d9fa2972f5e2349bb67071c5f.jpg \n", " extracting: train/Gato/Gato110_jpg.rf.e0330bdba372973e49c954056150258b.jpg \n", " extracting: train/Gato/Gato113_jpg.rf.876014e8d845ac26a955c4d609e802d8.jpg \n", " extracting: train/Gato/Gato115_jpg.rf.e5c9b16bb4127b0d73f75abf8c493dd0.jpg \n", " extracting: train/Gato/Gato119_jpg.rf.8ed64a0a590e0b1fb21410d9e64e2a1d.jpg \n", " extracting: train/Gato/Gato11_jpg.rf.5c0f9d40d14a6da53d5dc6279f07d50e.jpg \n", " extracting: train/Gato/Gato120_jpg.rf.4e194cd5ce2dc3996327a290f1645c56.jpg \n", " extracting: train/Gato/Gato121_jpg.rf.0894c8b7afc1e0e18742cc2f14e77a2c.jpg \n", " extracting: train/Gato/Gato122_jpg.rf.13b909d74b939f7ca580ebc0625d4a4b.jpg \n", " extracting: train/Gato/Gato14_jpg.rf.c2d0f13a0132ee3a60c7e260e2daad51.jpg \n", " extracting: train/Gato/Gato152_jpg.rf.b0cf34a0d197b3db427022c959e17315.jpg \n", " extracting: train/Gato/Gato16_jpg.rf.4bd23f0c816565fe23391f469e60d6cc.jpg \n", " extracting: train/Gato/Gato17_jpg.rf.93a42ded327b7b679299c94bf735e2c1.jpg \n", " extracting: train/Gato/Gato18_jpg.rf.ed8100a70f9bc89aeda0736127f10735.jpg \n", " extracting: train/Gato/Gato20_jpg.rf.46769b59a25e4974712bcffe2769d878.jpg \n", " extracting: train/Gato/Gato22_jpg.rf.68d67661066d5692c608fb3ac819f369.jpg \n", " extracting: train/Gato/Gato25_jpg.rf.78912ad6df6d21439a567bf2e2a36fe3.jpg \n", " extracting: train/Gato/Gato26_jpg.rf.375f84af7a3b1d79208bb63c674b1bd1.jpg \n", " extracting: train/Gato/Gato28_jpg.rf.77efe501d0bb4966befce42fe5565874.jpg \n", " extracting: train/Gato/Gato29_jpg.rf.91567190c0f1cca1bdb4c4ace2c337fa.jpg \n", " extracting: train/Gato/Gato2_jpg.rf.03e7a9147e1f4d044b4fbad9043c7f99.jpg \n", " extracting: train/Gato/Gato30_jpg.rf.7854f8755b82971dfbe2a6d935e37e5e.jpg \n", " extracting: train/Gato/Gato33_jpg.rf.2a6e692a92a92649740ce83df902fe43.jpg \n", " extracting: train/Gato/Gato36_jpg.rf.8fd53ef4b0378d62da94df9c0e4719c7.jpg \n", " extracting: train/Gato/Gato37_jpg.rf.756e05ecb4e5647e2961f0c4800df843.jpg \n", " extracting: train/Gato/Gato39_jpg.rf.21bd81c1c7a4a344378a4c344bcf1184.jpg \n", " extracting: train/Gato/Gato3_jpg.rf.aaa37ff2885cb5334dc9537d3e0bc6e7.jpg \n", " extracting: train/Gato/Gato40_jpg.rf.adb215b6c6eac142720ba800772eeeb2.jpg \n", " extracting: train/Gato/Gato41_jpg.rf.8ee732cf9a9c02b9feb2943442a33755.jpg \n", " extracting: train/Gato/Gato42_jpg.rf.f15f901f53b401d5e8b942ac085302dc.jpg \n", " extracting: train/Gato/Gato43_jpg.rf.60ed8332fa1e3fd0f3e9594199844252.jpg \n", " extracting: train/Gato/Gato45_jpg.rf.14ceda66f61376d6e5997fb43975eb7a.jpg \n", " extracting: train/Gato/Gato48_jpg.rf.017928a139b5a023c55b68cdda3603f3.jpg \n", " extracting: train/Gato/Gato50_jpg.rf.b950c7506860f0ab9b723c6009c0f22c.jpg \n", " extracting: train/Gato/Gato51_jpg.rf.ffbf53c3d61ce0159334d806694b9963.jpg \n", " extracting: train/Gato/Gato52_jpg.rf.5bd8a374c8942780758e23a922535556.jpg \n", " extracting: train/Gato/Gato54_jpg.rf.e317bd05412782f872033b0d41851d82.jpg \n", " extracting: train/Gato/Gato59_jpg.rf.f68602ddf464a24f0f55a78aaae5ff62.jpg \n", " extracting: train/Gato/Gato5_jpg.rf.4bcbbb88c28ec8a81a3b6b72e55d1d18.jpg \n", " extracting: train/Gato/Gato65_jpg.rf.289cee91b7c9f7bfa7b54cf8c8ba65a6.jpg \n", " extracting: train/Gato/Gato66_jpg.rf.dffa66bcde369e7f05305e1628114b30.jpg \n", " extracting: train/Gato/Gato67_jpg.rf.926be2d12a75b74ab9704dd5f63a61e4.jpg \n", " extracting: train/Gato/Gato69_jpg.rf.57ccabfade3f53d0f45b7945712e87c7.jpg \n", " extracting: train/Gato/Gato6_jpg.rf.40ba7e5507ce25bfc9f26e27a2e6eb26.jpg \n", " extracting: train/Gato/Gato70_jpg.rf.d69eb99c63edfbef4a9e9e05f66153ba.jpg \n", " extracting: train/Gato/Gato71_jpg.rf.1d1413cde8edc876bcd39fde6c0e37e3.jpg \n", " extracting: train/Gato/Gato72_jpg.rf.cc3b7566b569ac3a6d695291aba1cb90.jpg \n", " extracting: train/Gato/Gato73_jpg.rf.5e6617695ef565bc135b2c434e454eaf.jpg \n", " extracting: train/Gato/Gato74_jpg.rf.dd7679167aaee54f3f4fc88ba4d68f86.jpg \n", " extracting: train/Gato/Gato76_jpg.rf.50d2589dce6b5601d46e3536c4de6723.jpg \n", " extracting: train/Gato/Gato77_jpg.rf.6f4372c10f06e462e0bf9a934ebd104d.jpg \n", " extracting: train/Gato/Gato79_jpg.rf.2abf0e962f1017dae68d27f344bb3da0.jpg \n", " extracting: train/Gato/Gato80_jpg.rf.ae3b3e965bb3fe0313b2e6610e459f1d.jpg \n", " extracting: train/Gato/Gato81_jpg.rf.bc1c5ba52a45478f6217c45f7a08f969.jpg \n", " extracting: train/Gato/Gato82_jpg.rf.5b6cc8a76e04166cbc6f656ca35cc86c.jpg \n", " extracting: train/Gato/Gato87_jpg.rf.0ac1573fe4a3f58e29558c234ba10742.jpg \n", " extracting: train/Gato/Gato88_jpg.rf.44e11bcbf4f04f9315d7483d4626b06d.jpg \n", " extracting: train/Gato/Gato8_jpg.rf.3faae38bfc69b48d28f31b7fc662bb97.jpg \n", " extracting: train/Gato/Gato93_jpg.rf.d47a076d5a93ecf7234c87afe02bb1f0.jpg \n", " extracting: train/Gato/Gato94_jpg.rf.193562ef374251ee33315774a445f813.jpg \n", " extracting: train/Gato/Gato95_jpg.rf.aa22cc515f9ec3fc52f38935380a47ba.jpg \n", " extracting: train/Gato/Gato96_jpg.rf.952be5229b50b3658c6c6efd9bf7d520.jpg \n", " extracting: train/Gato/Gato97_jpg.rf.21935f84f753ef37d69f1a252096f76a.jpg \n", " extracting: train/Gato/Gato99_jpg.rf.b2292f9de09dd70f16c4deaea2d19686.jpg \n", " extracting: train/Gato/gato112_jpg.rf.b56092e638a3966bbd277b8e96822fe6.jpg \n", " creating: train/Leão/\n", " extracting: train/Leão/Leao105_jpg.rf.715fe41c7bf0c5ed5eb468bea760ea16.jpg \n", " extracting: train/Leão/Leao106_jpg.rf.ea2cc4f96f38a1ce224bdf4e863bb6c2.jpg \n", " extracting: train/Leão/Leao107_jpg.rf.0816d7e2f71359105a7c058dda73c1d5.jpg \n", " extracting: train/Leão/Leao11123_jpg.rf.6c18cbe8dbc840a3e410cabcf3c9bcd7.jpg \n", " extracting: train/Leão/Leao114_jpg.rf.2583bc2179644bdf5de5924a3efb6bdc.jpg \n", " extracting: train/Leão/Leao115_jpg.rf.c6ca4e4be21cb0441caa6768d368efe7.jpg \n", " extracting: train/Leão/Leao117_jpg.rf.5060b0fecbdb8f0eefca52803b3a4cc3.jpg \n", " extracting: train/Leão/Leao119_jpg.rf.8791c98c20771cdae65db45e8f7fb853.jpg \n", " extracting: train/Leão/Leao120_jpg.rf.0c6e800c02270334011f1cf71fb03cda.jpg \n", " extracting: train/Leão/Leao1222_jpg.rf.71171f5d3bc606a220beba30835df1e4.jpg \n", " extracting: train/Leão/Leao122_jpg.rf.e698630098b2990902ae5a2450a649a6.jpg \n", " extracting: train/Leão/Leao12334_jpg.rf.1e7d2d5258e26ff8b7586d6ba81e22d8.jpg \n", " extracting: train/Leão/Leao1233_jpg.rf.005bd0a5e64cbf739fb6de7bc1f28330.jpg \n", " extracting: train/Leão/Leao124_jpg.rf.478da523c61b4e1191754f457f9c3dc7.jpg \n", " extracting: train/Leão/Leao125_jpg.rf.5d62721ccd4de3ff4d4b1653f4b290c1.jpg \n", " extracting: train/Leão/Leao126_jpg.rf.fb2c33bad8f18486119f7eafa4701102.jpg \n", " extracting: train/Leão/Leao1272_jpg.rf.2223e0e88b1a9ee23d7636702f1c6d8f.jpg \n", " extracting: train/Leão/Leao128_jpg.rf.3f8f38633d83cc358aa820b7f4161e70.jpg \n", " extracting: train/Leão/Leao137_jpg.rf.60c140bd737854c19fd4b4a83f7f8cff.jpg \n", " extracting: train/Leão/Leao140_jpg.rf.5de1301b249131ae93d11083fc85d4d2.jpg \n", " extracting: train/Leão/Leao141_jpg.rf.c73abd58493606a97efcb0542effed51.jpg \n", " extracting: train/Leão/Leao142_jpg.rf.ca101d632b9f90e75af8d849d169c441.jpg \n", " extracting: train/Leão/Leao149_jpg.rf.7f46816b04e68f76d2c4969c3e152aef.jpg \n", " extracting: train/Leão/Leao14_jpg.rf.f747973c71c8692564d9bef5b7c57a4a.jpg \n", " extracting: train/Leão/Leao152_jpg.rf.7fcc67fd0dd280e15e97a988dbbca193.jpg \n", " extracting: train/Leão/Leao153_jpg.rf.556cf55f7c1751524ddf0a18b69d4c04.jpg \n", " extracting: train/Leão/Leao155_jpg.rf.608c35c40a396d00d7e114889c4d8a2e.jpg \n", " extracting: train/Leão/Leao157_jpg.rf.7fc95b1afc1015a05c71e4a5874033b2.jpg \n", " extracting: train/Leão/Leao161_jpg.rf.212f546ab66efeaee2fdc6065b11ff4e.jpg \n", " extracting: train/Leão/Leao162_jpg.rf.a4c69f4fb9b0a8d362b4d32b78bb92d6.jpg \n", " extracting: train/Leão/Leao164_jpg.rf.43bc8d4fa901727cb89bec4946b4af7b.jpg \n", " extracting: train/Leão/Leao166_jpg.rf.5840828cb35ca9ad6c2854c4712be016.jpg \n", " extracting: train/Leão/Leao171_jpg.rf.aecbbc8cc75da8219fbd8451a35c899d.jpg \n", " extracting: train/Leão/Leao172_jpg.rf.a5e7d357051f8ceee398c387a22497f6.jpg \n", " extracting: train/Leão/Leao182_jpg.rf.1fb432c353227e7633cad118212d8517.jpg \n", " extracting: train/Leão/Leao183_jpg.rf.51f044bda8d529f7a3eed7bc61fd4dfc.jpg \n", " extracting: train/Leão/Leao184_jpg.rf.2d2004d1f7819dca0450ce84bad90b52.jpg \n", " extracting: train/Leão/Leao190_jpg.rf.80ea73ce609a131df593ed6265ac876b.jpg \n", " extracting: train/Leão/Leao193_jpg.rf.2502ee2808125416d963e69219aa801d.jpg \n", " extracting: train/Leão/Leao196_jpg.rf.928eacd71a735ad0f0cf89e610867b3d.jpg \n", " extracting: train/Leão/Leao197_jpg.rf.910b75c3054eae2e85f4cfa06c535309.jpg \n", " extracting: train/Leão/Leao199_jpg.rf.825d6e6559f37ef51df774e3d1782e71.jpg \n", " extracting: train/Leão/Leao200_jpg.rf.23ed08708ecba007aaa5d390cc913d2e.jpg \n", " extracting: train/Leão/Leao3333_jpg.rf.5a35ff1369ad651c2722023e59a85cdd.jpg \n", " extracting: train/Leão/Leao3345_jpeg.rf.842b449d0e1c62334446f6e8a91c3764.jpg \n", " extracting: train/Leão/Leao35_jpg.rf.238b6add668bea15cb2b0b1522bdc8cc.jpg \n", " extracting: train/Leão/Leao36_jpg.rf.89a42ffc9cf7b464f94b6ebc8b67b3cf.jpg \n", " extracting: train/Leão/Leao37_jpg.rf.00e53369c54e6bd61bbd9915178890ed.jpg \n", " extracting: train/Leão/Leao38_jpg.rf.f88e897597ee8422f196dd5ce71150bf.jpg \n", " extracting: train/Leão/Leao39_jpg.rf.91887b2be63012e71d53963d4143a735.jpg \n", " extracting: train/Leão/Leao40_jpg.rf.afef71319f6edc0d781ac82c30afde47.jpg \n", " extracting: train/Leão/Leao42_jpg.rf.223d4db79d3c9a7aff41c7a6b610ee95.jpg \n", " extracting: train/Leão/Leao43_jpg.rf.c8584be302d9a5bcd7cc23b76399d35f.jpg \n", " extracting: train/Leão/Leao44_jpg.rf.a54db14d5fd5cfd5cf2299c0d1e0eef1.jpg \n", " extracting: train/Leão/Leao45_jpg.rf.0e8945a02381e5d234f6e1bf6e1ea095.jpg \n", " extracting: train/Leão/Leao58_jpg.rf.32a78833d150314256ddee9257631a18.jpg \n", " extracting: train/Leão/Leao60_jpg.rf.c87bc23867e23f8c5193566f0bf63faf.jpg \n", " extracting: train/Leão/Leao61_jpg.rf.d9dcb3b0bb4f2eeabfbe7a678fa85b70.jpg \n", " extracting: train/Leão/Leao65_jpg.rf.e7829f38cfd7e8888e1f9d843d0fd9ce.jpg \n", " extracting: train/Leão/Leao68_jpg.rf.ec7f1148a5c428dcae74b6a4a39bf270.jpg \n", " extracting: train/Leão/Leao75_jpg.rf.3fb6710f31badfc5e84f7d3261431824.jpg \n", " extracting: train/Leão/Leao78_jpg.rf.fef44b242c229b850764459743dbaaf7.jpg \n", " extracting: train/Leão/Leao80_jpg.rf.4c5a19d25b406ac736e47e299580f693.jpg \n", " extracting: train/Leão/Leao82_jpg.rf.a09a21d5b9ded813ef64cafd5fe73fac.jpg \n", " extracting: train/Leão/Leao83_jpg.rf.521b4624c4fd3545390b8a4952e9972d.jpg \n", " extracting: train/Leão/Leao84_jpg.rf.5055020df427761847b00dd1a855ef8c.jpg \n", " extracting: train/Leão/Leao88_jpg.rf.276d2db8a0f4b8edbbceec5fd70138d5.jpg \n", " extracting: train/Leão/Leao91_jpg.rf.facee9271f923f00923a745e403ddad3.jpg \n", " extracting: train/Leão/Leao94_jpg.rf.3f3ae3c47c3563172629e02a5c8a4050.jpg \n", " extracting: train/Leão/Leao95_jpg.rf.c7e498955dddf026ad3a94fb938dcd18.jpg \n", " extracting: train/Leão/leao112_jpeg.rf.b1896bc8806ab0dff10174548d73e7cf.jpg \n", " creating: valid/\n", " creating: valid/Cachorro/\n", " extracting: valid/Cachorro/Cachorro108_jpg.rf.9cf0fd42e9b23cefe520164146475b9a.jpg \n", " extracting: valid/Cachorro/Cachorro109_jpg.rf.33f668052da1868576279c8bf856be2b.jpg \n", " extracting: valid/Cachorro/Cachorro110_jpg.rf.40f4e9aadbdf6886c9382377952e5942.jpg \n", " extracting: valid/Cachorro/Cachorro119_jpg.rf.d90a0d86102cb0f03ff618fd6a28b378.jpg \n", " extracting: valid/Cachorro/Cachorro124_jpg.rf.da0d45b36f7abdbe733e38df66b88151.jpg \n", " extracting: valid/Cachorro/Cachorro128_jpg.rf.b4c13e9a824c3575f99ef3703bef2e51.jpg \n", " extracting: valid/Cachorro/Cachorro17_jpg.rf.2bf0189204babd4080a07ef2206de5ce.jpg \n", " extracting: valid/Cachorro/Cachorro25_jpg.rf.c7bbe2560779b1e037776c2ed225348b.jpg \n", " extracting: valid/Cachorro/Cachorro28_jpg.rf.18caec105ad95b29b1b007d40501ca67.jpg \n", " extracting: valid/Cachorro/Cachorro53_jpg.rf.5f4b0a048312c18b0f628bab409a5d7f.jpg \n", " extracting: valid/Cachorro/Cachorro56_jpg.rf.7c61596a848f161961d80b403611c35f.jpg \n", " extracting: valid/Cachorro/Cachorro58_jpg.rf.652ce5ac67f4e7242b9df100ec105f6c.jpg \n", " extracting: valid/Cachorro/Cachorro60_jpg.rf.207477d7f22cb5ff327ac2f0ba4c02ed.jpg \n", " extracting: valid/Cachorro/Cachorro61_jpg.rf.0221e5719b39300a16c1b24ecbe38836.jpg \n", " extracting: valid/Cachorro/Cachorro65_jpg.rf.8b2dba684dd6e5b927c4e1ded676b7cf.jpg \n", " extracting: valid/Cachorro/Cachorro67_jpg.rf.992438c643bb585f42d31b992b97671e.jpg \n", " extracting: valid/Cachorro/Cachorro70_jpg.rf.0bd154556f255fbb9579c35a545ea57e.jpg \n", " extracting: valid/Cachorro/Cachorro75_jpg.rf.117b1912017e7c64f19d04b653c01b6e.jpg \n", " extracting: valid/Cachorro/Cachorro7_jpg.rf.86384f914041c85d40d2da745da5a190.jpg \n", " extracting: valid/Cachorro/Cachorro82_jpg.rf.458b038798449a6704804bc2734bc835.jpg \n", " extracting: valid/Cachorro/Cachorro86_jpg.rf.6b6fb24f064efc166b7271aec1ace68b.jpg \n", " extracting: valid/Cachorro/Cachorro87_jpg.rf.95f46bd286dd9d44acea8276faffb728.jpg \n", " extracting: valid/Cachorro/Cachorro89_jpg.rf.d41bac44189c71d481a1c1e32a878d5c.jpg \n", " extracting: valid/Cachorro/Cachorro99_jpg.rf.876ff115d7b1d056bbb9852987f46e7e.jpg \n", " creating: valid/Gato/\n", " extracting: valid/Gato/Gato100_jpg.rf.251bc10abbfedd8e2e5848d245a95202.jpg \n", " extracting: valid/Gato/Gato116_jpg.rf.cec2b1c62ec7f1834fc33a73d4b06f8a.jpg \n", " extracting: valid/Gato/Gato117_jpg.rf.fe7507360c023ab522321ec67dbe3ac7.jpg \n", " extracting: valid/Gato/Gato19_jpg.rf.8b43413ed7c1e1cbe5f591a8d5292989.jpg \n", " extracting: valid/Gato/Gato27_jpg.rf.b6d141cef50eaaed1a9e3ea8e3a7f84d.jpg \n", " extracting: valid/Gato/Gato32_jpg.rf.a6dc2f6a7d0025d1116322df10fd698c.jpg \n", " extracting: valid/Gato/Gato35_jpg.rf.5ce7d51078a7be56c0ec8e20730729b7.jpg \n", " extracting: valid/Gato/Gato38_jpg.rf.656330daf7d765f23b5a22a39f5b93b6.jpg \n", " extracting: valid/Gato/Gato44_jpg.rf.ea04908c9e783eff9c3ff5174420b055.jpg \n", " extracting: valid/Gato/Gato53_jpg.rf.b1b219a3d6bcc249e176063d42a895f3.jpg \n", " extracting: valid/Gato/Gato58_jpg.rf.d8fadd399213b001860a66cdb34da197.jpg \n", " extracting: valid/Gato/Gato60_jpg.rf.a75182faddba605ec69cb19d471a45df.jpg \n", " extracting: valid/Gato/Gato68_jpg.rf.96eebfb9ea450f93eae4eb7569113f0c.jpg \n", " extracting: valid/Gato/Gato78_jpg.rf.4344e12fda89c5abfdc799714965904b.jpg \n", " extracting: valid/Gato/Gato7_jpg.rf.82ce4a53610d693cec0e735b1f0c4564.jpg \n", " extracting: valid/Gato/Gato85_jpg.rf.a096ef4817178dc4692efbfa7177726a.jpg \n", " creating: valid/Leão/\n", " extracting: valid/Leão/Leao102_jpg.rf.e0af765ba365e82dd3b3df1a129b8adc.jpg \n", " extracting: valid/Leão/Leao118_jpg.rf.220eec2d11f1de022894230a47b8ff37.jpg \n", " extracting: valid/Leão/Leao127_jpg.rf.00927834604dfc0436843eed952adef2.jpg \n", " extracting: valid/Leão/Leao129_jpg.rf.78b041c76660c2d7aa8cc7084b782876.jpg \n", " extracting: valid/Leão/Leao135_jpg.rf.1eea9d5e0c50472c661d3fbb9a4f1d74.jpg \n", " extracting: valid/Leão/Leao158_jpg.rf.0a8699ac5353c2f104846fb32e6d9186.jpg \n", " extracting: valid/Leão/Leao15_jpg.rf.47f576c055a0604d89f05e443812120d.jpg \n", " extracting: valid/Leão/Leao163_jpg.rf.f402fd237326a0e208a31c20f1f47f9c.jpg \n", " extracting: valid/Leão/Leao48_jpg.rf.5590199b925a380d059af9e5287aab82.jpg \n", " extracting: valid/Leão/Leao52_jpg.rf.cf562b65de1204b6795dff51bbc3c05b.jpg \n", " extracting: valid/Leão/Leao53_jpg.rf.d99c4f88c12740dc65218b2183e18564.jpg \n", " extracting: valid/Leão/Leao5_jpg.rf.e2fc9957c0d7cd5de1af9f4fac9766b6.jpg \n", " extracting: valid/Leão/Leao77_jpg.rf.dce9918c3a2e9241a5fb0862a1c610d2.jpg \n", " extracting: valid/Leão/Leao79_jpg.rf.06c92561631f5a53ea7bd3297c2fa400.jpg \n", " extracting: valid/Leão/Leao86_jpg.rf.7965afe2449e0ce4d121843a7a802e07.jpg \n", " extracting: valid/Leão/Leao90_jpg.rf.37aa35a851f7fc9c7199f4a8a3ab2c53.jpg \n", " extracting: valid/Leão/Leao97_jpg.rf.900241de203fbd4be420b9d193aae437.jpg \n", " extracting: valid/Leão/leao12333_jpg.rf.b82c0888ef2043ef74e7c886c8b52fa9.jpg \n", " extracting: valid/Leão/leao3341_jpeg.rf.47dc7bca3a71b598890be21894119719.jpg \n", "/content\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Gerando base e Reshape para treinamento" ], "metadata": { "id": "vWMIUNpqArLM" } }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "NV4PpnYBFoS9" }, "outputs": [], "source": [ "\n", "train_gen = ImageDataGenerator(rescale = 1./255,\n", " rotation_range = 7,\n", " horizontal_flip = True,\n", " shear_range = 0.2,\n", " height_shift_range = 0.07,\n", " zoom_range = 0.2)\n", "\n", "test_gen = ImageDataGenerator(rescale = 1./255)\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xXt7Dr1lFoS_", "outputId": "387b0911-96c1-4995-83e5-59366965fb17" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Found 208 images belonging to 3 classes.\n", "Found 33 images belonging to 3 classes.\n", "Found 59 images belonging to 3 classes.\n" ] } ], "source": [ "#gerando bases de treino e validação\n", "train_dir = train_gen.flow_from_directory('/content/images_dir/train/',\n", " target_size = (64,64),\n", " batch_size = 64,\n", " class_mode = 'categorical')\n", "\n", "test_dir = test_gen.flow_from_directory('/content/images_dir/test/',\n", " target_size = (64,64),\n", " batch_size = 64,\n", " class_mode = 'categorical')\n", "\n", "val_dir = test_gen.flow_from_directory('/content/images_dir/valid/',\n", " target_size = (64,64),\n", " batch_size = 64,\n", " class_mode = 'categorical')\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "SDDEcLdoFoTE", "outputId": "2e0df8ef-3715-44a2-8267-e91ef18df8b9" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n", "9412608/9406464 [==============================] - 0s 0us/step\n", "9420800/9406464 [==============================] - 0s 0us/step\n" ] } ], "source": [ "model_base = MobileNetV2(input_shape=(224,224,3), \n", " include_top=False,\n", " weights='imagenet')\n", "\n", "\n", "model_base.trainable = False\n", "model = Sequential([\n", " model_base,\n", " GlobalAveragePooling2D(),\n", " Dense(128, activation = 'relu'),\n", " Dropout(0.5),\n", " Dense(3, activation = 'softmax')\n", " ])\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "FtHmGCf_FoTF" }, "outputs": [], "source": [ "#Otimizador\n", "opt = Adam(learning_rate = 0.0001)\n", "model.compile(optimizer = opt, \n", " loss = 'binary_crossentropy',\n", " metrics=['accuracy'])" ] }, { "cell_type": "markdown", "source": [ "# Adicinoando pesos e viés através do WANDB" ], "metadata": { "id": "lmENLZhrB368" } }, { "cell_type": "code", "execution_count": 7, "metadata": { "id": "xF5_WFzAFoTW", "outputId": "9bb2426d-b3a2-429b-e7bd-63471e0ffefa", "colab": { "base_uri": "https://localhost:8080/", "height": 253 } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[K |████████████████████████████████| 1.8 MB 5.5 MB/s \n", "\u001b[K |████████████████████████████████| 157 kB 46.8 MB/s \n", "\u001b[K |████████████████████████████████| 181 kB 46.8 MB/s \n", "\u001b[K |████████████████████████████████| 63 kB 1.6 MB/s \n", "\u001b[K |████████████████████████████████| 157 kB 51.5 MB/s \n", "\u001b[K |████████████████████████████████| 157 kB 50.9 MB/s \n", "\u001b[K |████████████████████████████████| 157 kB 50.9 MB/s \n", "\u001b[K |████████████████████████████████| 157 kB 48.0 MB/s \n", "\u001b[K |████████████████████████████████| 156 kB 52.5 MB/s \n", "\u001b[?25h Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "ERROR:wandb.jupyter:Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " window._wandbApiKey = new Promise((resolve, reject) => {\n", " function loadScript(url) {\n", " return new Promise(function(resolve, reject) {\n", " let newScript = document.createElement(\"script\");\n", " newScript.onerror = reject;\n", " newScript.onload = resolve;\n", " document.body.appendChild(newScript);\n", " newScript.src = url;\n", " });\n", " }\n", " loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n", " const iframe = document.createElement('iframe')\n", " iframe.style.cssText = \"width:0;height:0;border:none\"\n", " document.body.appendChild(iframe)\n", " const handshake = new Postmate({\n", " container: iframe,\n", " url: 'https://wandb.ai/authorize'\n", " });\n", " const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n", " handshake.then(function(child) {\n", " child.on('authorize', data => {\n", " clearTimeout(timeout)\n", " resolve(data)\n", " });\n", " });\n", " })\n", " });\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n" ] } ], "source": [ "# Weights & Biases (optional)\n", "%pip install -q wandb\n", "import wandb\n", "\n", "\n", "\n", "import sys\n", "#aff5f62f1ae37d187c61e686708d8830fe9710a7\n", "def wandb_colab_login():\n", " \"\"\"Temporary hack to prevent colab from hanging\"\"\"\n", " sys.modules[\"google.colab2\"] = sys.modules[\"google.colab\"]\n", " del sys.modules[\"google.colab\"]\n", " wandb.login()\n", " sys.modules[\"google.colab\"] = sys.modules[\"google.colab2\"]\n", "wandb_colab_login()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nnfLpRCkFoTH", "outputId": "5d8edf34-76cc-4a44-bb3a-37cc711cb5c5" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/1000\n", "2/2 [==============================] - ETA: 0s - loss: 1.0496 - accuracy: 0.3750\n", "Epoch 1: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 9s 4s/step - loss: 1.0496 - accuracy: 0.3750 - val_loss: 0.8153 - val_accuracy: 0.4237\n", "Epoch 2/1000\n", "2/2 [==============================] - ETA: 0s - loss: 1.0002 - accuracy: 0.3281\n", "Epoch 2: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 4s 2s/step - loss: 1.0002 - accuracy: 0.3281 - val_loss: 0.7967 - val_accuracy: 0.4407\n", "Epoch 3/1000\n", "2/2 [==============================] - ETA: 0s - loss: 1.0473 - accuracy: 0.3594\n", "Epoch 3: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 3s 2s/step - loss: 1.0473 - accuracy: 0.3594 - val_loss: 0.7953 - val_accuracy: 0.4237\n", "Epoch 4/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.9252 - accuracy: 0.3250\n", "Epoch 4: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.9252 - accuracy: 0.3250 - val_loss: 0.8039 - val_accuracy: 0.3729\n", "Epoch 5/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.9771 - accuracy: 0.3000\n", "Epoch 5: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 781ms/step - loss: 0.9771 - accuracy: 0.3000 - val_loss: 0.8116 - val_accuracy: 0.3729\n", "Epoch 6/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.9402 - accuracy: 0.3125\n", "Epoch 6: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 789ms/step - loss: 0.9402 - accuracy: 0.3125 - val_loss: 0.8183 - val_accuracy: 0.3898\n", "Epoch 7/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.8416 - accuracy: 0.4750\n", "Epoch 7: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.8416 - accuracy: 0.4750 - val_loss: 0.8229 - val_accuracy: 0.3898\n", "Epoch 8/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.8543 - accuracy: 0.3516\n", "Epoch 8: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 913ms/step - loss: 0.8543 - accuracy: 0.3516 - val_loss: 0.8213 - val_accuracy: 0.4068\n", "Epoch 9/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.7657 - accuracy: 0.4844\n", "Epoch 9: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 908ms/step - loss: 0.7657 - accuracy: 0.4844 - val_loss: 0.8124 - val_accuracy: 0.4068\n", "Epoch 10/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.8208 - accuracy: 0.3125\n", "Epoch 10: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.8208 - accuracy: 0.3125 - val_loss: 0.8035 - val_accuracy: 0.4237\n", "Epoch 11/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.8510 - accuracy: 0.3875\n", "Epoch 11: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 789ms/step - loss: 0.8510 - accuracy: 0.3875 - val_loss: 0.7868 - val_accuracy: 0.4237\n", "Epoch 12/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.7841 - accuracy: 0.4609\n", "Epoch 12: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 896ms/step - loss: 0.7841 - accuracy: 0.4609 - val_loss: 0.7674 - val_accuracy: 0.4407\n", "Epoch 13/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.7320 - accuracy: 0.5125\n", "Epoch 13: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.7320 - accuracy: 0.5125 - val_loss: 0.7513 - val_accuracy: 0.4576\n", "Epoch 14/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.7788 - accuracy: 0.3828\n", "Epoch 14: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 908ms/step - loss: 0.7788 - accuracy: 0.3828 - val_loss: 0.7345 - val_accuracy: 0.4915\n", "Epoch 15/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.8054 - accuracy: 0.3250\n", "Epoch 15: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 803ms/step - loss: 0.8054 - accuracy: 0.3250 - val_loss: 0.7162 - val_accuracy: 0.4915\n", "Epoch 16/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.7073 - accuracy: 0.5125\n", "Epoch 16: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.7073 - accuracy: 0.5125 - val_loss: 0.6949 - val_accuracy: 0.5085\n", "Epoch 17/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.7984 - accuracy: 0.4250\n", "Epoch 17: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.7984 - accuracy: 0.4250 - val_loss: 0.6756 - val_accuracy: 0.5424\n", "Epoch 18/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.7332 - accuracy: 0.4750\n", "Epoch 18: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 777ms/step - loss: 0.7332 - accuracy: 0.4750 - val_loss: 0.6573 - val_accuracy: 0.5763\n", "Epoch 19/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.6789 - accuracy: 0.5000\n", "Epoch 19: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 928ms/step - loss: 0.6789 - accuracy: 0.5000 - val_loss: 0.6398 - val_accuracy: 0.5763\n", "Epoch 20/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.7541 - accuracy: 0.4844\n", "Epoch 20: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.7541 - accuracy: 0.4844 - val_loss: 0.6241 - val_accuracy: 0.5763\n", "Epoch 21/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.7528 - accuracy: 0.4688\n", "Epoch 21: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.7528 - accuracy: 0.4688 - val_loss: 0.6103 - val_accuracy: 0.5763\n", "Epoch 22/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.6765 - accuracy: 0.5000\n", "Epoch 22: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.6765 - accuracy: 0.5000 - val_loss: 0.5980 - val_accuracy: 0.5932\n", "Epoch 23/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.6817 - accuracy: 0.5625\n", "Epoch 23: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.6817 - accuracy: 0.5625 - val_loss: 0.5890 - val_accuracy: 0.6102\n", "Epoch 24/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.7056 - accuracy: 0.4125\n", "Epoch 24: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 785ms/step - loss: 0.7056 - accuracy: 0.4125 - val_loss: 0.5802 - val_accuracy: 0.6102\n", "Epoch 25/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.7238 - accuracy: 0.4453\n", "Epoch 25: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.7238 - accuracy: 0.4453 - val_loss: 0.5716 - val_accuracy: 0.6102\n", "Epoch 26/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.6118 - accuracy: 0.4875\n", "Epoch 26: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.6118 - accuracy: 0.4875 - val_loss: 0.5640 - val_accuracy: 0.6102\n", "Epoch 27/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.6136 - accuracy: 0.5250\n", "Epoch 27: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.6136 - accuracy: 0.5250 - val_loss: 0.5557 - val_accuracy: 0.6102\n", "Epoch 28/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.6424 - accuracy: 0.5156\n", "Epoch 28: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 925ms/step - loss: 0.6424 - accuracy: 0.5156 - val_loss: 0.5483 - val_accuracy: 0.6271\n", "Epoch 29/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.6367 - accuracy: 0.5703\n", "Epoch 29: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 925ms/step - loss: 0.6367 - accuracy: 0.5703 - val_loss: 0.5409 - val_accuracy: 0.6102\n", "Epoch 30/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5621 - accuracy: 0.6375\n", "Epoch 30: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.5621 - accuracy: 0.6375 - val_loss: 0.5350 - val_accuracy: 0.6102\n", "Epoch 31/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5903 - accuracy: 0.6625\n", "Epoch 31: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 773ms/step - loss: 0.5903 - accuracy: 0.6625 - val_loss: 0.5297 - val_accuracy: 0.6102\n", "Epoch 32/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5768 - accuracy: 0.5938\n", "Epoch 32: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.5768 - accuracy: 0.5938 - val_loss: 0.5246 - val_accuracy: 0.5932\n", "Epoch 33/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5517 - accuracy: 0.6625\n", "Epoch 33: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 771ms/step - loss: 0.5517 - accuracy: 0.6625 - val_loss: 0.5197 - val_accuracy: 0.6102\n", "Epoch 34/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5987 - accuracy: 0.5625\n", "Epoch 34: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.5987 - accuracy: 0.5625 - val_loss: 0.5156 - val_accuracy: 0.6271\n", "Epoch 35/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5768 - accuracy: 0.5859\n", "Epoch 35: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 866ms/step - loss: 0.5768 - accuracy: 0.5859 - val_loss: 0.5116 - val_accuracy: 0.6271\n", "Epoch 36/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5395 - accuracy: 0.7000\n", "Epoch 36: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.5395 - accuracy: 0.7000 - val_loss: 0.5072 - val_accuracy: 0.6271\n", "Epoch 37/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5549 - accuracy: 0.5625\n", "Epoch 37: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.5549 - accuracy: 0.5625 - val_loss: 0.5027 - val_accuracy: 0.6271\n", "Epoch 38/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5485 - accuracy: 0.5750\n", "Epoch 38: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 783ms/step - loss: 0.5485 - accuracy: 0.5750 - val_loss: 0.4985 - val_accuracy: 0.6271\n", "Epoch 39/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5600 - accuracy: 0.5875\n", "Epoch 39: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.5600 - accuracy: 0.5875 - val_loss: 0.4944 - val_accuracy: 0.6441\n", "Epoch 40/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5797 - accuracy: 0.6250\n", "Epoch 40: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 766ms/step - loss: 0.5797 - accuracy: 0.6250 - val_loss: 0.4913 - val_accuracy: 0.6441\n", "Epoch 41/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5891 - accuracy: 0.6125\n", "Epoch 41: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 850ms/step - loss: 0.5891 - accuracy: 0.6125 - val_loss: 0.4880 - val_accuracy: 0.6610\n", "Epoch 42/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5301 - accuracy: 0.6375\n", "Epoch 42: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 810ms/step - loss: 0.5301 - accuracy: 0.6375 - val_loss: 0.4847 - val_accuracy: 0.6610\n", "Epoch 43/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5775 - accuracy: 0.6328\n", "Epoch 43: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 942ms/step - loss: 0.5775 - accuracy: 0.6328 - val_loss: 0.4796 - val_accuracy: 0.6610\n", "Epoch 44/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4997 - accuracy: 0.6641\n", "Epoch 44: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.4997 - accuracy: 0.6641 - val_loss: 0.4753 - val_accuracy: 0.6610\n", "Epoch 45/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5236 - accuracy: 0.7109\n", "Epoch 45: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.5236 - accuracy: 0.7109 - val_loss: 0.4713 - val_accuracy: 0.6780\n", "Epoch 46/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5150 - accuracy: 0.6641\n", "Epoch 46: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.5150 - accuracy: 0.6641 - val_loss: 0.4674 - val_accuracy: 0.6780\n", "Epoch 47/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5213 - accuracy: 0.6625\n", "Epoch 47: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.5213 - accuracy: 0.6625 - val_loss: 0.4637 - val_accuracy: 0.6780\n", "Epoch 48/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5835 - accuracy: 0.6016\n", "Epoch 48: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 913ms/step - loss: 0.5835 - accuracy: 0.6016 - val_loss: 0.4594 - val_accuracy: 0.6780\n", "Epoch 49/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5356 - accuracy: 0.6641\n", "Epoch 49: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.5356 - accuracy: 0.6641 - val_loss: 0.4551 - val_accuracy: 0.6780\n", "Epoch 50/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5144 - accuracy: 0.6797\n", "Epoch 50: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.5144 - accuracy: 0.6797 - val_loss: 0.4520 - val_accuracy: 0.6949\n", "Epoch 51/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5832 - accuracy: 0.6875\n", "Epoch 51: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.5832 - accuracy: 0.6875 - val_loss: 0.4498 - val_accuracy: 0.6949\n", "Epoch 52/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5395 - accuracy: 0.6500\n", "Epoch 52: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 795ms/step - loss: 0.5395 - accuracy: 0.6500 - val_loss: 0.4471 - val_accuracy: 0.6949\n", "Epoch 53/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4901 - accuracy: 0.7188\n", "Epoch 53: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 995ms/step - loss: 0.4901 - accuracy: 0.7188 - val_loss: 0.4434 - val_accuracy: 0.6949\n", "Epoch 54/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4348 - accuracy: 0.7250\n", "Epoch 54: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 796ms/step - loss: 0.4348 - accuracy: 0.7250 - val_loss: 0.4400 - val_accuracy: 0.6949\n", "Epoch 55/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5062 - accuracy: 0.6641\n", "Epoch 55: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.5062 - accuracy: 0.6641 - val_loss: 0.4370 - val_accuracy: 0.7119\n", "Epoch 56/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5069 - accuracy: 0.5875\n", "Epoch 56: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.5069 - accuracy: 0.5875 - val_loss: 0.4306 - val_accuracy: 0.7119\n", "Epoch 57/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4512 - accuracy: 0.7125\n", "Epoch 57: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.4512 - accuracy: 0.7125 - val_loss: 0.4254 - val_accuracy: 0.7119\n", "Epoch 58/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5265 - accuracy: 0.6625\n", "Epoch 58: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.5265 - accuracy: 0.6625 - val_loss: 0.4208 - val_accuracy: 0.7119\n", "Epoch 59/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4557 - accuracy: 0.7375\n", "Epoch 59: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 792ms/step - loss: 0.4557 - accuracy: 0.7375 - val_loss: 0.4171 - val_accuracy: 0.7119\n", "Epoch 60/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5258 - accuracy: 0.6125\n", "Epoch 60: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 793ms/step - loss: 0.5258 - accuracy: 0.6125 - val_loss: 0.4139 - val_accuracy: 0.7119\n", "Epoch 61/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4988 - accuracy: 0.6641\n", "Epoch 61: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.4988 - accuracy: 0.6641 - val_loss: 0.4117 - val_accuracy: 0.7119\n", "Epoch 62/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5074 - accuracy: 0.6625\n", "Epoch 62: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.5074 - accuracy: 0.6625 - val_loss: 0.4109 - val_accuracy: 0.7119\n", "Epoch 63/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.5155 - accuracy: 0.6797\n", "Epoch 63: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.5155 - accuracy: 0.6797 - val_loss: 0.4105 - val_accuracy: 0.7119\n", "Epoch 64/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4738 - accuracy: 0.7031\n", "Epoch 64: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.4738 - accuracy: 0.7031 - val_loss: 0.4101 - val_accuracy: 0.7119\n", "Epoch 65/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4526 - accuracy: 0.7266\n", "Epoch 65: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.4526 - accuracy: 0.7266 - val_loss: 0.4099 - val_accuracy: 0.7288\n", "Epoch 66/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4432 - accuracy: 0.6875\n", "Epoch 66: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 917ms/step - loss: 0.4432 - accuracy: 0.6875 - val_loss: 0.4096 - val_accuracy: 0.7288\n", "Epoch 67/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4556 - accuracy: 0.7031\n", "Epoch 67: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 891ms/step - loss: 0.4556 - accuracy: 0.7031 - val_loss: 0.4089 - val_accuracy: 0.7288\n", "Epoch 68/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4906 - accuracy: 0.7000\n", "Epoch 68: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.4906 - accuracy: 0.7000 - val_loss: 0.4077 - val_accuracy: 0.7288\n", "Epoch 69/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4392 - accuracy: 0.6953\n", "Epoch 69: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 933ms/step - loss: 0.4392 - accuracy: 0.6953 - val_loss: 0.4067 - val_accuracy: 0.7288\n", "Epoch 70/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4505 - accuracy: 0.7188\n", "Epoch 70: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 911ms/step - loss: 0.4505 - accuracy: 0.7188 - val_loss: 0.4056 - val_accuracy: 0.7288\n", "Epoch 71/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4227 - accuracy: 0.8250\n", "Epoch 71: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.4227 - accuracy: 0.8250 - val_loss: 0.4038 - val_accuracy: 0.7288\n", "Epoch 72/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4216 - accuracy: 0.7188\n", "Epoch 72: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 942ms/step - loss: 0.4216 - accuracy: 0.7188 - val_loss: 0.4028 - val_accuracy: 0.7288\n", "Epoch 73/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4563 - accuracy: 0.7031\n", "Epoch 73: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.4563 - accuracy: 0.7031 - val_loss: 0.4029 - val_accuracy: 0.7288\n", "Epoch 74/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4717 - accuracy: 0.6719\n", "Epoch 74: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.4717 - accuracy: 0.6719 - val_loss: 0.4026 - val_accuracy: 0.7288\n", "Epoch 75/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3515 - accuracy: 0.8250\n", "Epoch 75: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.3515 - accuracy: 0.8250 - val_loss: 0.4009 - val_accuracy: 0.7119\n", "Epoch 76/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4396 - accuracy: 0.7125\n", "Epoch 76: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 795ms/step - loss: 0.4396 - accuracy: 0.7125 - val_loss: 0.4004 - val_accuracy: 0.7288\n", "Epoch 77/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4737 - accuracy: 0.6250\n", "Epoch 77: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.4737 - accuracy: 0.6250 - val_loss: 0.4002 - val_accuracy: 0.7458\n", "Epoch 78/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3818 - accuracy: 0.8125\n", "Epoch 78: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3818 - accuracy: 0.8125 - val_loss: 0.3997 - val_accuracy: 0.7458\n", "Epoch 79/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3942 - accuracy: 0.7812\n", "Epoch 79: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3942 - accuracy: 0.7812 - val_loss: 0.3999 - val_accuracy: 0.7458\n", "Epoch 80/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4376 - accuracy: 0.7625\n", "Epoch 80: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.4376 - accuracy: 0.7625 - val_loss: 0.3999 - val_accuracy: 0.7288\n", "Epoch 81/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4146 - accuracy: 0.7875\n", "Epoch 81: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.4146 - accuracy: 0.7875 - val_loss: 0.3985 - val_accuracy: 0.7458\n", "Epoch 82/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4513 - accuracy: 0.7109\n", "Epoch 82: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 952ms/step - loss: 0.4513 - accuracy: 0.7109 - val_loss: 0.3975 - val_accuracy: 0.7458\n", "Epoch 83/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4000 - accuracy: 0.7875\n", "Epoch 83: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.4000 - accuracy: 0.7875 - val_loss: 0.3966 - val_accuracy: 0.7458\n", "Epoch 84/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3920 - accuracy: 0.7812\n", "Epoch 84: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3920 - accuracy: 0.7812 - val_loss: 0.3957 - val_accuracy: 0.7458\n", "Epoch 85/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4480 - accuracy: 0.6750\n", "Epoch 85: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.4480 - accuracy: 0.6750 - val_loss: 0.3950 - val_accuracy: 0.7458\n", "Epoch 86/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4010 - accuracy: 0.7656\n", "Epoch 86: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 881ms/step - loss: 0.4010 - accuracy: 0.7656 - val_loss: 0.3956 - val_accuracy: 0.7288\n", "Epoch 87/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4635 - accuracy: 0.7125\n", "Epoch 87: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.4635 - accuracy: 0.7125 - val_loss: 0.3978 - val_accuracy: 0.7288\n", "Epoch 88/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4501 - accuracy: 0.7188\n", "Epoch 88: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 915ms/step - loss: 0.4501 - accuracy: 0.7188 - val_loss: 0.4002 - val_accuracy: 0.7627\n", "Epoch 89/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3909 - accuracy: 0.7875\n", "Epoch 89: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.3909 - accuracy: 0.7875 - val_loss: 0.4037 - val_accuracy: 0.7627\n", "Epoch 90/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3992 - accuracy: 0.7250\n", "Epoch 90: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.3992 - accuracy: 0.7250 - val_loss: 0.4045 - val_accuracy: 0.7627\n", "Epoch 91/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4022 - accuracy: 0.8203\n", "Epoch 91: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.4022 - accuracy: 0.8203 - val_loss: 0.4050 - val_accuracy: 0.7458\n", "Epoch 92/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4112 - accuracy: 0.7031\n", "Epoch 92: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 972ms/step - loss: 0.4112 - accuracy: 0.7031 - val_loss: 0.4050 - val_accuracy: 0.7458\n", "Epoch 93/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3795 - accuracy: 0.7500\n", "Epoch 93: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3795 - accuracy: 0.7500 - val_loss: 0.4046 - val_accuracy: 0.7458\n", "Epoch 94/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4178 - accuracy: 0.7250\n", "Epoch 94: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 786ms/step - loss: 0.4178 - accuracy: 0.7250 - val_loss: 0.4047 - val_accuracy: 0.7458\n", "Epoch 95/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3446 - accuracy: 0.8281\n", "Epoch 95: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3446 - accuracy: 0.8281 - val_loss: 0.4047 - val_accuracy: 0.7458\n", "Epoch 96/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4607 - accuracy: 0.7250\n", "Epoch 96: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.4607 - accuracy: 0.7250 - val_loss: 0.4035 - val_accuracy: 0.7458\n", "Epoch 97/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3616 - accuracy: 0.7875\n", "Epoch 97: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 809ms/step - loss: 0.3616 - accuracy: 0.7875 - val_loss: 0.4021 - val_accuracy: 0.7458\n", "Epoch 98/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3380 - accuracy: 0.7375\n", "Epoch 98: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 795ms/step - loss: 0.3380 - accuracy: 0.7375 - val_loss: 0.4014 - val_accuracy: 0.7458\n", "Epoch 99/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3621 - accuracy: 0.8047\n", "Epoch 99: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 925ms/step - loss: 0.3621 - accuracy: 0.8047 - val_loss: 0.3993 - val_accuracy: 0.7288\n", "Epoch 100/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3969 - accuracy: 0.7578\n", "Epoch 100: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 922ms/step - loss: 0.3969 - accuracy: 0.7578 - val_loss: 0.3952 - val_accuracy: 0.7288\n", "Epoch 101/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3638 - accuracy: 0.7500\n", "Epoch 101: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 807ms/step - loss: 0.3638 - accuracy: 0.7500 - val_loss: 0.3910 - val_accuracy: 0.7288\n", "Epoch 102/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3590 - accuracy: 0.7891\n", "Epoch 102: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 912ms/step - loss: 0.3590 - accuracy: 0.7891 - val_loss: 0.3877 - val_accuracy: 0.7288\n", "Epoch 103/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3947 - accuracy: 0.7656\n", "Epoch 103: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 959ms/step - loss: 0.3947 - accuracy: 0.7656 - val_loss: 0.3841 - val_accuracy: 0.7288\n", "Epoch 104/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4289 - accuracy: 0.7250\n", "Epoch 104: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 805ms/step - loss: 0.4289 - accuracy: 0.7250 - val_loss: 0.3815 - val_accuracy: 0.7288\n", "Epoch 105/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3684 - accuracy: 0.8359\n", "Epoch 105: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3684 - accuracy: 0.8359 - val_loss: 0.3784 - val_accuracy: 0.7288\n", "Epoch 106/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3745 - accuracy: 0.8000\n", "Epoch 106: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 866ms/step - loss: 0.3745 - accuracy: 0.8000 - val_loss: 0.3758 - val_accuracy: 0.7288\n", "Epoch 107/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3485 - accuracy: 0.8125\n", "Epoch 107: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 917ms/step - loss: 0.3485 - accuracy: 0.8125 - val_loss: 0.3743 - val_accuracy: 0.7458\n", "Epoch 108/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3889 - accuracy: 0.8000\n", "Epoch 108: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 997ms/step - loss: 0.3889 - accuracy: 0.8000 - val_loss: 0.3726 - val_accuracy: 0.7458\n", "Epoch 109/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3484 - accuracy: 0.8672\n", "Epoch 109: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 937ms/step - loss: 0.3484 - accuracy: 0.8672 - val_loss: 0.3712 - val_accuracy: 0.7458\n", "Epoch 110/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3734 - accuracy: 0.8047\n", "Epoch 110: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3734 - accuracy: 0.8047 - val_loss: 0.3696 - val_accuracy: 0.7458\n", "Epoch 111/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4089 - accuracy: 0.7875\n", "Epoch 111: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 789ms/step - loss: 0.4089 - accuracy: 0.7875 - val_loss: 0.3676 - val_accuracy: 0.7458\n", "Epoch 112/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3788 - accuracy: 0.7750\n", "Epoch 112: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 783ms/step - loss: 0.3788 - accuracy: 0.7750 - val_loss: 0.3646 - val_accuracy: 0.7288\n", "Epoch 113/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3728 - accuracy: 0.7812\n", "Epoch 113: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3728 - accuracy: 0.7812 - val_loss: 0.3621 - val_accuracy: 0.7288\n", "Epoch 114/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3751 - accuracy: 0.8000\n", "Epoch 114: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.3751 - accuracy: 0.8000 - val_loss: 0.3599 - val_accuracy: 0.7288\n", "Epoch 115/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3739 - accuracy: 0.7734\n", "Epoch 115: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 946ms/step - loss: 0.3739 - accuracy: 0.7734 - val_loss: 0.3578 - val_accuracy: 0.7288\n", "Epoch 116/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3883 - accuracy: 0.8000\n", "Epoch 116: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.3883 - accuracy: 0.8000 - val_loss: 0.3563 - val_accuracy: 0.7288\n", "Epoch 117/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3443 - accuracy: 0.8203\n", "Epoch 117: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3443 - accuracy: 0.8203 - val_loss: 0.3552 - val_accuracy: 0.7458\n", "Epoch 118/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3449 - accuracy: 0.8375\n", "Epoch 118: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.3449 - accuracy: 0.8375 - val_loss: 0.3555 - val_accuracy: 0.7458\n", "Epoch 119/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3562 - accuracy: 0.8000\n", "Epoch 119: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.3562 - accuracy: 0.8000 - val_loss: 0.3556 - val_accuracy: 0.7458\n", "Epoch 120/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2561 - accuracy: 0.8828\n", "Epoch 120: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 914ms/step - loss: 0.2561 - accuracy: 0.8828 - val_loss: 0.3562 - val_accuracy: 0.7458\n", "Epoch 121/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3495 - accuracy: 0.8125\n", "Epoch 121: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 916ms/step - loss: 0.3495 - accuracy: 0.8125 - val_loss: 0.3566 - val_accuracy: 0.7627\n", "Epoch 122/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3165 - accuracy: 0.8672\n", "Epoch 122: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3165 - accuracy: 0.8672 - val_loss: 0.3566 - val_accuracy: 0.7627\n", "Epoch 123/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3741 - accuracy: 0.7734\n", "Epoch 123: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3741 - accuracy: 0.7734 - val_loss: 0.3571 - val_accuracy: 0.7627\n", "Epoch 124/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3923 - accuracy: 0.7500\n", "Epoch 124: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 955ms/step - loss: 0.3923 - accuracy: 0.7500 - val_loss: 0.3574 - val_accuracy: 0.7627\n", "Epoch 125/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3380 - accuracy: 0.7812\n", "Epoch 125: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 912ms/step - loss: 0.3380 - accuracy: 0.7812 - val_loss: 0.3575 - val_accuracy: 0.7627\n", "Epoch 126/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3617 - accuracy: 0.7875\n", "Epoch 126: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.3617 - accuracy: 0.7875 - val_loss: 0.3581 - val_accuracy: 0.7627\n", "Epoch 127/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4007 - accuracy: 0.7000\n", "Epoch 127: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.4007 - accuracy: 0.7000 - val_loss: 0.3577 - val_accuracy: 0.7627\n", "Epoch 128/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3632 - accuracy: 0.8000\n", "Epoch 128: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.3632 - accuracy: 0.8000 - val_loss: 0.3570 - val_accuracy: 0.7627\n", "Epoch 129/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3418 - accuracy: 0.8359\n", "Epoch 129: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3418 - accuracy: 0.8359 - val_loss: 0.3558 - val_accuracy: 0.7627\n", "Epoch 130/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3338 - accuracy: 0.8250\n", "Epoch 130: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 815ms/step - loss: 0.3338 - accuracy: 0.8250 - val_loss: 0.3545 - val_accuracy: 0.7627\n", "Epoch 131/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3705 - accuracy: 0.7750\n", "Epoch 131: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.3705 - accuracy: 0.7750 - val_loss: 0.3534 - val_accuracy: 0.7627\n", "Epoch 132/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2992 - accuracy: 0.8625\n", "Epoch 132: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2992 - accuracy: 0.8625 - val_loss: 0.3531 - val_accuracy: 0.7627\n", "Epoch 133/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3112 - accuracy: 0.8438\n", "Epoch 133: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 940ms/step - loss: 0.3112 - accuracy: 0.8438 - val_loss: 0.3533 - val_accuracy: 0.7627\n", "Epoch 134/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3687 - accuracy: 0.8203\n", "Epoch 134: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 926ms/step - loss: 0.3687 - accuracy: 0.8203 - val_loss: 0.3521 - val_accuracy: 0.7627\n", "Epoch 135/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.4165 - accuracy: 0.7250\n", "Epoch 135: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.4165 - accuracy: 0.7250 - val_loss: 0.3497 - val_accuracy: 0.7627\n", "Epoch 136/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2755 - accuracy: 0.8750\n", "Epoch 136: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 801ms/step - loss: 0.2755 - accuracy: 0.8750 - val_loss: 0.3483 - val_accuracy: 0.7627\n", "Epoch 137/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3457 - accuracy: 0.8000\n", "Epoch 137: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 783ms/step - loss: 0.3457 - accuracy: 0.8000 - val_loss: 0.3478 - val_accuracy: 0.7627\n", "Epoch 138/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3676 - accuracy: 0.7812\n", "Epoch 138: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3676 - accuracy: 0.7812 - val_loss: 0.3470 - val_accuracy: 0.7627\n", "Epoch 139/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3189 - accuracy: 0.7875\n", "Epoch 139: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 781ms/step - loss: 0.3189 - accuracy: 0.7875 - val_loss: 0.3467 - val_accuracy: 0.7627\n", "Epoch 140/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3633 - accuracy: 0.7875\n", "Epoch 140: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.3633 - accuracy: 0.7875 - val_loss: 0.3483 - val_accuracy: 0.7627\n", "Epoch 141/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3355 - accuracy: 0.7875\n", "Epoch 141: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 852ms/step - loss: 0.3355 - accuracy: 0.7875 - val_loss: 0.3495 - val_accuracy: 0.7627\n", "Epoch 142/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3416 - accuracy: 0.8250\n", "Epoch 142: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 796ms/step - loss: 0.3416 - accuracy: 0.8250 - val_loss: 0.3497 - val_accuracy: 0.7627\n", "Epoch 143/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3214 - accuracy: 0.8438\n", "Epoch 143: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3214 - accuracy: 0.8438 - val_loss: 0.3494 - val_accuracy: 0.7627\n", "Epoch 144/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3541 - accuracy: 0.7875\n", "Epoch 144: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3541 - accuracy: 0.7875 - val_loss: 0.3490 - val_accuracy: 0.7627\n", "Epoch 145/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3347 - accuracy: 0.8500\n", "Epoch 145: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 806ms/step - loss: 0.3347 - accuracy: 0.8500 - val_loss: 0.3488 - val_accuracy: 0.7627\n", "Epoch 146/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3238 - accuracy: 0.8594\n", "Epoch 146: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 969ms/step - loss: 0.3238 - accuracy: 0.8594 - val_loss: 0.3493 - val_accuracy: 0.7627\n", "Epoch 147/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3252 - accuracy: 0.8250\n", "Epoch 147: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 799ms/step - loss: 0.3252 - accuracy: 0.8250 - val_loss: 0.3499 - val_accuracy: 0.7627\n", "Epoch 148/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3136 - accuracy: 0.8250\n", "Epoch 148: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 766ms/step - loss: 0.3136 - accuracy: 0.8250 - val_loss: 0.3515 - val_accuracy: 0.7627\n", "Epoch 149/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3215 - accuracy: 0.8250\n", "Epoch 149: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.3215 - accuracy: 0.8250 - val_loss: 0.3529 - val_accuracy: 0.7627\n", "Epoch 150/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3838 - accuracy: 0.7625\n", "Epoch 150: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.3838 - accuracy: 0.7625 - val_loss: 0.3546 - val_accuracy: 0.7627\n", "Epoch 151/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3322 - accuracy: 0.8125\n", "Epoch 151: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 809ms/step - loss: 0.3322 - accuracy: 0.8125 - val_loss: 0.3537 - val_accuracy: 0.7627\n", "Epoch 152/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3422 - accuracy: 0.8281\n", "Epoch 152: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 913ms/step - loss: 0.3422 - accuracy: 0.8281 - val_loss: 0.3523 - val_accuracy: 0.7627\n", "Epoch 153/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3141 - accuracy: 0.8500\n", "Epoch 153: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 876ms/step - loss: 0.3141 - accuracy: 0.8500 - val_loss: 0.3495 - val_accuracy: 0.7627\n", "Epoch 154/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3786 - accuracy: 0.7625\n", "Epoch 154: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.3786 - accuracy: 0.7625 - val_loss: 0.3458 - val_accuracy: 0.7627\n", "Epoch 155/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3309 - accuracy: 0.8125\n", "Epoch 155: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3309 - accuracy: 0.8125 - val_loss: 0.3425 - val_accuracy: 0.7627\n", "Epoch 156/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3570 - accuracy: 0.7969\n", "Epoch 156: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 928ms/step - loss: 0.3570 - accuracy: 0.7969 - val_loss: 0.3386 - val_accuracy: 0.7797\n", "Epoch 157/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3137 - accuracy: 0.8250\n", "Epoch 157: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 779ms/step - loss: 0.3137 - accuracy: 0.8250 - val_loss: 0.3349 - val_accuracy: 0.7797\n", "Epoch 158/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3485 - accuracy: 0.8281\n", "Epoch 158: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3485 - accuracy: 0.8281 - val_loss: 0.3321 - val_accuracy: 0.7797\n", "Epoch 159/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3114 - accuracy: 0.8594\n", "Epoch 159: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 997ms/step - loss: 0.3114 - accuracy: 0.8594 - val_loss: 0.3295 - val_accuracy: 0.7797\n", "Epoch 160/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3695 - accuracy: 0.7750\n", "Epoch 160: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.3695 - accuracy: 0.7750 - val_loss: 0.3255 - val_accuracy: 0.7797\n", "Epoch 161/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3590 - accuracy: 0.8125\n", "Epoch 161: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 794ms/step - loss: 0.3590 - accuracy: 0.8125 - val_loss: 0.3215 - val_accuracy: 0.7797\n", "Epoch 162/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3375 - accuracy: 0.8250\n", "Epoch 162: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.3375 - accuracy: 0.8250 - val_loss: 0.3184 - val_accuracy: 0.7797\n", "Epoch 163/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2919 - accuracy: 0.8672\n", "Epoch 163: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2919 - accuracy: 0.8672 - val_loss: 0.3172 - val_accuracy: 0.7797\n", "Epoch 164/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2972 - accuracy: 0.8594\n", "Epoch 164: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 937ms/step - loss: 0.2972 - accuracy: 0.8594 - val_loss: 0.3171 - val_accuracy: 0.7797\n", "Epoch 165/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3267 - accuracy: 0.8359\n", "Epoch 165: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3267 - accuracy: 0.8359 - val_loss: 0.3175 - val_accuracy: 0.7797\n", "Epoch 166/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2999 - accuracy: 0.8438\n", "Epoch 166: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2999 - accuracy: 0.8438 - val_loss: 0.3182 - val_accuracy: 0.7797\n", "Epoch 167/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3014 - accuracy: 0.8750\n", "Epoch 167: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 787ms/step - loss: 0.3014 - accuracy: 0.8750 - val_loss: 0.3198 - val_accuracy: 0.7797\n", "Epoch 168/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2670 - accuracy: 0.8250\n", "Epoch 168: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 810ms/step - loss: 0.2670 - accuracy: 0.8250 - val_loss: 0.3217 - val_accuracy: 0.7797\n", "Epoch 169/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3162 - accuracy: 0.8750\n", "Epoch 169: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 793ms/step - loss: 0.3162 - accuracy: 0.8750 - val_loss: 0.3219 - val_accuracy: 0.7797\n", "Epoch 170/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3178 - accuracy: 0.8047\n", "Epoch 170: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 943ms/step - loss: 0.3178 - accuracy: 0.8047 - val_loss: 0.3221 - val_accuracy: 0.7797\n", "Epoch 171/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2931 - accuracy: 0.8672\n", "Epoch 171: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 923ms/step - loss: 0.2931 - accuracy: 0.8672 - val_loss: 0.3225 - val_accuracy: 0.7797\n", "Epoch 172/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3197 - accuracy: 0.8047\n", "Epoch 172: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3197 - accuracy: 0.8047 - val_loss: 0.3238 - val_accuracy: 0.7797\n", "Epoch 173/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2872 - accuracy: 0.8281\n", "Epoch 173: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2872 - accuracy: 0.8281 - val_loss: 0.3255 - val_accuracy: 0.7797\n", "Epoch 174/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3595 - accuracy: 0.7734\n", "Epoch 174: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3595 - accuracy: 0.7734 - val_loss: 0.3273 - val_accuracy: 0.7797\n", "Epoch 175/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3140 - accuracy: 0.8375\n", "Epoch 175: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 811ms/step - loss: 0.3140 - accuracy: 0.8375 - val_loss: 0.3280 - val_accuracy: 0.7797\n", "Epoch 176/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3210 - accuracy: 0.8125\n", "Epoch 176: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3210 - accuracy: 0.8125 - val_loss: 0.3281 - val_accuracy: 0.7797\n", "Epoch 177/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2593 - accuracy: 0.8125\n", "Epoch 177: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2593 - accuracy: 0.8125 - val_loss: 0.3297 - val_accuracy: 0.7797\n", "Epoch 178/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3493 - accuracy: 0.7891\n", "Epoch 178: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3493 - accuracy: 0.7891 - val_loss: 0.3316 - val_accuracy: 0.7797\n", "Epoch 179/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3391 - accuracy: 0.8375\n", "Epoch 179: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.3391 - accuracy: 0.8375 - val_loss: 0.3345 - val_accuracy: 0.7797\n", "Epoch 180/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2908 - accuracy: 0.8438\n", "Epoch 180: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2908 - accuracy: 0.8438 - val_loss: 0.3373 - val_accuracy: 0.7797\n", "Epoch 181/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2884 - accuracy: 0.8438\n", "Epoch 181: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 912ms/step - loss: 0.2884 - accuracy: 0.8438 - val_loss: 0.3386 - val_accuracy: 0.7797\n", "Epoch 182/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2741 - accuracy: 0.8750\n", "Epoch 182: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2741 - accuracy: 0.8750 - val_loss: 0.3397 - val_accuracy: 0.7966\n", "Epoch 183/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3079 - accuracy: 0.8375\n", "Epoch 183: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.3079 - accuracy: 0.8375 - val_loss: 0.3402 - val_accuracy: 0.7966\n", "Epoch 184/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2915 - accuracy: 0.8500\n", "Epoch 184: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 821ms/step - loss: 0.2915 - accuracy: 0.8500 - val_loss: 0.3408 - val_accuracy: 0.8136\n", "Epoch 185/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2488 - accuracy: 0.9062\n", "Epoch 185: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2488 - accuracy: 0.9062 - val_loss: 0.3411 - val_accuracy: 0.8136\n", "Epoch 186/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2850 - accuracy: 0.8281\n", "Epoch 186: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2850 - accuracy: 0.8281 - val_loss: 0.3412 - val_accuracy: 0.8136\n", "Epoch 187/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3010 - accuracy: 0.8375\n", "Epoch 187: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 816ms/step - loss: 0.3010 - accuracy: 0.8375 - val_loss: 0.3412 - val_accuracy: 0.7966\n", "Epoch 188/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2825 - accuracy: 0.8594\n", "Epoch 188: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 979ms/step - loss: 0.2825 - accuracy: 0.8594 - val_loss: 0.3410 - val_accuracy: 0.7966\n", "Epoch 189/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3138 - accuracy: 0.8125\n", "Epoch 189: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 956ms/step - loss: 0.3138 - accuracy: 0.8125 - val_loss: 0.3392 - val_accuracy: 0.7966\n", "Epoch 190/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3285 - accuracy: 0.8000\n", "Epoch 190: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 793ms/step - loss: 0.3285 - accuracy: 0.8000 - val_loss: 0.3374 - val_accuracy: 0.8136\n", "Epoch 191/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3562 - accuracy: 0.7375\n", "Epoch 191: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 794ms/step - loss: 0.3562 - accuracy: 0.7375 - val_loss: 0.3362 - val_accuracy: 0.8305\n", "Epoch 192/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2750 - accuracy: 0.8625\n", "Epoch 192: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 805ms/step - loss: 0.2750 - accuracy: 0.8625 - val_loss: 0.3371 - val_accuracy: 0.8305\n", "Epoch 193/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2853 - accuracy: 0.8750\n", "Epoch 193: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 778ms/step - loss: 0.2853 - accuracy: 0.8750 - val_loss: 0.3378 - val_accuracy: 0.8305\n", "Epoch 194/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2862 - accuracy: 0.8625\n", "Epoch 194: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2862 - accuracy: 0.8625 - val_loss: 0.3387 - val_accuracy: 0.8136\n", "Epoch 195/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3483 - accuracy: 0.7625\n", "Epoch 195: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.3483 - accuracy: 0.7625 - val_loss: 0.3393 - val_accuracy: 0.8136\n", "Epoch 196/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2863 - accuracy: 0.8594\n", "Epoch 196: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2863 - accuracy: 0.8594 - val_loss: 0.3378 - val_accuracy: 0.8136\n", "Epoch 197/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2744 - accuracy: 0.8500\n", "Epoch 197: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 824ms/step - loss: 0.2744 - accuracy: 0.8500 - val_loss: 0.3355 - val_accuracy: 0.8136\n", "Epoch 198/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2827 - accuracy: 0.8438\n", "Epoch 198: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 952ms/step - loss: 0.2827 - accuracy: 0.8438 - val_loss: 0.3326 - val_accuracy: 0.8136\n", "Epoch 199/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2542 - accuracy: 0.8875\n", "Epoch 199: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 815ms/step - loss: 0.2542 - accuracy: 0.8875 - val_loss: 0.3295 - val_accuracy: 0.8136\n", "Epoch 200/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2779 - accuracy: 0.8672\n", "Epoch 200: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2779 - accuracy: 0.8672 - val_loss: 0.3259 - val_accuracy: 0.8305\n", "Epoch 201/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3151 - accuracy: 0.8516\n", "Epoch 201: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.3151 - accuracy: 0.8516 - val_loss: 0.3212 - val_accuracy: 0.8305\n", "Epoch 202/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2635 - accuracy: 0.8438\n", "Epoch 202: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2635 - accuracy: 0.8438 - val_loss: 0.3172 - val_accuracy: 0.8305\n", "Epoch 203/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2691 - accuracy: 0.8906\n", "Epoch 203: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2691 - accuracy: 0.8906 - val_loss: 0.3138 - val_accuracy: 0.8305\n", "Epoch 204/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2818 - accuracy: 0.8500\n", "Epoch 204: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2818 - accuracy: 0.8500 - val_loss: 0.3109 - val_accuracy: 0.8305\n", "Epoch 205/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2874 - accuracy: 0.8125\n", "Epoch 205: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2874 - accuracy: 0.8125 - val_loss: 0.3089 - val_accuracy: 0.8136\n", "Epoch 206/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2961 - accuracy: 0.8500\n", "Epoch 206: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 821ms/step - loss: 0.2961 - accuracy: 0.8500 - val_loss: 0.3080 - val_accuracy: 0.8136\n", "Epoch 207/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2628 - accuracy: 0.8516\n", "Epoch 207: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2628 - accuracy: 0.8516 - val_loss: 0.3077 - val_accuracy: 0.8136\n", "Epoch 208/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2807 - accuracy: 0.8750\n", "Epoch 208: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 792ms/step - loss: 0.2807 - accuracy: 0.8750 - val_loss: 0.3076 - val_accuracy: 0.8136\n", "Epoch 209/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2190 - accuracy: 0.8828\n", "Epoch 209: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 902ms/step - loss: 0.2190 - accuracy: 0.8828 - val_loss: 0.3073 - val_accuracy: 0.8136\n", "Epoch 210/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2307 - accuracy: 0.8875\n", "Epoch 210: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2307 - accuracy: 0.8875 - val_loss: 0.3073 - val_accuracy: 0.8136\n", "Epoch 211/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2403 - accuracy: 0.8672\n", "Epoch 211: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2403 - accuracy: 0.8672 - val_loss: 0.3079 - val_accuracy: 0.8136\n", "Epoch 212/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2151 - accuracy: 0.9375\n", "Epoch 212: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2151 - accuracy: 0.9375 - val_loss: 0.3075 - val_accuracy: 0.8136\n", "Epoch 213/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2767 - accuracy: 0.8875\n", "Epoch 213: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 795ms/step - loss: 0.2767 - accuracy: 0.8875 - val_loss: 0.3060 - val_accuracy: 0.8136\n", "Epoch 214/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2731 - accuracy: 0.8672\n", "Epoch 214: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2731 - accuracy: 0.8672 - val_loss: 0.3040 - val_accuracy: 0.8136\n", "Epoch 215/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2449 - accuracy: 0.8828\n", "Epoch 215: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2449 - accuracy: 0.8828 - val_loss: 0.3022 - val_accuracy: 0.8136\n", "Epoch 216/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2654 - accuracy: 0.8203\n", "Epoch 216: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2654 - accuracy: 0.8203 - val_loss: 0.2999 - val_accuracy: 0.8136\n", "Epoch 217/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2781 - accuracy: 0.8672\n", "Epoch 217: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2781 - accuracy: 0.8672 - val_loss: 0.2985 - val_accuracy: 0.8136\n", "Epoch 218/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.3467 - accuracy: 0.7875\n", "Epoch 218: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 808ms/step - loss: 0.3467 - accuracy: 0.7875 - val_loss: 0.2967 - val_accuracy: 0.8136\n", "Epoch 219/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2858 - accuracy: 0.8750\n", "Epoch 219: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2858 - accuracy: 0.8750 - val_loss: 0.2970 - val_accuracy: 0.8136\n", "Epoch 220/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2070 - accuracy: 0.9125\n", "Epoch 220: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2070 - accuracy: 0.9125 - val_loss: 0.2983 - val_accuracy: 0.8136\n", "Epoch 221/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2974 - accuracy: 0.8359\n", "Epoch 221: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2974 - accuracy: 0.8359 - val_loss: 0.2998 - val_accuracy: 0.8136\n", "Epoch 222/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2884 - accuracy: 0.8625\n", "Epoch 222: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 806ms/step - loss: 0.2884 - accuracy: 0.8625 - val_loss: 0.3019 - val_accuracy: 0.8136\n", "Epoch 223/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2783 - accuracy: 0.8438\n", "Epoch 223: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2783 - accuracy: 0.8438 - val_loss: 0.3043 - val_accuracy: 0.8136\n", "Epoch 224/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2062 - accuracy: 0.8875\n", "Epoch 224: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2062 - accuracy: 0.8875 - val_loss: 0.3075 - val_accuracy: 0.8136\n", "Epoch 225/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2499 - accuracy: 0.8500\n", "Epoch 225: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2499 - accuracy: 0.8500 - val_loss: 0.3094 - val_accuracy: 0.8136\n", "Epoch 226/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2541 - accuracy: 0.8672\n", "Epoch 226: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 957ms/step - loss: 0.2541 - accuracy: 0.8672 - val_loss: 0.3105 - val_accuracy: 0.8136\n", "Epoch 227/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2353 - accuracy: 0.8672\n", "Epoch 227: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 903ms/step - loss: 0.2353 - accuracy: 0.8672 - val_loss: 0.3106 - val_accuracy: 0.8305\n", "Epoch 228/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2782 - accuracy: 0.8375\n", "Epoch 228: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 792ms/step - loss: 0.2782 - accuracy: 0.8375 - val_loss: 0.3112 - val_accuracy: 0.8305\n", "Epoch 229/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2693 - accuracy: 0.8875\n", "Epoch 229: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 795ms/step - loss: 0.2693 - accuracy: 0.8875 - val_loss: 0.3124 - val_accuracy: 0.8305\n", "Epoch 230/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2889 - accuracy: 0.8281\n", "Epoch 230: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 943ms/step - loss: 0.2889 - accuracy: 0.8281 - val_loss: 0.3135 - val_accuracy: 0.8305\n", "Epoch 231/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2589 - accuracy: 0.8984\n", "Epoch 231: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 907ms/step - loss: 0.2589 - accuracy: 0.8984 - val_loss: 0.3135 - val_accuracy: 0.8305\n", "Epoch 232/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2456 - accuracy: 0.8984\n", "Epoch 232: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2456 - accuracy: 0.8984 - val_loss: 0.3123 - val_accuracy: 0.8305\n", "Epoch 233/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2860 - accuracy: 0.8281\n", "Epoch 233: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2860 - accuracy: 0.8281 - val_loss: 0.3108 - val_accuracy: 0.8305\n", "Epoch 234/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2758 - accuracy: 0.8438\n", "Epoch 234: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 910ms/step - loss: 0.2758 - accuracy: 0.8438 - val_loss: 0.3082 - val_accuracy: 0.8305\n", "Epoch 235/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2963 - accuracy: 0.8438\n", "Epoch 235: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2963 - accuracy: 0.8438 - val_loss: 0.3071 - val_accuracy: 0.8136\n", "Epoch 236/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2494 - accuracy: 0.8906\n", "Epoch 236: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 946ms/step - loss: 0.2494 - accuracy: 0.8906 - val_loss: 0.3057 - val_accuracy: 0.8136\n", "Epoch 237/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2573 - accuracy: 0.9062\n", "Epoch 237: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 917ms/step - loss: 0.2573 - accuracy: 0.9062 - val_loss: 0.3048 - val_accuracy: 0.8136\n", "Epoch 238/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2491 - accuracy: 0.8828\n", "Epoch 238: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 921ms/step - loss: 0.2491 - accuracy: 0.8828 - val_loss: 0.3050 - val_accuracy: 0.8136\n", "Epoch 239/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2366 - accuracy: 0.9000\n", "Epoch 239: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2366 - accuracy: 0.9000 - val_loss: 0.3059 - val_accuracy: 0.8305\n", "Epoch 240/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2333 - accuracy: 0.9062\n", "Epoch 240: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 945ms/step - loss: 0.2333 - accuracy: 0.9062 - val_loss: 0.3063 - val_accuracy: 0.8475\n", "Epoch 241/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2809 - accuracy: 0.8672\n", "Epoch 241: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2809 - accuracy: 0.8672 - val_loss: 0.3059 - val_accuracy: 0.8305\n", "Epoch 242/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2800 - accuracy: 0.8750\n", "Epoch 242: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2800 - accuracy: 0.8750 - val_loss: 0.3063 - val_accuracy: 0.8475\n", "Epoch 243/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2448 - accuracy: 0.9000\n", "Epoch 243: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2448 - accuracy: 0.9000 - val_loss: 0.3057 - val_accuracy: 0.8305\n", "Epoch 244/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2235 - accuracy: 0.9000\n", "Epoch 244: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 794ms/step - loss: 0.2235 - accuracy: 0.9000 - val_loss: 0.3050 - val_accuracy: 0.8136\n", "Epoch 245/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2548 - accuracy: 0.8625\n", "Epoch 245: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2548 - accuracy: 0.8625 - val_loss: 0.3034 - val_accuracy: 0.8136\n", "Epoch 246/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2482 - accuracy: 0.8672\n", "Epoch 246: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 946ms/step - loss: 0.2482 - accuracy: 0.8672 - val_loss: 0.3021 - val_accuracy: 0.8136\n", "Epoch 247/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2149 - accuracy: 0.9062\n", "Epoch 247: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2149 - accuracy: 0.9062 - val_loss: 0.3014 - val_accuracy: 0.8136\n", "Epoch 248/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2617 - accuracy: 0.8594\n", "Epoch 248: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2617 - accuracy: 0.8594 - val_loss: 0.3010 - val_accuracy: 0.8136\n", "Epoch 249/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2135 - accuracy: 0.9219\n", "Epoch 249: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2135 - accuracy: 0.9219 - val_loss: 0.3009 - val_accuracy: 0.8136\n", "Epoch 250/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2178 - accuracy: 0.9297\n", "Epoch 250: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2178 - accuracy: 0.9297 - val_loss: 0.3010 - val_accuracy: 0.8136\n", "Epoch 251/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2670 - accuracy: 0.8750\n", "Epoch 251: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2670 - accuracy: 0.8750 - val_loss: 0.3018 - val_accuracy: 0.8136\n", "Epoch 252/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2248 - accuracy: 0.8750\n", "Epoch 252: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 818ms/step - loss: 0.2248 - accuracy: 0.8750 - val_loss: 0.3011 - val_accuracy: 0.8136\n", "Epoch 253/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2740 - accuracy: 0.8828\n", "Epoch 253: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2740 - accuracy: 0.8828 - val_loss: 0.2994 - val_accuracy: 0.8136\n", "Epoch 254/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2816 - accuracy: 0.8250\n", "Epoch 254: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 803ms/step - loss: 0.2816 - accuracy: 0.8250 - val_loss: 0.2979 - val_accuracy: 0.8136\n", "Epoch 255/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2820 - accuracy: 0.8359\n", "Epoch 255: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 947ms/step - loss: 0.2820 - accuracy: 0.8359 - val_loss: 0.2963 - val_accuracy: 0.8136\n", "Epoch 256/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2573 - accuracy: 0.8594\n", "Epoch 256: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2573 - accuracy: 0.8594 - val_loss: 0.2953 - val_accuracy: 0.8136\n", "Epoch 257/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2565 - accuracy: 0.8594\n", "Epoch 257: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2565 - accuracy: 0.8594 - val_loss: 0.2960 - val_accuracy: 0.8136\n", "Epoch 258/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2307 - accuracy: 0.8984\n", "Epoch 258: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2307 - accuracy: 0.8984 - val_loss: 0.2969 - val_accuracy: 0.8136\n", "Epoch 259/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2131 - accuracy: 0.8906\n", "Epoch 259: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2131 - accuracy: 0.8906 - val_loss: 0.2983 - val_accuracy: 0.8136\n", "Epoch 260/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2280 - accuracy: 0.8906\n", "Epoch 260: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 902ms/step - loss: 0.2280 - accuracy: 0.8906 - val_loss: 0.2995 - val_accuracy: 0.8136\n", "Epoch 261/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2603 - accuracy: 0.8828\n", "Epoch 261: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2603 - accuracy: 0.8828 - val_loss: 0.3003 - val_accuracy: 0.8136\n", "Epoch 262/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2892 - accuracy: 0.8375\n", "Epoch 262: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2892 - accuracy: 0.8375 - val_loss: 0.3015 - val_accuracy: 0.8136\n", "Epoch 263/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2298 - accuracy: 0.8875\n", "Epoch 263: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2298 - accuracy: 0.8875 - val_loss: 0.3009 - val_accuracy: 0.8136\n", "Epoch 264/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2543 - accuracy: 0.9062\n", "Epoch 264: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 958ms/step - loss: 0.2543 - accuracy: 0.9062 - val_loss: 0.3001 - val_accuracy: 0.8136\n", "Epoch 265/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2106 - accuracy: 0.9375\n", "Epoch 265: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 814ms/step - loss: 0.2106 - accuracy: 0.9375 - val_loss: 0.2987 - val_accuracy: 0.8136\n", "Epoch 266/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2526 - accuracy: 0.8828\n", "Epoch 266: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2526 - accuracy: 0.8828 - val_loss: 0.2968 - val_accuracy: 0.8136\n", "Epoch 267/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2803 - accuracy: 0.8500\n", "Epoch 267: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 853ms/step - loss: 0.2803 - accuracy: 0.8500 - val_loss: 0.2950 - val_accuracy: 0.8136\n", "Epoch 268/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2660 - accuracy: 0.8750\n", "Epoch 268: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 806ms/step - loss: 0.2660 - accuracy: 0.8750 - val_loss: 0.2931 - val_accuracy: 0.8136\n", "Epoch 269/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2276 - accuracy: 0.8828\n", "Epoch 269: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2276 - accuracy: 0.8828 - val_loss: 0.2913 - val_accuracy: 0.8136\n", "Epoch 270/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2157 - accuracy: 0.9125\n", "Epoch 270: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 860ms/step - loss: 0.2157 - accuracy: 0.9125 - val_loss: 0.2903 - val_accuracy: 0.8136\n", "Epoch 271/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1974 - accuracy: 0.9375\n", "Epoch 271: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 898ms/step - loss: 0.1974 - accuracy: 0.9375 - val_loss: 0.2898 - val_accuracy: 0.8136\n", "Epoch 272/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2401 - accuracy: 0.8750\n", "Epoch 272: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 943ms/step - loss: 0.2401 - accuracy: 0.8750 - val_loss: 0.2889 - val_accuracy: 0.8136\n", "Epoch 273/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2718 - accuracy: 0.8375\n", "Epoch 273: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2718 - accuracy: 0.8375 - val_loss: 0.2886 - val_accuracy: 0.8136\n", "Epoch 274/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2322 - accuracy: 0.8984\n", "Epoch 274: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 930ms/step - loss: 0.2322 - accuracy: 0.8984 - val_loss: 0.2888 - val_accuracy: 0.8136\n", "Epoch 275/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2986 - accuracy: 0.8438\n", "Epoch 275: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 957ms/step - loss: 0.2986 - accuracy: 0.8438 - val_loss: 0.2887 - val_accuracy: 0.8136\n", "Epoch 276/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2662 - accuracy: 0.8438\n", "Epoch 276: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2662 - accuracy: 0.8438 - val_loss: 0.2889 - val_accuracy: 0.8136\n", "Epoch 277/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2386 - accuracy: 0.8984\n", "Epoch 277: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2386 - accuracy: 0.8984 - val_loss: 0.2899 - val_accuracy: 0.8136\n", "Epoch 278/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2327 - accuracy: 0.9250\n", "Epoch 278: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2327 - accuracy: 0.9250 - val_loss: 0.2929 - val_accuracy: 0.8136\n", "Epoch 279/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2378 - accuracy: 0.8984\n", "Epoch 279: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2378 - accuracy: 0.8984 - val_loss: 0.2975 - val_accuracy: 0.8136\n", "Epoch 280/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2511 - accuracy: 0.8594\n", "Epoch 280: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2511 - accuracy: 0.8594 - val_loss: 0.3020 - val_accuracy: 0.8136\n", "Epoch 281/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2288 - accuracy: 0.8984\n", "Epoch 281: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 916ms/step - loss: 0.2288 - accuracy: 0.8984 - val_loss: 0.3068 - val_accuracy: 0.8136\n", "Epoch 282/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2698 - accuracy: 0.8359\n", "Epoch 282: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2698 - accuracy: 0.8359 - val_loss: 0.3105 - val_accuracy: 0.8136\n", "Epoch 283/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2154 - accuracy: 0.9141\n", "Epoch 283: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2154 - accuracy: 0.9141 - val_loss: 0.3148 - val_accuracy: 0.7966\n", "Epoch 284/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2556 - accuracy: 0.8500\n", "Epoch 284: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 842ms/step - loss: 0.2556 - accuracy: 0.8500 - val_loss: 0.3190 - val_accuracy: 0.7627\n", "Epoch 285/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2494 - accuracy: 0.8625\n", "Epoch 285: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 2s/step - loss: 0.2494 - accuracy: 0.8625 - val_loss: 0.3235 - val_accuracy: 0.7458\n", "Epoch 286/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2026 - accuracy: 0.8875\n", "Epoch 286: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2026 - accuracy: 0.8875 - val_loss: 0.3262 - val_accuracy: 0.7627\n", "Epoch 287/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2219 - accuracy: 0.8750\n", "Epoch 287: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2219 - accuracy: 0.8750 - val_loss: 0.3293 - val_accuracy: 0.7627\n", "Epoch 288/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2030 - accuracy: 0.9141\n", "Epoch 288: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 909ms/step - loss: 0.2030 - accuracy: 0.9141 - val_loss: 0.3301 - val_accuracy: 0.7627\n", "Epoch 289/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2287 - accuracy: 0.8906\n", "Epoch 289: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 914ms/step - loss: 0.2287 - accuracy: 0.8906 - val_loss: 0.3300 - val_accuracy: 0.7627\n", "Epoch 290/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2328 - accuracy: 0.8750\n", "Epoch 290: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 950ms/step - loss: 0.2328 - accuracy: 0.8750 - val_loss: 0.3270 - val_accuracy: 0.7797\n", "Epoch 291/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2071 - accuracy: 0.9141\n", "Epoch 291: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2071 - accuracy: 0.9141 - val_loss: 0.3240 - val_accuracy: 0.7797\n", "Epoch 292/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2068 - accuracy: 0.9000\n", "Epoch 292: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2068 - accuracy: 0.9000 - val_loss: 0.3218 - val_accuracy: 0.7797\n", "Epoch 293/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1890 - accuracy: 0.9250\n", "Epoch 293: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 804ms/step - loss: 0.1890 - accuracy: 0.9250 - val_loss: 0.3199 - val_accuracy: 0.7797\n", "Epoch 294/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2426 - accuracy: 0.8875\n", "Epoch 294: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 790ms/step - loss: 0.2426 - accuracy: 0.8875 - val_loss: 0.3161 - val_accuracy: 0.8136\n", "Epoch 295/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2291 - accuracy: 0.9125\n", "Epoch 295: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2291 - accuracy: 0.9125 - val_loss: 0.3102 - val_accuracy: 0.8475\n", "Epoch 296/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2617 - accuracy: 0.8500\n", "Epoch 296: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 824ms/step - loss: 0.2617 - accuracy: 0.8500 - val_loss: 0.3041 - val_accuracy: 0.8305\n", "Epoch 297/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1950 - accuracy: 0.9500\n", "Epoch 297: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 818ms/step - loss: 0.1950 - accuracy: 0.9500 - val_loss: 0.2988 - val_accuracy: 0.8305\n", "Epoch 298/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2231 - accuracy: 0.9141\n", "Epoch 298: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2231 - accuracy: 0.9141 - val_loss: 0.2959 - val_accuracy: 0.8305\n", "Epoch 299/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1917 - accuracy: 0.9000\n", "Epoch 299: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1917 - accuracy: 0.9000 - val_loss: 0.2945 - val_accuracy: 0.8305\n", "Epoch 300/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2121 - accuracy: 0.9000\n", "Epoch 300: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 794ms/step - loss: 0.2121 - accuracy: 0.9000 - val_loss: 0.2938 - val_accuracy: 0.8305\n", "Epoch 301/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2052 - accuracy: 0.8828\n", "Epoch 301: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2052 - accuracy: 0.8828 - val_loss: 0.2929 - val_accuracy: 0.8305\n", "Epoch 302/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1914 - accuracy: 0.9375\n", "Epoch 302: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 795ms/step - loss: 0.1914 - accuracy: 0.9375 - val_loss: 0.2915 - val_accuracy: 0.8305\n", "Epoch 303/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2616 - accuracy: 0.8250\n", "Epoch 303: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 800ms/step - loss: 0.2616 - accuracy: 0.8250 - val_loss: 0.2906 - val_accuracy: 0.8305\n", "Epoch 304/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2484 - accuracy: 0.8750\n", "Epoch 304: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2484 - accuracy: 0.8750 - val_loss: 0.2926 - val_accuracy: 0.8305\n", "Epoch 305/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2136 - accuracy: 0.9062\n", "Epoch 305: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2136 - accuracy: 0.9062 - val_loss: 0.2943 - val_accuracy: 0.8305\n", "Epoch 306/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2577 - accuracy: 0.8750\n", "Epoch 306: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 792ms/step - loss: 0.2577 - accuracy: 0.8750 - val_loss: 0.2947 - val_accuracy: 0.8305\n", "Epoch 307/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2036 - accuracy: 0.9297\n", "Epoch 307: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2036 - accuracy: 0.9297 - val_loss: 0.2952 - val_accuracy: 0.8305\n", "Epoch 308/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2358 - accuracy: 0.8594\n", "Epoch 308: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 906ms/step - loss: 0.2358 - accuracy: 0.8594 - val_loss: 0.2963 - val_accuracy: 0.8305\n", "Epoch 309/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2349 - accuracy: 0.9062\n", "Epoch 309: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2349 - accuracy: 0.9062 - val_loss: 0.2975 - val_accuracy: 0.8305\n", "Epoch 310/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2118 - accuracy: 0.8625\n", "Epoch 310: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 808ms/step - loss: 0.2118 - accuracy: 0.8625 - val_loss: 0.2989 - val_accuracy: 0.8305\n", "Epoch 311/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1725 - accuracy: 0.9000\n", "Epoch 311: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1725 - accuracy: 0.9000 - val_loss: 0.2993 - val_accuracy: 0.8305\n", "Epoch 312/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2201 - accuracy: 0.9125\n", "Epoch 312: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2201 - accuracy: 0.9125 - val_loss: 0.3002 - val_accuracy: 0.8305\n", "Epoch 313/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2136 - accuracy: 0.8750\n", "Epoch 313: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2136 - accuracy: 0.8750 - val_loss: 0.3005 - val_accuracy: 0.8305\n", "Epoch 314/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2057 - accuracy: 0.8906\n", "Epoch 314: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 934ms/step - loss: 0.2057 - accuracy: 0.8906 - val_loss: 0.3016 - val_accuracy: 0.8305\n", "Epoch 315/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2134 - accuracy: 0.8984\n", "Epoch 315: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 968ms/step - loss: 0.2134 - accuracy: 0.8984 - val_loss: 0.3029 - val_accuracy: 0.8305\n", "Epoch 316/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2028 - accuracy: 0.9375\n", "Epoch 316: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2028 - accuracy: 0.9375 - val_loss: 0.3031 - val_accuracy: 0.8305\n", "Epoch 317/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2105 - accuracy: 0.8750\n", "Epoch 317: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2105 - accuracy: 0.8750 - val_loss: 0.3014 - val_accuracy: 0.8305\n", "Epoch 318/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2106 - accuracy: 0.8984\n", "Epoch 318: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 918ms/step - loss: 0.2106 - accuracy: 0.8984 - val_loss: 0.3000 - val_accuracy: 0.8305\n", "Epoch 319/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1630 - accuracy: 0.9750\n", "Epoch 319: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 796ms/step - loss: 0.1630 - accuracy: 0.9750 - val_loss: 0.3004 - val_accuracy: 0.8305\n", "Epoch 320/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1539 - accuracy: 0.9500\n", "Epoch 320: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 810ms/step - loss: 0.1539 - accuracy: 0.9500 - val_loss: 0.3006 - val_accuracy: 0.8305\n", "Epoch 321/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2218 - accuracy: 0.8594\n", "Epoch 321: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2218 - accuracy: 0.8594 - val_loss: 0.3013 - val_accuracy: 0.8305\n", "Epoch 322/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2165 - accuracy: 0.9062\n", "Epoch 322: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2165 - accuracy: 0.9062 - val_loss: 0.3022 - val_accuracy: 0.8305\n", "Epoch 323/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1919 - accuracy: 0.9000\n", "Epoch 323: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1919 - accuracy: 0.9000 - val_loss: 0.3030 - val_accuracy: 0.8305\n", "Epoch 324/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1958 - accuracy: 0.9000\n", "Epoch 324: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 850ms/step - loss: 0.1958 - accuracy: 0.9000 - val_loss: 0.3028 - val_accuracy: 0.8305\n", "Epoch 325/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1868 - accuracy: 0.9000\n", "Epoch 325: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 814ms/step - loss: 0.1868 - accuracy: 0.9000 - val_loss: 0.3007 - val_accuracy: 0.8305\n", "Epoch 326/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2316 - accuracy: 0.9062\n", "Epoch 326: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 941ms/step - loss: 0.2316 - accuracy: 0.9062 - val_loss: 0.2972 - val_accuracy: 0.8305\n", "Epoch 327/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2059 - accuracy: 0.8875\n", "Epoch 327: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2059 - accuracy: 0.8875 - val_loss: 0.2908 - val_accuracy: 0.8305\n", "Epoch 328/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1977 - accuracy: 0.8906\n", "Epoch 328: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 969ms/step - loss: 0.1977 - accuracy: 0.8906 - val_loss: 0.2869 - val_accuracy: 0.8305\n", "Epoch 329/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2260 - accuracy: 0.8984\n", "Epoch 329: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 992ms/step - loss: 0.2260 - accuracy: 0.8984 - val_loss: 0.2843 - val_accuracy: 0.8305\n", "Epoch 330/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2437 - accuracy: 0.8625\n", "Epoch 330: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2437 - accuracy: 0.8625 - val_loss: 0.2842 - val_accuracy: 0.8305\n", "Epoch 331/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2069 - accuracy: 0.8984\n", "Epoch 331: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 935ms/step - loss: 0.2069 - accuracy: 0.8984 - val_loss: 0.2851 - val_accuracy: 0.8305\n", "Epoch 332/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1874 - accuracy: 0.9000\n", "Epoch 332: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 869ms/step - loss: 0.1874 - accuracy: 0.9000 - val_loss: 0.2855 - val_accuracy: 0.8305\n", "Epoch 333/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1848 - accuracy: 0.9125\n", "Epoch 333: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 787ms/step - loss: 0.1848 - accuracy: 0.9125 - val_loss: 0.2884 - val_accuracy: 0.8305\n", "Epoch 334/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2140 - accuracy: 0.8984\n", "Epoch 334: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2140 - accuracy: 0.8984 - val_loss: 0.2922 - val_accuracy: 0.8305\n", "Epoch 335/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2155 - accuracy: 0.8594\n", "Epoch 335: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 998ms/step - loss: 0.2155 - accuracy: 0.8594 - val_loss: 0.2948 - val_accuracy: 0.8305\n", "Epoch 336/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2458 - accuracy: 0.8625\n", "Epoch 336: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 826ms/step - loss: 0.2458 - accuracy: 0.8625 - val_loss: 0.2973 - val_accuracy: 0.8305\n", "Epoch 337/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1843 - accuracy: 0.9125\n", "Epoch 337: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 812ms/step - loss: 0.1843 - accuracy: 0.9125 - val_loss: 0.3001 - val_accuracy: 0.8136\n", "Epoch 338/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2171 - accuracy: 0.9000\n", "Epoch 338: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 847ms/step - loss: 0.2171 - accuracy: 0.9000 - val_loss: 0.3006 - val_accuracy: 0.8136\n", "Epoch 339/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2334 - accuracy: 0.8500\n", "Epoch 339: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2334 - accuracy: 0.8500 - val_loss: 0.3007 - val_accuracy: 0.8136\n", "Epoch 340/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1649 - accuracy: 0.9531\n", "Epoch 340: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 921ms/step - loss: 0.1649 - accuracy: 0.9531 - val_loss: 0.3008 - val_accuracy: 0.8136\n", "Epoch 341/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1953 - accuracy: 0.8984\n", "Epoch 341: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1953 - accuracy: 0.8984 - val_loss: 0.3000 - val_accuracy: 0.8136\n", "Epoch 342/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1953 - accuracy: 0.8875\n", "Epoch 342: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 820ms/step - loss: 0.1953 - accuracy: 0.8875 - val_loss: 0.2995 - val_accuracy: 0.8136\n", "Epoch 343/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2022 - accuracy: 0.8906\n", "Epoch 343: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 931ms/step - loss: 0.2022 - accuracy: 0.8906 - val_loss: 0.2981 - val_accuracy: 0.8136\n", "Epoch 344/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2112 - accuracy: 0.8875\n", "Epoch 344: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2112 - accuracy: 0.8875 - val_loss: 0.2967 - val_accuracy: 0.8136\n", "Epoch 345/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2026 - accuracy: 0.9125\n", "Epoch 345: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2026 - accuracy: 0.9125 - val_loss: 0.2950 - val_accuracy: 0.8136\n", "Epoch 346/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2523 - accuracy: 0.8500\n", "Epoch 346: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2523 - accuracy: 0.8500 - val_loss: 0.2945 - val_accuracy: 0.8136\n", "Epoch 347/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1992 - accuracy: 0.8906\n", "Epoch 347: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1992 - accuracy: 0.8906 - val_loss: 0.2937 - val_accuracy: 0.8136\n", "Epoch 348/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2214 - accuracy: 0.8906\n", "Epoch 348: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2214 - accuracy: 0.8906 - val_loss: 0.2934 - val_accuracy: 0.8136\n", "Epoch 349/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1557 - accuracy: 0.9375\n", "Epoch 349: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1557 - accuracy: 0.9375 - val_loss: 0.2937 - val_accuracy: 0.8136\n", "Epoch 350/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2254 - accuracy: 0.8828\n", "Epoch 350: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2254 - accuracy: 0.8828 - val_loss: 0.2925 - val_accuracy: 0.8136\n", "Epoch 351/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2194 - accuracy: 0.8906\n", "Epoch 351: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 891ms/step - loss: 0.2194 - accuracy: 0.8906 - val_loss: 0.2909 - val_accuracy: 0.8136\n", "Epoch 352/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2548 - accuracy: 0.8750\n", "Epoch 352: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 963ms/step - loss: 0.2548 - accuracy: 0.8750 - val_loss: 0.2898 - val_accuracy: 0.8136\n", "Epoch 353/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2142 - accuracy: 0.9062\n", "Epoch 353: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2142 - accuracy: 0.9062 - val_loss: 0.2904 - val_accuracy: 0.8136\n", "Epoch 354/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2285 - accuracy: 0.8984\n", "Epoch 354: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2285 - accuracy: 0.8984 - val_loss: 0.2903 - val_accuracy: 0.8136\n", "Epoch 355/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1971 - accuracy: 0.9250\n", "Epoch 355: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 813ms/step - loss: 0.1971 - accuracy: 0.9250 - val_loss: 0.2898 - val_accuracy: 0.8136\n", "Epoch 356/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1707 - accuracy: 0.9125\n", "Epoch 356: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 804ms/step - loss: 0.1707 - accuracy: 0.9125 - val_loss: 0.2897 - val_accuracy: 0.7966\n", "Epoch 357/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1891 - accuracy: 0.9297\n", "Epoch 357: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1891 - accuracy: 0.9297 - val_loss: 0.2902 - val_accuracy: 0.7966\n", "Epoch 358/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2287 - accuracy: 0.8906\n", "Epoch 358: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 916ms/step - loss: 0.2287 - accuracy: 0.8906 - val_loss: 0.2905 - val_accuracy: 0.7966\n", "Epoch 359/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1855 - accuracy: 0.9000\n", "Epoch 359: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 808ms/step - loss: 0.1855 - accuracy: 0.9000 - val_loss: 0.2893 - val_accuracy: 0.7966\n", "Epoch 360/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1888 - accuracy: 0.9000\n", "Epoch 360: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1888 - accuracy: 0.9000 - val_loss: 0.2888 - val_accuracy: 0.7966\n", "Epoch 361/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1960 - accuracy: 0.8906\n", "Epoch 361: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 937ms/step - loss: 0.1960 - accuracy: 0.8906 - val_loss: 0.2888 - val_accuracy: 0.8136\n", "Epoch 362/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1805 - accuracy: 0.9219\n", "Epoch 362: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1805 - accuracy: 0.9219 - val_loss: 0.2886 - val_accuracy: 0.8136\n", "Epoch 363/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2204 - accuracy: 0.8438\n", "Epoch 363: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2204 - accuracy: 0.8438 - val_loss: 0.2874 - val_accuracy: 0.8136\n", "Epoch 364/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2377 - accuracy: 0.8750\n", "Epoch 364: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2377 - accuracy: 0.8750 - val_loss: 0.2852 - val_accuracy: 0.8305\n", "Epoch 365/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2509 - accuracy: 0.8359\n", "Epoch 365: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2509 - accuracy: 0.8359 - val_loss: 0.2844 - val_accuracy: 0.8305\n", "Epoch 366/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2157 - accuracy: 0.9062\n", "Epoch 366: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 937ms/step - loss: 0.2157 - accuracy: 0.9062 - val_loss: 0.2826 - val_accuracy: 0.8305\n", "Epoch 367/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2052 - accuracy: 0.9062\n", "Epoch 367: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2052 - accuracy: 0.9062 - val_loss: 0.2812 - val_accuracy: 0.8305\n", "Epoch 368/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1466 - accuracy: 0.9766\n", "Epoch 368: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 914ms/step - loss: 0.1466 - accuracy: 0.9766 - val_loss: 0.2792 - val_accuracy: 0.8475\n", "Epoch 369/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2298 - accuracy: 0.8672\n", "Epoch 369: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2298 - accuracy: 0.8672 - val_loss: 0.2770 - val_accuracy: 0.8305\n", "Epoch 370/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2274 - accuracy: 0.8984\n", "Epoch 370: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2274 - accuracy: 0.8984 - val_loss: 0.2750 - val_accuracy: 0.8305\n", "Epoch 371/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2067 - accuracy: 0.8875\n", "Epoch 371: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 811ms/step - loss: 0.2067 - accuracy: 0.8875 - val_loss: 0.2723 - val_accuracy: 0.8305\n", "Epoch 372/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1376 - accuracy: 0.9250\n", "Epoch 372: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 806ms/step - loss: 0.1376 - accuracy: 0.9250 - val_loss: 0.2710 - val_accuracy: 0.8305\n", "Epoch 373/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1334 - accuracy: 0.9766\n", "Epoch 373: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1334 - accuracy: 0.9766 - val_loss: 0.2704 - val_accuracy: 0.8305\n", "Epoch 374/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1969 - accuracy: 0.9062\n", "Epoch 374: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1969 - accuracy: 0.9062 - val_loss: 0.2690 - val_accuracy: 0.8305\n", "Epoch 375/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1532 - accuracy: 0.9250\n", "Epoch 375: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1532 - accuracy: 0.9250 - val_loss: 0.2681 - val_accuracy: 0.8305\n", "Epoch 376/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1761 - accuracy: 0.9375\n", "Epoch 376: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1761 - accuracy: 0.9375 - val_loss: 0.2677 - val_accuracy: 0.8305\n", "Epoch 377/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1927 - accuracy: 0.9219\n", "Epoch 377: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 916ms/step - loss: 0.1927 - accuracy: 0.9219 - val_loss: 0.2674 - val_accuracy: 0.8305\n", "Epoch 378/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1983 - accuracy: 0.9297\n", "Epoch 378: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1983 - accuracy: 0.9297 - val_loss: 0.2671 - val_accuracy: 0.8305\n", "Epoch 379/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1826 - accuracy: 0.9375\n", "Epoch 379: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 806ms/step - loss: 0.1826 - accuracy: 0.9375 - val_loss: 0.2670 - val_accuracy: 0.8305\n", "Epoch 380/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1814 - accuracy: 0.8875\n", "Epoch 380: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 803ms/step - loss: 0.1814 - accuracy: 0.8875 - val_loss: 0.2679 - val_accuracy: 0.8305\n", "Epoch 381/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1725 - accuracy: 0.9125\n", "Epoch 381: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 797ms/step - loss: 0.1725 - accuracy: 0.9125 - val_loss: 0.2694 - val_accuracy: 0.8305\n", "Epoch 382/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1709 - accuracy: 0.9219\n", "Epoch 382: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 948ms/step - loss: 0.1709 - accuracy: 0.9219 - val_loss: 0.2718 - val_accuracy: 0.8305\n", "Epoch 383/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1744 - accuracy: 0.9125\n", "Epoch 383: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 988ms/step - loss: 0.1744 - accuracy: 0.9125 - val_loss: 0.2752 - val_accuracy: 0.8305\n", "Epoch 384/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1834 - accuracy: 0.9250\n", "Epoch 384: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 815ms/step - loss: 0.1834 - accuracy: 0.9250 - val_loss: 0.2793 - val_accuracy: 0.8136\n", "Epoch 385/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1865 - accuracy: 0.9297\n", "Epoch 385: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1865 - accuracy: 0.9297 - val_loss: 0.2834 - val_accuracy: 0.8136\n", "Epoch 386/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2197 - accuracy: 0.8750\n", "Epoch 386: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2197 - accuracy: 0.8750 - val_loss: 0.2869 - val_accuracy: 0.8305\n", "Epoch 387/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1715 - accuracy: 0.9141\n", "Epoch 387: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 938ms/step - loss: 0.1715 - accuracy: 0.9141 - val_loss: 0.2888 - val_accuracy: 0.8305\n", "Epoch 388/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1848 - accuracy: 0.8750\n", "Epoch 388: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 815ms/step - loss: 0.1848 - accuracy: 0.8750 - val_loss: 0.2891 - val_accuracy: 0.8305\n", "Epoch 389/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2054 - accuracy: 0.9219\n", "Epoch 389: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2054 - accuracy: 0.9219 - val_loss: 0.2882 - val_accuracy: 0.8305\n", "Epoch 390/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1498 - accuracy: 0.9500\n", "Epoch 390: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1498 - accuracy: 0.9500 - val_loss: 0.2871 - val_accuracy: 0.8305\n", "Epoch 391/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1969 - accuracy: 0.9125\n", "Epoch 391: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 796ms/step - loss: 0.1969 - accuracy: 0.9125 - val_loss: 0.2851 - val_accuracy: 0.8305\n", "Epoch 392/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1831 - accuracy: 0.9125\n", "Epoch 392: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1831 - accuracy: 0.9125 - val_loss: 0.2831 - val_accuracy: 0.8305\n", "Epoch 393/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2146 - accuracy: 0.8625\n", "Epoch 393: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 811ms/step - loss: 0.2146 - accuracy: 0.8625 - val_loss: 0.2820 - val_accuracy: 0.8305\n", "Epoch 394/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1512 - accuracy: 0.9375\n", "Epoch 394: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 797ms/step - loss: 0.1512 - accuracy: 0.9375 - val_loss: 0.2816 - val_accuracy: 0.8305\n", "Epoch 395/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1887 - accuracy: 0.8984\n", "Epoch 395: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1887 - accuracy: 0.8984 - val_loss: 0.2810 - val_accuracy: 0.8305\n", "Epoch 396/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1964 - accuracy: 0.9250\n", "Epoch 396: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 805ms/step - loss: 0.1964 - accuracy: 0.9250 - val_loss: 0.2817 - val_accuracy: 0.8305\n", "Epoch 397/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1661 - accuracy: 0.9219\n", "Epoch 397: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 969ms/step - loss: 0.1661 - accuracy: 0.9219 - val_loss: 0.2819 - val_accuracy: 0.8136\n", "Epoch 398/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1866 - accuracy: 0.9219\n", "Epoch 398: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1866 - accuracy: 0.9219 - val_loss: 0.2835 - val_accuracy: 0.8136\n", "Epoch 399/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1613 - accuracy: 0.9453\n", "Epoch 399: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1613 - accuracy: 0.9453 - val_loss: 0.2854 - val_accuracy: 0.8136\n", "Epoch 400/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1936 - accuracy: 0.9000\n", "Epoch 400: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1936 - accuracy: 0.9000 - val_loss: 0.2866 - val_accuracy: 0.8136\n", "Epoch 401/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1871 - accuracy: 0.9219\n", "Epoch 401: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1871 - accuracy: 0.9219 - val_loss: 0.2878 - val_accuracy: 0.7966\n", "Epoch 402/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1557 - accuracy: 0.9375\n", "Epoch 402: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1557 - accuracy: 0.9375 - val_loss: 0.2889 - val_accuracy: 0.7966\n", "Epoch 403/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1863 - accuracy: 0.9125\n", "Epoch 403: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 822ms/step - loss: 0.1863 - accuracy: 0.9125 - val_loss: 0.2906 - val_accuracy: 0.8136\n", "Epoch 404/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1650 - accuracy: 0.9297\n", "Epoch 404: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 948ms/step - loss: 0.1650 - accuracy: 0.9297 - val_loss: 0.2921 - val_accuracy: 0.8136\n", "Epoch 405/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1796 - accuracy: 0.9141\n", "Epoch 405: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 956ms/step - loss: 0.1796 - accuracy: 0.9141 - val_loss: 0.2936 - val_accuracy: 0.8136\n", "Epoch 406/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1615 - accuracy: 0.9531\n", "Epoch 406: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1615 - accuracy: 0.9531 - val_loss: 0.2949 - val_accuracy: 0.8136\n", "Epoch 407/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1877 - accuracy: 0.9141\n", "Epoch 407: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1877 - accuracy: 0.9141 - val_loss: 0.2954 - val_accuracy: 0.8136\n", "Epoch 408/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2060 - accuracy: 0.8875\n", "Epoch 408: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2060 - accuracy: 0.8875 - val_loss: 0.2953 - val_accuracy: 0.8136\n", "Epoch 409/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1334 - accuracy: 0.9688\n", "Epoch 409: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 943ms/step - loss: 0.1334 - accuracy: 0.9688 - val_loss: 0.2956 - val_accuracy: 0.8136\n", "Epoch 410/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1217 - accuracy: 0.9500\n", "Epoch 410: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 818ms/step - loss: 0.1217 - accuracy: 0.9500 - val_loss: 0.2970 - val_accuracy: 0.8136\n", "Epoch 411/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1435 - accuracy: 0.9609\n", "Epoch 411: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 956ms/step - loss: 0.1435 - accuracy: 0.9609 - val_loss: 0.2978 - val_accuracy: 0.8136\n", "Epoch 412/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2369 - accuracy: 0.8875\n", "Epoch 412: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2369 - accuracy: 0.8875 - val_loss: 0.2975 - val_accuracy: 0.8136\n", "Epoch 413/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1769 - accuracy: 0.9062\n", "Epoch 413: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 925ms/step - loss: 0.1769 - accuracy: 0.9062 - val_loss: 0.2976 - val_accuracy: 0.8136\n", "Epoch 414/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1529 - accuracy: 0.9297\n", "Epoch 414: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1529 - accuracy: 0.9297 - val_loss: 0.2980 - val_accuracy: 0.8136\n", "Epoch 415/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1929 - accuracy: 0.9141\n", "Epoch 415: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1929 - accuracy: 0.9141 - val_loss: 0.2981 - val_accuracy: 0.8136\n", "Epoch 416/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1664 - accuracy: 0.9375\n", "Epoch 416: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1664 - accuracy: 0.9375 - val_loss: 0.2983 - val_accuracy: 0.8136\n", "Epoch 417/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1497 - accuracy: 0.9500\n", "Epoch 417: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 802ms/step - loss: 0.1497 - accuracy: 0.9500 - val_loss: 0.2982 - val_accuracy: 0.8136\n", "Epoch 418/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1411 - accuracy: 0.9500\n", "Epoch 418: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1411 - accuracy: 0.9500 - val_loss: 0.2985 - val_accuracy: 0.8136\n", "Epoch 419/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2223 - accuracy: 0.8750\n", "Epoch 419: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2223 - accuracy: 0.8750 - val_loss: 0.2979 - val_accuracy: 0.8136\n", "Epoch 420/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2264 - accuracy: 0.8750\n", "Epoch 420: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 940ms/step - loss: 0.2264 - accuracy: 0.8750 - val_loss: 0.2962 - val_accuracy: 0.8136\n", "Epoch 421/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1621 - accuracy: 0.9219\n", "Epoch 421: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 898ms/step - loss: 0.1621 - accuracy: 0.9219 - val_loss: 0.2952 - val_accuracy: 0.8136\n", "Epoch 422/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1696 - accuracy: 0.9500\n", "Epoch 422: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1696 - accuracy: 0.9500 - val_loss: 0.2945 - val_accuracy: 0.8305\n", "Epoch 423/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2096 - accuracy: 0.8984\n", "Epoch 423: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2096 - accuracy: 0.8984 - val_loss: 0.2934 - val_accuracy: 0.8305\n", "Epoch 424/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2152 - accuracy: 0.9000\n", "Epoch 424: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2152 - accuracy: 0.9000 - val_loss: 0.2935 - val_accuracy: 0.8305\n", "Epoch 425/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1662 - accuracy: 0.9297\n", "Epoch 425: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 902ms/step - loss: 0.1662 - accuracy: 0.9297 - val_loss: 0.2931 - val_accuracy: 0.8305\n", "Epoch 426/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1505 - accuracy: 0.9297\n", "Epoch 426: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 904ms/step - loss: 0.1505 - accuracy: 0.9297 - val_loss: 0.2917 - val_accuracy: 0.8305\n", "Epoch 427/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1576 - accuracy: 0.9375\n", "Epoch 427: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1576 - accuracy: 0.9375 - val_loss: 0.2896 - val_accuracy: 0.8305\n", "Epoch 428/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2311 - accuracy: 0.8625\n", "Epoch 428: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 800ms/step - loss: 0.2311 - accuracy: 0.8625 - val_loss: 0.2872 - val_accuracy: 0.8305\n", "Epoch 429/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1310 - accuracy: 0.9125\n", "Epoch 429: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 815ms/step - loss: 0.1310 - accuracy: 0.9125 - val_loss: 0.2852 - val_accuracy: 0.8305\n", "Epoch 430/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1362 - accuracy: 0.9625\n", "Epoch 430: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 809ms/step - loss: 0.1362 - accuracy: 0.9625 - val_loss: 0.2846 - val_accuracy: 0.8305\n", "Epoch 431/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1907 - accuracy: 0.8672\n", "Epoch 431: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 970ms/step - loss: 0.1907 - accuracy: 0.8672 - val_loss: 0.2838 - val_accuracy: 0.8305\n", "Epoch 432/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1620 - accuracy: 0.9375\n", "Epoch 432: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1620 - accuracy: 0.9375 - val_loss: 0.2835 - val_accuracy: 0.8305\n", "Epoch 433/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1835 - accuracy: 0.9000\n", "Epoch 433: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 804ms/step - loss: 0.1835 - accuracy: 0.9000 - val_loss: 0.2827 - val_accuracy: 0.8305\n", "Epoch 434/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1855 - accuracy: 0.8875\n", "Epoch 434: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 819ms/step - loss: 0.1855 - accuracy: 0.8875 - val_loss: 0.2822 - val_accuracy: 0.8305\n", "Epoch 435/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1618 - accuracy: 0.9453\n", "Epoch 435: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1618 - accuracy: 0.9453 - val_loss: 0.2819 - val_accuracy: 0.8305\n", "Epoch 436/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1945 - accuracy: 0.9000\n", "Epoch 436: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 824ms/step - loss: 0.1945 - accuracy: 0.9000 - val_loss: 0.2820 - val_accuracy: 0.8305\n", "Epoch 437/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1356 - accuracy: 0.9766\n", "Epoch 437: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1356 - accuracy: 0.9766 - val_loss: 0.2816 - val_accuracy: 0.8305\n", "Epoch 438/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1677 - accuracy: 0.9125\n", "Epoch 438: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1677 - accuracy: 0.9125 - val_loss: 0.2828 - val_accuracy: 0.8305\n", "Epoch 439/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1504 - accuracy: 0.9219\n", "Epoch 439: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 953ms/step - loss: 0.1504 - accuracy: 0.9219 - val_loss: 0.2843 - val_accuracy: 0.8305\n", "Epoch 440/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2032 - accuracy: 0.8875\n", "Epoch 440: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 842ms/step - loss: 0.2032 - accuracy: 0.8875 - val_loss: 0.2862 - val_accuracy: 0.8305\n", "Epoch 441/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1492 - accuracy: 0.9625\n", "Epoch 441: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 815ms/step - loss: 0.1492 - accuracy: 0.9625 - val_loss: 0.2884 - val_accuracy: 0.8305\n", "Epoch 442/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1689 - accuracy: 0.9125\n", "Epoch 442: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 819ms/step - loss: 0.1689 - accuracy: 0.9125 - val_loss: 0.2880 - val_accuracy: 0.8305\n", "Epoch 443/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1659 - accuracy: 0.9250\n", "Epoch 443: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1659 - accuracy: 0.9250 - val_loss: 0.2883 - val_accuracy: 0.8305\n", "Epoch 444/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2104 - accuracy: 0.8828\n", "Epoch 444: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 949ms/step - loss: 0.2104 - accuracy: 0.8828 - val_loss: 0.2863 - val_accuracy: 0.8305\n", "Epoch 445/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1544 - accuracy: 0.9219\n", "Epoch 445: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 942ms/step - loss: 0.1544 - accuracy: 0.9219 - val_loss: 0.2832 - val_accuracy: 0.8305\n", "Epoch 446/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1321 - accuracy: 0.9766\n", "Epoch 446: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 938ms/step - loss: 0.1321 - accuracy: 0.9766 - val_loss: 0.2813 - val_accuracy: 0.8305\n", "Epoch 447/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1680 - accuracy: 0.9125\n", "Epoch 447: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1680 - accuracy: 0.9125 - val_loss: 0.2811 - val_accuracy: 0.8136\n", "Epoch 448/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1816 - accuracy: 0.9141\n", "Epoch 448: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1816 - accuracy: 0.9141 - val_loss: 0.2806 - val_accuracy: 0.8136\n", "Epoch 449/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1797 - accuracy: 0.9000\n", "Epoch 449: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1797 - accuracy: 0.9000 - val_loss: 0.2814 - val_accuracy: 0.8136\n", "Epoch 450/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1986 - accuracy: 0.8750\n", "Epoch 450: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1986 - accuracy: 0.8750 - val_loss: 0.2840 - val_accuracy: 0.8136\n", "Epoch 451/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1813 - accuracy: 0.8984\n", "Epoch 451: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1813 - accuracy: 0.8984 - val_loss: 0.2866 - val_accuracy: 0.8136\n", "Epoch 452/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2064 - accuracy: 0.8375\n", "Epoch 452: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 833ms/step - loss: 0.2064 - accuracy: 0.8375 - val_loss: 0.2891 - val_accuracy: 0.8136\n", "Epoch 453/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1394 - accuracy: 0.9625\n", "Epoch 453: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 831ms/step - loss: 0.1394 - accuracy: 0.9625 - val_loss: 0.2909 - val_accuracy: 0.8136\n", "Epoch 454/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1555 - accuracy: 0.9375\n", "Epoch 454: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1555 - accuracy: 0.9375 - val_loss: 0.2903 - val_accuracy: 0.8136\n", "Epoch 455/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1647 - accuracy: 0.9375\n", "Epoch 455: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 874ms/step - loss: 0.1647 - accuracy: 0.9375 - val_loss: 0.2888 - val_accuracy: 0.8136\n", "Epoch 456/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2253 - accuracy: 0.8625\n", "Epoch 456: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2253 - accuracy: 0.8625 - val_loss: 0.2889 - val_accuracy: 0.8136\n", "Epoch 457/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1515 - accuracy: 0.9625\n", "Epoch 457: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1515 - accuracy: 0.9625 - val_loss: 0.2885 - val_accuracy: 0.8136\n", "Epoch 458/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1796 - accuracy: 0.9141\n", "Epoch 458: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1796 - accuracy: 0.9141 - val_loss: 0.2875 - val_accuracy: 0.8136\n", "Epoch 459/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1726 - accuracy: 0.9000\n", "Epoch 459: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1726 - accuracy: 0.9000 - val_loss: 0.2845 - val_accuracy: 0.8136\n", "Epoch 460/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1235 - accuracy: 0.9500\n", "Epoch 460: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1235 - accuracy: 0.9500 - val_loss: 0.2820 - val_accuracy: 0.8136\n", "Epoch 461/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1356 - accuracy: 0.9375\n", "Epoch 461: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1356 - accuracy: 0.9375 - val_loss: 0.2795 - val_accuracy: 0.8136\n", "Epoch 462/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1549 - accuracy: 0.9625\n", "Epoch 462: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1549 - accuracy: 0.9625 - val_loss: 0.2786 - val_accuracy: 0.8136\n", "Epoch 463/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1813 - accuracy: 0.9141\n", "Epoch 463: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 936ms/step - loss: 0.1813 - accuracy: 0.9141 - val_loss: 0.2789 - val_accuracy: 0.8305\n", "Epoch 464/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1662 - accuracy: 0.9375\n", "Epoch 464: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1662 - accuracy: 0.9375 - val_loss: 0.2788 - val_accuracy: 0.8305\n", "Epoch 465/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1256 - accuracy: 0.9750\n", "Epoch 465: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 833ms/step - loss: 0.1256 - accuracy: 0.9750 - val_loss: 0.2806 - val_accuracy: 0.8305\n", "Epoch 466/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1848 - accuracy: 0.9141\n", "Epoch 466: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1848 - accuracy: 0.9141 - val_loss: 0.2832 - val_accuracy: 0.8136\n", "Epoch 467/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1815 - accuracy: 0.9219\n", "Epoch 467: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 932ms/step - loss: 0.1815 - accuracy: 0.9219 - val_loss: 0.2864 - val_accuracy: 0.8136\n", "Epoch 468/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1715 - accuracy: 0.8906\n", "Epoch 468: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1715 - accuracy: 0.8906 - val_loss: 0.2882 - val_accuracy: 0.8136\n", "Epoch 469/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1390 - accuracy: 0.9375\n", "Epoch 469: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 969ms/step - loss: 0.1390 - accuracy: 0.9375 - val_loss: 0.2885 - val_accuracy: 0.8136\n", "Epoch 470/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1557 - accuracy: 0.9000\n", "Epoch 470: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 808ms/step - loss: 0.1557 - accuracy: 0.9000 - val_loss: 0.2893 - val_accuracy: 0.8136\n", "Epoch 471/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1416 - accuracy: 0.9375\n", "Epoch 471: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1416 - accuracy: 0.9375 - val_loss: 0.2901 - val_accuracy: 0.8136\n", "Epoch 472/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1847 - accuracy: 0.9000\n", "Epoch 472: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 875ms/step - loss: 0.1847 - accuracy: 0.9000 - val_loss: 0.2897 - val_accuracy: 0.8136\n", "Epoch 473/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1655 - accuracy: 0.9297\n", "Epoch 473: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 953ms/step - loss: 0.1655 - accuracy: 0.9297 - val_loss: 0.2874 - val_accuracy: 0.8136\n", "Epoch 474/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1800 - accuracy: 0.9141\n", "Epoch 474: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 923ms/step - loss: 0.1800 - accuracy: 0.9141 - val_loss: 0.2858 - val_accuracy: 0.8136\n", "Epoch 475/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1262 - accuracy: 0.9453\n", "Epoch 475: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 993ms/step - loss: 0.1262 - accuracy: 0.9453 - val_loss: 0.2833 - val_accuracy: 0.8305\n", "Epoch 476/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2006 - accuracy: 0.8906\n", "Epoch 476: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 930ms/step - loss: 0.2006 - accuracy: 0.8906 - val_loss: 0.2805 - val_accuracy: 0.8305\n", "Epoch 477/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1352 - accuracy: 0.9609\n", "Epoch 477: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 925ms/step - loss: 0.1352 - accuracy: 0.9609 - val_loss: 0.2774 - val_accuracy: 0.8305\n", "Epoch 478/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1754 - accuracy: 0.8906\n", "Epoch 478: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1754 - accuracy: 0.8906 - val_loss: 0.2742 - val_accuracy: 0.8305\n", "Epoch 479/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1439 - accuracy: 0.9531\n", "Epoch 479: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 920ms/step - loss: 0.1439 - accuracy: 0.9531 - val_loss: 0.2717 - val_accuracy: 0.8305\n", "Epoch 480/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1415 - accuracy: 0.9531\n", "Epoch 480: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1415 - accuracy: 0.9531 - val_loss: 0.2691 - val_accuracy: 0.8305\n", "Epoch 481/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1797 - accuracy: 0.9062\n", "Epoch 481: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1797 - accuracy: 0.9062 - val_loss: 0.2675 - val_accuracy: 0.8305\n", "Epoch 482/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1773 - accuracy: 0.9000\n", "Epoch 482: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1773 - accuracy: 0.9000 - val_loss: 0.2663 - val_accuracy: 0.8305\n", "Epoch 483/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1369 - accuracy: 0.9375\n", "Epoch 483: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1369 - accuracy: 0.9375 - val_loss: 0.2664 - val_accuracy: 0.8305\n", "Epoch 484/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1577 - accuracy: 0.9141\n", "Epoch 484: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1577 - accuracy: 0.9141 - val_loss: 0.2667 - val_accuracy: 0.8305\n", "Epoch 485/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1333 - accuracy: 0.9531\n", "Epoch 485: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 956ms/step - loss: 0.1333 - accuracy: 0.9531 - val_loss: 0.2676 - val_accuracy: 0.8305\n", "Epoch 486/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1250 - accuracy: 0.9625\n", "Epoch 486: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 825ms/step - loss: 0.1250 - accuracy: 0.9625 - val_loss: 0.2692 - val_accuracy: 0.8305\n", "Epoch 487/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1775 - accuracy: 0.8875\n", "Epoch 487: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1775 - accuracy: 0.8875 - val_loss: 0.2708 - val_accuracy: 0.8305\n", "Epoch 488/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1744 - accuracy: 0.9297\n", "Epoch 488: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1744 - accuracy: 0.9297 - val_loss: 0.2726 - val_accuracy: 0.8305\n", "Epoch 489/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1200 - accuracy: 0.9500\n", "Epoch 489: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1200 - accuracy: 0.9500 - val_loss: 0.2729 - val_accuracy: 0.8305\n", "Epoch 490/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1249 - accuracy: 0.9375\n", "Epoch 490: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1249 - accuracy: 0.9375 - val_loss: 0.2736 - val_accuracy: 0.8305\n", "Epoch 491/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1771 - accuracy: 0.9250\n", "Epoch 491: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1771 - accuracy: 0.9250 - val_loss: 0.2729 - val_accuracy: 0.8305\n", "Epoch 492/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1549 - accuracy: 0.9125\n", "Epoch 492: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 818ms/step - loss: 0.1549 - accuracy: 0.9125 - val_loss: 0.2700 - val_accuracy: 0.8305\n", "Epoch 493/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1681 - accuracy: 0.9141\n", "Epoch 493: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1681 - accuracy: 0.9141 - val_loss: 0.2669 - val_accuracy: 0.8305\n", "Epoch 494/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2009 - accuracy: 0.8750\n", "Epoch 494: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 828ms/step - loss: 0.2009 - accuracy: 0.8750 - val_loss: 0.2638 - val_accuracy: 0.8475\n", "Epoch 495/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1664 - accuracy: 0.9375\n", "Epoch 495: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1664 - accuracy: 0.9375 - val_loss: 0.2620 - val_accuracy: 0.8475\n", "Epoch 496/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2320 - accuracy: 0.8984\n", "Epoch 496: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.2320 - accuracy: 0.8984 - val_loss: 0.2619 - val_accuracy: 0.8475\n", "Epoch 497/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1626 - accuracy: 0.8906\n", "Epoch 497: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1626 - accuracy: 0.8906 - val_loss: 0.2602 - val_accuracy: 0.8644\n", "Epoch 498/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1545 - accuracy: 0.9531\n", "Epoch 498: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 979ms/step - loss: 0.1545 - accuracy: 0.9531 - val_loss: 0.2595 - val_accuracy: 0.8644\n", "Epoch 499/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1404 - accuracy: 0.9875\n", "Epoch 499: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1404 - accuracy: 0.9875 - val_loss: 0.2609 - val_accuracy: 0.8644\n", "Epoch 500/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1046 - accuracy: 0.9875\n", "Epoch 500: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 843ms/step - loss: 0.1046 - accuracy: 0.9875 - val_loss: 0.2629 - val_accuracy: 0.8644\n", "Epoch 501/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1495 - accuracy: 0.9531\n", "Epoch 501: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 952ms/step - loss: 0.1495 - accuracy: 0.9531 - val_loss: 0.2650 - val_accuracy: 0.8644\n", "Epoch 502/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1643 - accuracy: 0.9141\n", "Epoch 502: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1643 - accuracy: 0.9141 - val_loss: 0.2670 - val_accuracy: 0.8644\n", "Epoch 503/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1779 - accuracy: 0.9062\n", "Epoch 503: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1779 - accuracy: 0.9062 - val_loss: 0.2686 - val_accuracy: 0.8644\n", "Epoch 504/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1600 - accuracy: 0.9625\n", "Epoch 504: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1600 - accuracy: 0.9625 - val_loss: 0.2689 - val_accuracy: 0.8644\n", "Epoch 505/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1275 - accuracy: 0.9625\n", "Epoch 505: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1275 - accuracy: 0.9625 - val_loss: 0.2680 - val_accuracy: 0.8644\n", "Epoch 506/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1473 - accuracy: 0.9375\n", "Epoch 506: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1473 - accuracy: 0.9375 - val_loss: 0.2678 - val_accuracy: 0.8644\n", "Epoch 507/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1198 - accuracy: 0.9609\n", "Epoch 507: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 968ms/step - loss: 0.1198 - accuracy: 0.9609 - val_loss: 0.2672 - val_accuracy: 0.8644\n", "Epoch 508/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1290 - accuracy: 0.9625\n", "Epoch 508: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 804ms/step - loss: 0.1290 - accuracy: 0.9625 - val_loss: 0.2670 - val_accuracy: 0.8644\n", "Epoch 509/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1622 - accuracy: 0.9219\n", "Epoch 509: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1622 - accuracy: 0.9219 - val_loss: 0.2672 - val_accuracy: 0.8644\n", "Epoch 510/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1284 - accuracy: 0.9250\n", "Epoch 510: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 835ms/step - loss: 0.1284 - accuracy: 0.9250 - val_loss: 0.2674 - val_accuracy: 0.8644\n", "Epoch 511/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1641 - accuracy: 0.9375\n", "Epoch 511: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1641 - accuracy: 0.9375 - val_loss: 0.2685 - val_accuracy: 0.8644\n", "Epoch 512/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1069 - accuracy: 0.9609\n", "Epoch 512: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1069 - accuracy: 0.9609 - val_loss: 0.2706 - val_accuracy: 0.8475\n", "Epoch 513/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1871 - accuracy: 0.9250\n", "Epoch 513: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 834ms/step - loss: 0.1871 - accuracy: 0.9250 - val_loss: 0.2733 - val_accuracy: 0.8305\n", "Epoch 514/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1451 - accuracy: 0.9297\n", "Epoch 514: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1451 - accuracy: 0.9297 - val_loss: 0.2743 - val_accuracy: 0.8305\n", "Epoch 515/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1631 - accuracy: 0.9375\n", "Epoch 515: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1631 - accuracy: 0.9375 - val_loss: 0.2753 - val_accuracy: 0.8305\n", "Epoch 516/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1393 - accuracy: 0.9297\n", "Epoch 516: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1393 - accuracy: 0.9297 - val_loss: 0.2769 - val_accuracy: 0.8305\n", "Epoch 517/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1717 - accuracy: 0.9250\n", "Epoch 517: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1717 - accuracy: 0.9250 - val_loss: 0.2786 - val_accuracy: 0.8305\n", "Epoch 518/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2001 - accuracy: 0.9250\n", "Epoch 518: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 809ms/step - loss: 0.2001 - accuracy: 0.9250 - val_loss: 0.2801 - val_accuracy: 0.8136\n", "Epoch 519/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1469 - accuracy: 0.9062\n", "Epoch 519: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 994ms/step - loss: 0.1469 - accuracy: 0.9062 - val_loss: 0.2800 - val_accuracy: 0.8136\n", "Epoch 520/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1444 - accuracy: 0.9531\n", "Epoch 520: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 929ms/step - loss: 0.1444 - accuracy: 0.9531 - val_loss: 0.2781 - val_accuracy: 0.8136\n", "Epoch 521/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1783 - accuracy: 0.9219\n", "Epoch 521: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1783 - accuracy: 0.9219 - val_loss: 0.2761 - val_accuracy: 0.8136\n", "Epoch 522/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1481 - accuracy: 0.9625\n", "Epoch 522: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 815ms/step - loss: 0.1481 - accuracy: 0.9625 - val_loss: 0.2747 - val_accuracy: 0.8136\n", "Epoch 523/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1230 - accuracy: 0.9500\n", "Epoch 523: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1230 - accuracy: 0.9500 - val_loss: 0.2744 - val_accuracy: 0.8136\n", "Epoch 524/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1329 - accuracy: 0.9625\n", "Epoch 524: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1329 - accuracy: 0.9625 - val_loss: 0.2744 - val_accuracy: 0.8136\n", "Epoch 525/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1305 - accuracy: 0.9531\n", "Epoch 525: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1305 - accuracy: 0.9531 - val_loss: 0.2744 - val_accuracy: 0.8136\n", "Epoch 526/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0974 - accuracy: 0.9750\n", "Epoch 526: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0974 - accuracy: 0.9750 - val_loss: 0.2743 - val_accuracy: 0.8136\n", "Epoch 527/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2049 - accuracy: 0.9125\n", "Epoch 527: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.2049 - accuracy: 0.9125 - val_loss: 0.2730 - val_accuracy: 0.8136\n", "Epoch 528/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1441 - accuracy: 0.9297\n", "Epoch 528: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 964ms/step - loss: 0.1441 - accuracy: 0.9297 - val_loss: 0.2722 - val_accuracy: 0.8136\n", "Epoch 529/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1328 - accuracy: 0.9453\n", "Epoch 529: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 973ms/step - loss: 0.1328 - accuracy: 0.9453 - val_loss: 0.2716 - val_accuracy: 0.8136\n", "Epoch 530/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1522 - accuracy: 0.9375\n", "Epoch 530: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1522 - accuracy: 0.9375 - val_loss: 0.2708 - val_accuracy: 0.8136\n", "Epoch 531/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1479 - accuracy: 0.9531\n", "Epoch 531: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1479 - accuracy: 0.9531 - val_loss: 0.2707 - val_accuracy: 0.8136\n", "Epoch 532/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1405 - accuracy: 0.9375\n", "Epoch 532: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 824ms/step - loss: 0.1405 - accuracy: 0.9375 - val_loss: 0.2708 - val_accuracy: 0.8136\n", "Epoch 533/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1355 - accuracy: 0.9219\n", "Epoch 533: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 929ms/step - loss: 0.1355 - accuracy: 0.9219 - val_loss: 0.2722 - val_accuracy: 0.8136\n", "Epoch 534/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1524 - accuracy: 0.9375\n", "Epoch 534: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 975ms/step - loss: 0.1524 - accuracy: 0.9375 - val_loss: 0.2752 - val_accuracy: 0.8136\n", "Epoch 535/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1148 - accuracy: 0.9625\n", "Epoch 535: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 825ms/step - loss: 0.1148 - accuracy: 0.9625 - val_loss: 0.2764 - val_accuracy: 0.8136\n", "Epoch 536/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1230 - accuracy: 0.9500\n", "Epoch 536: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 812ms/step - loss: 0.1230 - accuracy: 0.9500 - val_loss: 0.2759 - val_accuracy: 0.8136\n", "Epoch 537/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1516 - accuracy: 0.9500\n", "Epoch 537: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1516 - accuracy: 0.9500 - val_loss: 0.2749 - val_accuracy: 0.8136\n", "Epoch 538/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1491 - accuracy: 0.9125\n", "Epoch 538: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 835ms/step - loss: 0.1491 - accuracy: 0.9125 - val_loss: 0.2737 - val_accuracy: 0.8136\n", "Epoch 539/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1335 - accuracy: 0.9766\n", "Epoch 539: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 934ms/step - loss: 0.1335 - accuracy: 0.9766 - val_loss: 0.2722 - val_accuracy: 0.8305\n", "Epoch 540/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1515 - accuracy: 0.9375\n", "Epoch 540: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 836ms/step - loss: 0.1515 - accuracy: 0.9375 - val_loss: 0.2716 - val_accuracy: 0.8305\n", "Epoch 541/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1613 - accuracy: 0.9125\n", "Epoch 541: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 835ms/step - loss: 0.1613 - accuracy: 0.9125 - val_loss: 0.2709 - val_accuracy: 0.8305\n", "Epoch 542/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1141 - accuracy: 0.9375\n", "Epoch 542: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1141 - accuracy: 0.9375 - val_loss: 0.2692 - val_accuracy: 0.8305\n", "Epoch 543/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1393 - accuracy: 0.9453\n", "Epoch 543: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1393 - accuracy: 0.9453 - val_loss: 0.2681 - val_accuracy: 0.8305\n", "Epoch 544/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1320 - accuracy: 0.9625\n", "Epoch 544: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1320 - accuracy: 0.9625 - val_loss: 0.2639 - val_accuracy: 0.8305\n", "Epoch 545/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1872 - accuracy: 0.9500\n", "Epoch 545: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1872 - accuracy: 0.9500 - val_loss: 0.2605 - val_accuracy: 0.8475\n", "Epoch 546/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1484 - accuracy: 0.9375\n", "Epoch 546: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 867ms/step - loss: 0.1484 - accuracy: 0.9375 - val_loss: 0.2576 - val_accuracy: 0.8475\n", "Epoch 547/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1332 - accuracy: 0.9250\n", "Epoch 547: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1332 - accuracy: 0.9250 - val_loss: 0.2548 - val_accuracy: 0.8475\n", "Epoch 548/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1152 - accuracy: 0.9375\n", "Epoch 548: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 863ms/step - loss: 0.1152 - accuracy: 0.9375 - val_loss: 0.2531 - val_accuracy: 0.8475\n", "Epoch 549/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1229 - accuracy: 0.9375\n", "Epoch 549: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 816ms/step - loss: 0.1229 - accuracy: 0.9375 - val_loss: 0.2502 - val_accuracy: 0.8475\n", "Epoch 550/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1275 - accuracy: 0.9375\n", "Epoch 550: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 970ms/step - loss: 0.1275 - accuracy: 0.9375 - val_loss: 0.2477 - val_accuracy: 0.8475\n", "Epoch 551/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1139 - accuracy: 0.9609\n", "Epoch 551: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1139 - accuracy: 0.9609 - val_loss: 0.2460 - val_accuracy: 0.8475\n", "Epoch 552/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1195 - accuracy: 0.9625\n", "Epoch 552: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 843ms/step - loss: 0.1195 - accuracy: 0.9625 - val_loss: 0.2457 - val_accuracy: 0.8475\n", "Epoch 553/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1418 - accuracy: 0.9609\n", "Epoch 553: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1418 - accuracy: 0.9609 - val_loss: 0.2463 - val_accuracy: 0.8644\n", "Epoch 554/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1361 - accuracy: 0.9531\n", "Epoch 554: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 928ms/step - loss: 0.1361 - accuracy: 0.9531 - val_loss: 0.2481 - val_accuracy: 0.8644\n", "Epoch 555/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1261 - accuracy: 0.9609\n", "Epoch 555: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1261 - accuracy: 0.9609 - val_loss: 0.2497 - val_accuracy: 0.8644\n", "Epoch 556/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1351 - accuracy: 0.9375\n", "Epoch 556: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1351 - accuracy: 0.9375 - val_loss: 0.2502 - val_accuracy: 0.8644\n", "Epoch 557/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1348 - accuracy: 0.9609\n", "Epoch 557: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 979ms/step - loss: 0.1348 - accuracy: 0.9609 - val_loss: 0.2511 - val_accuracy: 0.8644\n", "Epoch 558/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1423 - accuracy: 0.9453\n", "Epoch 558: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 966ms/step - loss: 0.1423 - accuracy: 0.9453 - val_loss: 0.2523 - val_accuracy: 0.8475\n", "Epoch 559/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1183 - accuracy: 0.9500\n", "Epoch 559: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1183 - accuracy: 0.9500 - val_loss: 0.2542 - val_accuracy: 0.8475\n", "Epoch 560/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1366 - accuracy: 0.9375\n", "Epoch 560: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1366 - accuracy: 0.9375 - val_loss: 0.2565 - val_accuracy: 0.8475\n", "Epoch 561/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1263 - accuracy: 0.9453\n", "Epoch 561: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1263 - accuracy: 0.9453 - val_loss: 0.2591 - val_accuracy: 0.8475\n", "Epoch 562/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1715 - accuracy: 0.9141\n", "Epoch 562: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1715 - accuracy: 0.9141 - val_loss: 0.2615 - val_accuracy: 0.8475\n", "Epoch 563/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1418 - accuracy: 0.9250\n", "Epoch 563: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1418 - accuracy: 0.9250 - val_loss: 0.2651 - val_accuracy: 0.8475\n", "Epoch 564/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1290 - accuracy: 0.9625\n", "Epoch 564: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 811ms/step - loss: 0.1290 - accuracy: 0.9625 - val_loss: 0.2691 - val_accuracy: 0.8305\n", "Epoch 565/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1817 - accuracy: 0.9375\n", "Epoch 565: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1817 - accuracy: 0.9375 - val_loss: 0.2708 - val_accuracy: 0.8305\n", "Epoch 566/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1019 - accuracy: 0.9500\n", "Epoch 566: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1019 - accuracy: 0.9500 - val_loss: 0.2701 - val_accuracy: 0.8305\n", "Epoch 567/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1623 - accuracy: 0.9125\n", "Epoch 567: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1623 - accuracy: 0.9125 - val_loss: 0.2697 - val_accuracy: 0.8305\n", "Epoch 568/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1237 - accuracy: 0.9250\n", "Epoch 568: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 837ms/step - loss: 0.1237 - accuracy: 0.9250 - val_loss: 0.2684 - val_accuracy: 0.8475\n", "Epoch 569/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1747 - accuracy: 0.8984\n", "Epoch 569: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 987ms/step - loss: 0.1747 - accuracy: 0.8984 - val_loss: 0.2667 - val_accuracy: 0.8475\n", "Epoch 570/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1495 - accuracy: 0.9375\n", "Epoch 570: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1495 - accuracy: 0.9375 - val_loss: 0.2644 - val_accuracy: 0.8475\n", "Epoch 571/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1420 - accuracy: 0.9453\n", "Epoch 571: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1420 - accuracy: 0.9453 - val_loss: 0.2626 - val_accuracy: 0.8475\n", "Epoch 572/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1442 - accuracy: 0.9250\n", "Epoch 572: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 863ms/step - loss: 0.1442 - accuracy: 0.9250 - val_loss: 0.2603 - val_accuracy: 0.8475\n", "Epoch 573/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1683 - accuracy: 0.9141\n", "Epoch 573: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 970ms/step - loss: 0.1683 - accuracy: 0.9141 - val_loss: 0.2589 - val_accuracy: 0.8475\n", "Epoch 574/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1001 - accuracy: 0.9875\n", "Epoch 574: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1001 - accuracy: 0.9875 - val_loss: 0.2574 - val_accuracy: 0.8475\n", "Epoch 575/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1083 - accuracy: 0.9766\n", "Epoch 575: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 930ms/step - loss: 0.1083 - accuracy: 0.9766 - val_loss: 0.2565 - val_accuracy: 0.8475\n", "Epoch 576/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1630 - accuracy: 0.9125\n", "Epoch 576: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 993ms/step - loss: 0.1630 - accuracy: 0.9125 - val_loss: 0.2553 - val_accuracy: 0.8305\n", "Epoch 577/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1247 - accuracy: 0.9688\n", "Epoch 577: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 954ms/step - loss: 0.1247 - accuracy: 0.9688 - val_loss: 0.2550 - val_accuracy: 0.8305\n", "Epoch 578/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1639 - accuracy: 0.9297\n", "Epoch 578: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1639 - accuracy: 0.9297 - val_loss: 0.2545 - val_accuracy: 0.8305\n", "Epoch 579/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1569 - accuracy: 0.9500\n", "Epoch 579: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1569 - accuracy: 0.9500 - val_loss: 0.2547 - val_accuracy: 0.8305\n", "Epoch 580/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1216 - accuracy: 0.9531\n", "Epoch 580: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 973ms/step - loss: 0.1216 - accuracy: 0.9531 - val_loss: 0.2551 - val_accuracy: 0.8305\n", "Epoch 581/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1174 - accuracy: 0.9625\n", "Epoch 581: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 823ms/step - loss: 0.1174 - accuracy: 0.9625 - val_loss: 0.2562 - val_accuracy: 0.8305\n", "Epoch 582/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1507 - accuracy: 0.9125\n", "Epoch 582: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 824ms/step - loss: 0.1507 - accuracy: 0.9125 - val_loss: 0.2584 - val_accuracy: 0.8305\n", "Epoch 583/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1742 - accuracy: 0.9125\n", "Epoch 583: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1742 - accuracy: 0.9125 - val_loss: 0.2610 - val_accuracy: 0.8305\n", "Epoch 584/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1347 - accuracy: 0.9500\n", "Epoch 584: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 832ms/step - loss: 0.1347 - accuracy: 0.9500 - val_loss: 0.2647 - val_accuracy: 0.8136\n", "Epoch 585/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1067 - accuracy: 0.9625\n", "Epoch 585: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 813ms/step - loss: 0.1067 - accuracy: 0.9625 - val_loss: 0.2673 - val_accuracy: 0.8136\n", "Epoch 586/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1478 - accuracy: 0.9375\n", "Epoch 586: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1478 - accuracy: 0.9375 - val_loss: 0.2684 - val_accuracy: 0.8136\n", "Epoch 587/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1327 - accuracy: 0.9375\n", "Epoch 587: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1327 - accuracy: 0.9375 - val_loss: 0.2703 - val_accuracy: 0.8136\n", "Epoch 588/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1022 - accuracy: 0.9844\n", "Epoch 588: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 926ms/step - loss: 0.1022 - accuracy: 0.9844 - val_loss: 0.2727 - val_accuracy: 0.8136\n", "Epoch 589/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.2192 - accuracy: 0.9250\n", "Epoch 589: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 815ms/step - loss: 0.2192 - accuracy: 0.9250 - val_loss: 0.2742 - val_accuracy: 0.8136\n", "Epoch 590/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1731 - accuracy: 0.9000\n", "Epoch 590: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1731 - accuracy: 0.9000 - val_loss: 0.2751 - val_accuracy: 0.8136\n", "Epoch 591/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1368 - accuracy: 0.9453\n", "Epoch 591: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1368 - accuracy: 0.9453 - val_loss: 0.2766 - val_accuracy: 0.8136\n", "Epoch 592/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1619 - accuracy: 0.9531\n", "Epoch 592: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1619 - accuracy: 0.9531 - val_loss: 0.2789 - val_accuracy: 0.8136\n", "Epoch 593/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1565 - accuracy: 0.9453\n", "Epoch 593: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1565 - accuracy: 0.9453 - val_loss: 0.2819 - val_accuracy: 0.8136\n", "Epoch 594/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1473 - accuracy: 0.9375\n", "Epoch 594: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1473 - accuracy: 0.9375 - val_loss: 0.2856 - val_accuracy: 0.8136\n", "Epoch 595/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1418 - accuracy: 0.9500\n", "Epoch 595: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 844ms/step - loss: 0.1418 - accuracy: 0.9500 - val_loss: 0.2865 - val_accuracy: 0.8136\n", "Epoch 596/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1448 - accuracy: 0.9375\n", "Epoch 596: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 965ms/step - loss: 0.1448 - accuracy: 0.9375 - val_loss: 0.2876 - val_accuracy: 0.8136\n", "Epoch 597/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1282 - accuracy: 0.9531\n", "Epoch 597: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1282 - accuracy: 0.9531 - val_loss: 0.2887 - val_accuracy: 0.8136\n", "Epoch 598/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1232 - accuracy: 0.9625\n", "Epoch 598: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1232 - accuracy: 0.9625 - val_loss: 0.2871 - val_accuracy: 0.8136\n", "Epoch 599/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1416 - accuracy: 0.9297\n", "Epoch 599: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 940ms/step - loss: 0.1416 - accuracy: 0.9297 - val_loss: 0.2858 - val_accuracy: 0.8136\n", "Epoch 600/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1402 - accuracy: 0.9219\n", "Epoch 600: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1402 - accuracy: 0.9219 - val_loss: 0.2840 - val_accuracy: 0.8136\n", "Epoch 601/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1639 - accuracy: 0.9125\n", "Epoch 601: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 848ms/step - loss: 0.1639 - accuracy: 0.9125 - val_loss: 0.2813 - val_accuracy: 0.8305\n", "Epoch 602/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1876 - accuracy: 0.9250\n", "Epoch 602: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 820ms/step - loss: 0.1876 - accuracy: 0.9250 - val_loss: 0.2773 - val_accuracy: 0.8305\n", "Epoch 603/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1317 - accuracy: 0.9500\n", "Epoch 603: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 826ms/step - loss: 0.1317 - accuracy: 0.9500 - val_loss: 0.2740 - val_accuracy: 0.8136\n", "Epoch 604/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1224 - accuracy: 0.9500\n", "Epoch 604: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1224 - accuracy: 0.9500 - val_loss: 0.2705 - val_accuracy: 0.8136\n", "Epoch 605/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1412 - accuracy: 0.9375\n", "Epoch 605: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1412 - accuracy: 0.9375 - val_loss: 0.2674 - val_accuracy: 0.8136\n", "Epoch 606/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1069 - accuracy: 0.9750\n", "Epoch 606: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1069 - accuracy: 0.9750 - val_loss: 0.2641 - val_accuracy: 0.8305\n", "Epoch 607/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0904 - accuracy: 0.9750\n", "Epoch 607: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 811ms/step - loss: 0.0904 - accuracy: 0.9750 - val_loss: 0.2630 - val_accuracy: 0.8305\n", "Epoch 608/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1305 - accuracy: 0.9375\n", "Epoch 608: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1305 - accuracy: 0.9375 - val_loss: 0.2647 - val_accuracy: 0.8305\n", "Epoch 609/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1477 - accuracy: 0.9375\n", "Epoch 609: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 831ms/step - loss: 0.1477 - accuracy: 0.9375 - val_loss: 0.2663 - val_accuracy: 0.8305\n", "Epoch 610/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0939 - accuracy: 1.0000\n", "Epoch 610: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0939 - accuracy: 1.0000 - val_loss: 0.2680 - val_accuracy: 0.8475\n", "Epoch 611/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0889 - accuracy: 0.9875\n", "Epoch 611: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 845ms/step - loss: 0.0889 - accuracy: 0.9875 - val_loss: 0.2703 - val_accuracy: 0.8305\n", "Epoch 612/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1134 - accuracy: 0.9609\n", "Epoch 612: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1134 - accuracy: 0.9609 - val_loss: 0.2725 - val_accuracy: 0.8305\n", "Epoch 613/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1093 - accuracy: 0.9688\n", "Epoch 613: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 932ms/step - loss: 0.1093 - accuracy: 0.9688 - val_loss: 0.2741 - val_accuracy: 0.8305\n", "Epoch 614/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1112 - accuracy: 0.9688\n", "Epoch 614: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1112 - accuracy: 0.9688 - val_loss: 0.2750 - val_accuracy: 0.8305\n", "Epoch 615/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1013 - accuracy: 1.0000\n", "Epoch 615: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1013 - accuracy: 1.0000 - val_loss: 0.2758 - val_accuracy: 0.8305\n", "Epoch 616/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1483 - accuracy: 0.9141\n", "Epoch 616: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1483 - accuracy: 0.9141 - val_loss: 0.2760 - val_accuracy: 0.8305\n", "Epoch 617/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1175 - accuracy: 0.9625\n", "Epoch 617: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1175 - accuracy: 0.9625 - val_loss: 0.2762 - val_accuracy: 0.8305\n", "Epoch 618/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1037 - accuracy: 0.9688\n", "Epoch 618: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 955ms/step - loss: 0.1037 - accuracy: 0.9688 - val_loss: 0.2767 - val_accuracy: 0.8305\n", "Epoch 619/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1226 - accuracy: 0.9500\n", "Epoch 619: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 820ms/step - loss: 0.1226 - accuracy: 0.9500 - val_loss: 0.2775 - val_accuracy: 0.8305\n", "Epoch 620/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1093 - accuracy: 0.9625\n", "Epoch 620: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 820ms/step - loss: 0.1093 - accuracy: 0.9625 - val_loss: 0.2780 - val_accuracy: 0.8305\n", "Epoch 621/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1217 - accuracy: 0.9453\n", "Epoch 621: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 923ms/step - loss: 0.1217 - accuracy: 0.9453 - val_loss: 0.2780 - val_accuracy: 0.8475\n", "Epoch 622/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1332 - accuracy: 0.9688\n", "Epoch 622: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 958ms/step - loss: 0.1332 - accuracy: 0.9688 - val_loss: 0.2768 - val_accuracy: 0.8475\n", "Epoch 623/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1901 - accuracy: 0.8750\n", "Epoch 623: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 874ms/step - loss: 0.1901 - accuracy: 0.8750 - val_loss: 0.2755 - val_accuracy: 0.8475\n", "Epoch 624/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1137 - accuracy: 0.9531\n", "Epoch 624: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 931ms/step - loss: 0.1137 - accuracy: 0.9531 - val_loss: 0.2747 - val_accuracy: 0.8475\n", "Epoch 625/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1145 - accuracy: 0.9453\n", "Epoch 625: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 982ms/step - loss: 0.1145 - accuracy: 0.9453 - val_loss: 0.2742 - val_accuracy: 0.8475\n", "Epoch 626/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1495 - accuracy: 0.9453\n", "Epoch 626: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 985ms/step - loss: 0.1495 - accuracy: 0.9453 - val_loss: 0.2736 - val_accuracy: 0.8475\n", "Epoch 627/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0794 - accuracy: 0.9875\n", "Epoch 627: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 817ms/step - loss: 0.0794 - accuracy: 0.9875 - val_loss: 0.2719 - val_accuracy: 0.8475\n", "Epoch 628/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1697 - accuracy: 0.9141\n", "Epoch 628: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 968ms/step - loss: 0.1697 - accuracy: 0.9141 - val_loss: 0.2718 - val_accuracy: 0.8475\n", "Epoch 629/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1177 - accuracy: 0.9297\n", "Epoch 629: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1177 - accuracy: 0.9297 - val_loss: 0.2714 - val_accuracy: 0.8475\n", "Epoch 630/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1289 - accuracy: 0.9453\n", "Epoch 630: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 930ms/step - loss: 0.1289 - accuracy: 0.9453 - val_loss: 0.2695 - val_accuracy: 0.8475\n", "Epoch 631/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1265 - accuracy: 0.9625\n", "Epoch 631: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1265 - accuracy: 0.9625 - val_loss: 0.2698 - val_accuracy: 0.8475\n", "Epoch 632/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1210 - accuracy: 0.9375\n", "Epoch 632: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1210 - accuracy: 0.9375 - val_loss: 0.2694 - val_accuracy: 0.8475\n", "Epoch 633/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1212 - accuracy: 0.9531\n", "Epoch 633: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 910ms/step - loss: 0.1212 - accuracy: 0.9531 - val_loss: 0.2685 - val_accuracy: 0.8475\n", "Epoch 634/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0945 - accuracy: 0.9625\n", "Epoch 634: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 828ms/step - loss: 0.0945 - accuracy: 0.9625 - val_loss: 0.2682 - val_accuracy: 0.8475\n", "Epoch 635/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1332 - accuracy: 0.9453\n", "Epoch 635: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1332 - accuracy: 0.9453 - val_loss: 0.2689 - val_accuracy: 0.8305\n", "Epoch 636/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1162 - accuracy: 0.9297\n", "Epoch 636: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1162 - accuracy: 0.9297 - val_loss: 0.2700 - val_accuracy: 0.8305\n", "Epoch 637/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1188 - accuracy: 0.9453\n", "Epoch 637: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 944ms/step - loss: 0.1188 - accuracy: 0.9453 - val_loss: 0.2703 - val_accuracy: 0.8305\n", "Epoch 638/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1679 - accuracy: 0.9125\n", "Epoch 638: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 835ms/step - loss: 0.1679 - accuracy: 0.9125 - val_loss: 0.2692 - val_accuracy: 0.8305\n", "Epoch 639/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0977 - accuracy: 0.9625\n", "Epoch 639: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 837ms/step - loss: 0.0977 - accuracy: 0.9625 - val_loss: 0.2677 - val_accuracy: 0.8305\n", "Epoch 640/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0780 - accuracy: 0.9844\n", "Epoch 640: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 934ms/step - loss: 0.0780 - accuracy: 0.9844 - val_loss: 0.2665 - val_accuracy: 0.8305\n", "Epoch 641/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0954 - accuracy: 0.9625\n", "Epoch 641: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 809ms/step - loss: 0.0954 - accuracy: 0.9625 - val_loss: 0.2658 - val_accuracy: 0.8305\n", "Epoch 642/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1260 - accuracy: 0.9531\n", "Epoch 642: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1260 - accuracy: 0.9531 - val_loss: 0.2659 - val_accuracy: 0.8305\n", "Epoch 643/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1252 - accuracy: 0.9453\n", "Epoch 643: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1252 - accuracy: 0.9453 - val_loss: 0.2662 - val_accuracy: 0.8305\n", "Epoch 644/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1139 - accuracy: 0.9625\n", "Epoch 644: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 820ms/step - loss: 0.1139 - accuracy: 0.9625 - val_loss: 0.2659 - val_accuracy: 0.8475\n", "Epoch 645/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1121 - accuracy: 0.9531\n", "Epoch 645: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1121 - accuracy: 0.9531 - val_loss: 0.2654 - val_accuracy: 0.8475\n", "Epoch 646/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1068 - accuracy: 0.9688\n", "Epoch 646: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1068 - accuracy: 0.9688 - val_loss: 0.2652 - val_accuracy: 0.8475\n", "Epoch 647/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1136 - accuracy: 0.9625\n", "Epoch 647: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1136 - accuracy: 0.9625 - val_loss: 0.2650 - val_accuracy: 0.8475\n", "Epoch 648/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1084 - accuracy: 0.9688\n", "Epoch 648: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1084 - accuracy: 0.9688 - val_loss: 0.2641 - val_accuracy: 0.8475\n", "Epoch 649/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1123 - accuracy: 0.9531\n", "Epoch 649: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 999ms/step - loss: 0.1123 - accuracy: 0.9531 - val_loss: 0.2637 - val_accuracy: 0.8475\n", "Epoch 650/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1562 - accuracy: 0.9375\n", "Epoch 650: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1562 - accuracy: 0.9375 - val_loss: 0.2633 - val_accuracy: 0.8475\n", "Epoch 651/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1610 - accuracy: 0.9375\n", "Epoch 651: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 804ms/step - loss: 0.1610 - accuracy: 0.9375 - val_loss: 0.2635 - val_accuracy: 0.8475\n", "Epoch 652/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1656 - accuracy: 0.9141\n", "Epoch 652: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1656 - accuracy: 0.9141 - val_loss: 0.2640 - val_accuracy: 0.8475\n", "Epoch 653/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1222 - accuracy: 0.9500\n", "Epoch 653: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 822ms/step - loss: 0.1222 - accuracy: 0.9500 - val_loss: 0.2651 - val_accuracy: 0.8475\n", "Epoch 654/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1006 - accuracy: 0.9766\n", "Epoch 654: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1006 - accuracy: 0.9766 - val_loss: 0.2669 - val_accuracy: 0.8475\n", "Epoch 655/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1395 - accuracy: 0.9250\n", "Epoch 655: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 826ms/step - loss: 0.1395 - accuracy: 0.9250 - val_loss: 0.2695 - val_accuracy: 0.8475\n", "Epoch 656/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1042 - accuracy: 0.9766\n", "Epoch 656: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1042 - accuracy: 0.9766 - val_loss: 0.2724 - val_accuracy: 0.8475\n", "Epoch 657/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1471 - accuracy: 0.9125\n", "Epoch 657: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1471 - accuracy: 0.9125 - val_loss: 0.2752 - val_accuracy: 0.8475\n", "Epoch 658/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1069 - accuracy: 0.9531\n", "Epoch 658: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 935ms/step - loss: 0.1069 - accuracy: 0.9531 - val_loss: 0.2782 - val_accuracy: 0.8475\n", "Epoch 659/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0970 - accuracy: 0.9766\n", "Epoch 659: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0970 - accuracy: 0.9766 - val_loss: 0.2803 - val_accuracy: 0.8475\n", "Epoch 660/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1135 - accuracy: 0.9609\n", "Epoch 660: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1135 - accuracy: 0.9609 - val_loss: 0.2815 - val_accuracy: 0.8305\n", "Epoch 661/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0622 - accuracy: 0.9875\n", "Epoch 661: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 801ms/step - loss: 0.0622 - accuracy: 0.9875 - val_loss: 0.2827 - val_accuracy: 0.8305\n", "Epoch 662/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1074 - accuracy: 0.9625\n", "Epoch 662: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 812ms/step - loss: 0.1074 - accuracy: 0.9625 - val_loss: 0.2826 - val_accuracy: 0.8305\n", "Epoch 663/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1000 - accuracy: 0.9844\n", "Epoch 663: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1000 - accuracy: 0.9844 - val_loss: 0.2818 - val_accuracy: 0.8475\n", "Epoch 664/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0919 - accuracy: 0.9500\n", "Epoch 664: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 840ms/step - loss: 0.0919 - accuracy: 0.9500 - val_loss: 0.2819 - val_accuracy: 0.8475\n", "Epoch 665/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1268 - accuracy: 0.9375\n", "Epoch 665: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1268 - accuracy: 0.9375 - val_loss: 0.2829 - val_accuracy: 0.8475\n", "Epoch 666/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1491 - accuracy: 0.9250\n", "Epoch 666: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1491 - accuracy: 0.9250 - val_loss: 0.2811 - val_accuracy: 0.8475\n", "Epoch 667/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1190 - accuracy: 0.9500\n", "Epoch 667: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 820ms/step - loss: 0.1190 - accuracy: 0.9500 - val_loss: 0.2784 - val_accuracy: 0.8475\n", "Epoch 668/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0955 - accuracy: 0.9688\n", "Epoch 668: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0955 - accuracy: 0.9688 - val_loss: 0.2763 - val_accuracy: 0.8475\n", "Epoch 669/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1251 - accuracy: 0.9531\n", "Epoch 669: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1251 - accuracy: 0.9531 - val_loss: 0.2759 - val_accuracy: 0.8475\n", "Epoch 670/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1130 - accuracy: 0.9500\n", "Epoch 670: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 821ms/step - loss: 0.1130 - accuracy: 0.9500 - val_loss: 0.2762 - val_accuracy: 0.8475\n", "Epoch 671/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1206 - accuracy: 0.9375\n", "Epoch 671: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1206 - accuracy: 0.9375 - val_loss: 0.2766 - val_accuracy: 0.8305\n", "Epoch 672/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1287 - accuracy: 0.9453\n", "Epoch 672: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1287 - accuracy: 0.9453 - val_loss: 0.2768 - val_accuracy: 0.8305\n", "Epoch 673/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1517 - accuracy: 0.9250\n", "Epoch 673: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 818ms/step - loss: 0.1517 - accuracy: 0.9250 - val_loss: 0.2769 - val_accuracy: 0.8305\n", "Epoch 674/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1057 - accuracy: 0.9609\n", "Epoch 674: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1057 - accuracy: 0.9609 - val_loss: 0.2767 - val_accuracy: 0.8305\n", "Epoch 675/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1428 - accuracy: 0.9375\n", "Epoch 675: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 834ms/step - loss: 0.1428 - accuracy: 0.9375 - val_loss: 0.2772 - val_accuracy: 0.8305\n", "Epoch 676/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1095 - accuracy: 0.9625\n", "Epoch 676: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1095 - accuracy: 0.9625 - val_loss: 0.2795 - val_accuracy: 0.8305\n", "Epoch 677/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1420 - accuracy: 0.9375\n", "Epoch 677: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1420 - accuracy: 0.9375 - val_loss: 0.2809 - val_accuracy: 0.8305\n", "Epoch 678/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1261 - accuracy: 0.9141\n", "Epoch 678: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1261 - accuracy: 0.9141 - val_loss: 0.2811 - val_accuracy: 0.8305\n", "Epoch 679/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1210 - accuracy: 0.9625\n", "Epoch 679: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 808ms/step - loss: 0.1210 - accuracy: 0.9625 - val_loss: 0.2805 - val_accuracy: 0.8305\n", "Epoch 680/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1199 - accuracy: 0.9250\n", "Epoch 680: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 826ms/step - loss: 0.1199 - accuracy: 0.9250 - val_loss: 0.2789 - val_accuracy: 0.8305\n", "Epoch 681/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1262 - accuracy: 0.9688\n", "Epoch 681: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 938ms/step - loss: 0.1262 - accuracy: 0.9688 - val_loss: 0.2781 - val_accuracy: 0.8305\n", "Epoch 682/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1391 - accuracy: 0.9219\n", "Epoch 682: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1391 - accuracy: 0.9219 - val_loss: 0.2770 - val_accuracy: 0.8305\n", "Epoch 683/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0833 - accuracy: 0.9875\n", "Epoch 683: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0833 - accuracy: 0.9875 - val_loss: 0.2774 - val_accuracy: 0.8305\n", "Epoch 684/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1212 - accuracy: 0.9375\n", "Epoch 684: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 999ms/step - loss: 0.1212 - accuracy: 0.9375 - val_loss: 0.2778 - val_accuracy: 0.8305\n", "Epoch 685/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1233 - accuracy: 0.9531\n", "Epoch 685: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1233 - accuracy: 0.9531 - val_loss: 0.2769 - val_accuracy: 0.8305\n", "Epoch 686/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1080 - accuracy: 0.9609\n", "Epoch 686: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1080 - accuracy: 0.9609 - val_loss: 0.2748 - val_accuracy: 0.8305\n", "Epoch 687/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1526 - accuracy: 0.9125\n", "Epoch 687: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1526 - accuracy: 0.9125 - val_loss: 0.2761 - val_accuracy: 0.8305\n", "Epoch 688/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1283 - accuracy: 0.9375\n", "Epoch 688: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1283 - accuracy: 0.9375 - val_loss: 0.2777 - val_accuracy: 0.8305\n", "Epoch 689/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1500 - accuracy: 0.9375\n", "Epoch 689: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 831ms/step - loss: 0.1500 - accuracy: 0.9375 - val_loss: 0.2809 - val_accuracy: 0.8305\n", "Epoch 690/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1213 - accuracy: 0.9375\n", "Epoch 690: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1213 - accuracy: 0.9375 - val_loss: 0.2837 - val_accuracy: 0.8305\n", "Epoch 691/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1150 - accuracy: 0.9531\n", "Epoch 691: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1150 - accuracy: 0.9531 - val_loss: 0.2858 - val_accuracy: 0.8305\n", "Epoch 692/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0847 - accuracy: 0.9766\n", "Epoch 692: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0847 - accuracy: 0.9766 - val_loss: 0.2873 - val_accuracy: 0.8305\n", "Epoch 693/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1106 - accuracy: 0.9625\n", "Epoch 693: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1106 - accuracy: 0.9625 - val_loss: 0.2868 - val_accuracy: 0.8305\n", "Epoch 694/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1030 - accuracy: 0.9750\n", "Epoch 694: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 833ms/step - loss: 0.1030 - accuracy: 0.9750 - val_loss: 0.2863 - val_accuracy: 0.8305\n", "Epoch 695/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1061 - accuracy: 0.9531\n", "Epoch 695: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 955ms/step - loss: 0.1061 - accuracy: 0.9531 - val_loss: 0.2856 - val_accuracy: 0.8305\n", "Epoch 696/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1274 - accuracy: 0.9297\n", "Epoch 696: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1274 - accuracy: 0.9297 - val_loss: 0.2846 - val_accuracy: 0.8305\n", "Epoch 697/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1182 - accuracy: 0.9531\n", "Epoch 697: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1182 - accuracy: 0.9531 - val_loss: 0.2838 - val_accuracy: 0.8305\n", "Epoch 698/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1083 - accuracy: 0.9453\n", "Epoch 698: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1083 - accuracy: 0.9453 - val_loss: 0.2828 - val_accuracy: 0.8305\n", "Epoch 699/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1175 - accuracy: 0.9531\n", "Epoch 699: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1175 - accuracy: 0.9531 - val_loss: 0.2830 - val_accuracy: 0.8305\n", "Epoch 700/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1411 - accuracy: 0.9297\n", "Epoch 700: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 957ms/step - loss: 0.1411 - accuracy: 0.9297 - val_loss: 0.2833 - val_accuracy: 0.8305\n", "Epoch 701/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1243 - accuracy: 0.9453\n", "Epoch 701: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1243 - accuracy: 0.9453 - val_loss: 0.2845 - val_accuracy: 0.8305\n", "Epoch 702/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1150 - accuracy: 0.9500\n", "Epoch 702: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 861ms/step - loss: 0.1150 - accuracy: 0.9500 - val_loss: 0.2868 - val_accuracy: 0.8305\n", "Epoch 703/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1140 - accuracy: 0.9250\n", "Epoch 703: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1140 - accuracy: 0.9250 - val_loss: 0.2885 - val_accuracy: 0.8305\n", "Epoch 704/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1070 - accuracy: 0.9531\n", "Epoch 704: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 934ms/step - loss: 0.1070 - accuracy: 0.9531 - val_loss: 0.2881 - val_accuracy: 0.8305\n", "Epoch 705/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1123 - accuracy: 0.9625\n", "Epoch 705: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1123 - accuracy: 0.9625 - val_loss: 0.2871 - val_accuracy: 0.8305\n", "Epoch 706/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1124 - accuracy: 0.9453\n", "Epoch 706: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1124 - accuracy: 0.9453 - val_loss: 0.2852 - val_accuracy: 0.8305\n", "Epoch 707/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0818 - accuracy: 0.9531\n", "Epoch 707: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0818 - accuracy: 0.9531 - val_loss: 0.2834 - val_accuracy: 0.8305\n", "Epoch 708/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0923 - accuracy: 1.0000\n", "Epoch 708: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 846ms/step - loss: 0.0923 - accuracy: 1.0000 - val_loss: 0.2816 - val_accuracy: 0.8305\n", "Epoch 709/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1267 - accuracy: 0.9297\n", "Epoch 709: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1267 - accuracy: 0.9297 - val_loss: 0.2808 - val_accuracy: 0.8305\n", "Epoch 710/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1103 - accuracy: 0.9500\n", "Epoch 710: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1103 - accuracy: 0.9500 - val_loss: 0.2803 - val_accuracy: 0.8305\n", "Epoch 711/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1186 - accuracy: 0.9453\n", "Epoch 711: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 957ms/step - loss: 0.1186 - accuracy: 0.9453 - val_loss: 0.2794 - val_accuracy: 0.8305\n", "Epoch 712/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1164 - accuracy: 0.9500\n", "Epoch 712: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 888ms/step - loss: 0.1164 - accuracy: 0.9500 - val_loss: 0.2793 - val_accuracy: 0.8305\n", "Epoch 713/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1329 - accuracy: 0.9453\n", "Epoch 713: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 920ms/step - loss: 0.1329 - accuracy: 0.9453 - val_loss: 0.2797 - val_accuracy: 0.8305\n", "Epoch 714/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1029 - accuracy: 0.9453\n", "Epoch 714: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1029 - accuracy: 0.9453 - val_loss: 0.2799 - val_accuracy: 0.8305\n", "Epoch 715/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0814 - accuracy: 0.9750\n", "Epoch 715: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0814 - accuracy: 0.9750 - val_loss: 0.2799 - val_accuracy: 0.8305\n", "Epoch 716/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1071 - accuracy: 0.9609\n", "Epoch 716: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 936ms/step - loss: 0.1071 - accuracy: 0.9609 - val_loss: 0.2795 - val_accuracy: 0.8475\n", "Epoch 717/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0719 - accuracy: 1.0000\n", "Epoch 717: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0719 - accuracy: 1.0000 - val_loss: 0.2809 - val_accuracy: 0.8305\n", "Epoch 718/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1597 - accuracy: 0.9375\n", "Epoch 718: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 821ms/step - loss: 0.1597 - accuracy: 0.9375 - val_loss: 0.2791 - val_accuracy: 0.8475\n", "Epoch 719/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1307 - accuracy: 0.9750\n", "Epoch 719: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 834ms/step - loss: 0.1307 - accuracy: 0.9750 - val_loss: 0.2759 - val_accuracy: 0.8475\n", "Epoch 720/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0994 - accuracy: 0.9922\n", "Epoch 720: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0994 - accuracy: 0.9922 - val_loss: 0.2731 - val_accuracy: 0.8475\n", "Epoch 721/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1031 - accuracy: 0.9750\n", "Epoch 721: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 859ms/step - loss: 0.1031 - accuracy: 0.9750 - val_loss: 0.2718 - val_accuracy: 0.8475\n", "Epoch 722/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1109 - accuracy: 0.9375\n", "Epoch 722: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 832ms/step - loss: 0.1109 - accuracy: 0.9375 - val_loss: 0.2699 - val_accuracy: 0.8475\n", "Epoch 723/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0936 - accuracy: 0.9500\n", "Epoch 723: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0936 - accuracy: 0.9500 - val_loss: 0.2673 - val_accuracy: 0.8475\n", "Epoch 724/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1319 - accuracy: 0.9500\n", "Epoch 724: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1319 - accuracy: 0.9500 - val_loss: 0.2645 - val_accuracy: 0.8475\n", "Epoch 725/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1114 - accuracy: 0.9375\n", "Epoch 725: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1114 - accuracy: 0.9375 - val_loss: 0.2619 - val_accuracy: 0.8475\n", "Epoch 726/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0872 - accuracy: 0.9875\n", "Epoch 726: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 805ms/step - loss: 0.0872 - accuracy: 0.9875 - val_loss: 0.2602 - val_accuracy: 0.8475\n", "Epoch 727/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1199 - accuracy: 0.9609\n", "Epoch 727: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1199 - accuracy: 0.9609 - val_loss: 0.2602 - val_accuracy: 0.8475\n", "Epoch 728/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1012 - accuracy: 0.9609\n", "Epoch 728: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 926ms/step - loss: 0.1012 - accuracy: 0.9609 - val_loss: 0.2608 - val_accuracy: 0.8475\n", "Epoch 729/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0955 - accuracy: 0.9750\n", "Epoch 729: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0955 - accuracy: 0.9750 - val_loss: 0.2607 - val_accuracy: 0.8475\n", "Epoch 730/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1248 - accuracy: 0.9297\n", "Epoch 730: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 970ms/step - loss: 0.1248 - accuracy: 0.9297 - val_loss: 0.2611 - val_accuracy: 0.8475\n", "Epoch 731/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1311 - accuracy: 0.9219\n", "Epoch 731: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1311 - accuracy: 0.9219 - val_loss: 0.2610 - val_accuracy: 0.8475\n", "Epoch 732/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1236 - accuracy: 0.9375\n", "Epoch 732: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 812ms/step - loss: 0.1236 - accuracy: 0.9375 - val_loss: 0.2621 - val_accuracy: 0.8305\n", "Epoch 733/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1027 - accuracy: 0.9609\n", "Epoch 733: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 959ms/step - loss: 0.1027 - accuracy: 0.9609 - val_loss: 0.2639 - val_accuracy: 0.8305\n", "Epoch 734/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1354 - accuracy: 0.9453\n", "Epoch 734: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1354 - accuracy: 0.9453 - val_loss: 0.2655 - val_accuracy: 0.8305\n", "Epoch 735/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1007 - accuracy: 0.9531\n", "Epoch 735: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 940ms/step - loss: 0.1007 - accuracy: 0.9531 - val_loss: 0.2681 - val_accuracy: 0.8305\n", "Epoch 736/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1023 - accuracy: 0.9609\n", "Epoch 736: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1023 - accuracy: 0.9609 - val_loss: 0.2705 - val_accuracy: 0.8305\n", "Epoch 737/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0855 - accuracy: 0.9688\n", "Epoch 737: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 901ms/step - loss: 0.0855 - accuracy: 0.9688 - val_loss: 0.2720 - val_accuracy: 0.8305\n", "Epoch 738/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1273 - accuracy: 0.9000\n", "Epoch 738: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 838ms/step - loss: 0.1273 - accuracy: 0.9000 - val_loss: 0.2730 - val_accuracy: 0.8305\n", "Epoch 739/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1079 - accuracy: 0.9250\n", "Epoch 739: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1079 - accuracy: 0.9250 - val_loss: 0.2744 - val_accuracy: 0.8305\n", "Epoch 740/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0813 - accuracy: 0.9922\n", "Epoch 740: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0813 - accuracy: 0.9922 - val_loss: 0.2757 - val_accuracy: 0.8305\n", "Epoch 741/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1141 - accuracy: 0.9500\n", "Epoch 741: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 839ms/step - loss: 0.1141 - accuracy: 0.9500 - val_loss: 0.2759 - val_accuracy: 0.8305\n", "Epoch 742/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0984 - accuracy: 0.9844\n", "Epoch 742: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 951ms/step - loss: 0.0984 - accuracy: 0.9844 - val_loss: 0.2755 - val_accuracy: 0.8305\n", "Epoch 743/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0862 - accuracy: 0.9609\n", "Epoch 743: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0862 - accuracy: 0.9609 - val_loss: 0.2756 - val_accuracy: 0.8305\n", "Epoch 744/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1266 - accuracy: 0.9453\n", "Epoch 744: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 954ms/step - loss: 0.1266 - accuracy: 0.9453 - val_loss: 0.2753 - val_accuracy: 0.8305\n", "Epoch 745/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0972 - accuracy: 0.9625\n", "Epoch 745: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 838ms/step - loss: 0.0972 - accuracy: 0.9625 - val_loss: 0.2741 - val_accuracy: 0.8305\n", "Epoch 746/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1272 - accuracy: 0.9375\n", "Epoch 746: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1272 - accuracy: 0.9375 - val_loss: 0.2730 - val_accuracy: 0.8305\n", "Epoch 747/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1130 - accuracy: 0.9250\n", "Epoch 747: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 850ms/step - loss: 0.1130 - accuracy: 0.9250 - val_loss: 0.2731 - val_accuracy: 0.8305\n", "Epoch 748/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1005 - accuracy: 0.9609\n", "Epoch 748: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1005 - accuracy: 0.9609 - val_loss: 0.2731 - val_accuracy: 0.8305\n", "Epoch 749/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1331 - accuracy: 0.9219\n", "Epoch 749: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1331 - accuracy: 0.9219 - val_loss: 0.2735 - val_accuracy: 0.8305\n", "Epoch 750/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0987 - accuracy: 0.9531\n", "Epoch 750: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 948ms/step - loss: 0.0987 - accuracy: 0.9531 - val_loss: 0.2732 - val_accuracy: 0.8305\n", "Epoch 751/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1306 - accuracy: 0.9625\n", "Epoch 751: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1306 - accuracy: 0.9625 - val_loss: 0.2735 - val_accuracy: 0.8305\n", "Epoch 752/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1052 - accuracy: 0.9609\n", "Epoch 752: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1052 - accuracy: 0.9609 - val_loss: 0.2742 - val_accuracy: 0.8305\n", "Epoch 753/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1138 - accuracy: 0.9531\n", "Epoch 753: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1138 - accuracy: 0.9531 - val_loss: 0.2751 - val_accuracy: 0.8305\n", "Epoch 754/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0997 - accuracy: 0.9688\n", "Epoch 754: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0997 - accuracy: 0.9688 - val_loss: 0.2757 - val_accuracy: 0.8305\n", "Epoch 755/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0910 - accuracy: 0.9766\n", "Epoch 755: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 964ms/step - loss: 0.0910 - accuracy: 0.9766 - val_loss: 0.2760 - val_accuracy: 0.8305\n", "Epoch 756/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0916 - accuracy: 0.9531\n", "Epoch 756: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0916 - accuracy: 0.9531 - val_loss: 0.2756 - val_accuracy: 0.8305\n", "Epoch 757/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0892 - accuracy: 0.9688\n", "Epoch 757: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0892 - accuracy: 0.9688 - val_loss: 0.2744 - val_accuracy: 0.8305\n", "Epoch 758/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1605 - accuracy: 0.9125\n", "Epoch 758: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1605 - accuracy: 0.9125 - val_loss: 0.2720 - val_accuracy: 0.8475\n", "Epoch 759/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1353 - accuracy: 0.9375\n", "Epoch 759: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1353 - accuracy: 0.9375 - val_loss: 0.2697 - val_accuracy: 0.8475\n", "Epoch 760/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0941 - accuracy: 0.9875\n", "Epoch 760: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0941 - accuracy: 0.9875 - val_loss: 0.2682 - val_accuracy: 0.8475\n", "Epoch 761/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0846 - accuracy: 0.9922\n", "Epoch 761: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0846 - accuracy: 0.9922 - val_loss: 0.2674 - val_accuracy: 0.8475\n", "Epoch 762/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0976 - accuracy: 0.9609\n", "Epoch 762: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0976 - accuracy: 0.9609 - val_loss: 0.2673 - val_accuracy: 0.8475\n", "Epoch 763/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0895 - accuracy: 0.9500\n", "Epoch 763: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0895 - accuracy: 0.9500 - val_loss: 0.2657 - val_accuracy: 0.8475\n", "Epoch 764/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0793 - accuracy: 0.9766\n", "Epoch 764: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 985ms/step - loss: 0.0793 - accuracy: 0.9766 - val_loss: 0.2641 - val_accuracy: 0.8475\n", "Epoch 765/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0875 - accuracy: 0.9688\n", "Epoch 765: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 964ms/step - loss: 0.0875 - accuracy: 0.9688 - val_loss: 0.2638 - val_accuracy: 0.8475\n", "Epoch 766/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1283 - accuracy: 0.9500\n", "Epoch 766: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1283 - accuracy: 0.9500 - val_loss: 0.2612 - val_accuracy: 0.8475\n", "Epoch 767/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1182 - accuracy: 0.9375\n", "Epoch 767: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1182 - accuracy: 0.9375 - val_loss: 0.2574 - val_accuracy: 0.8475\n", "Epoch 768/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0919 - accuracy: 0.9453\n", "Epoch 768: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0919 - accuracy: 0.9453 - val_loss: 0.2547 - val_accuracy: 0.8475\n", "Epoch 769/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1081 - accuracy: 0.9750\n", "Epoch 769: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 847ms/step - loss: 0.1081 - accuracy: 0.9750 - val_loss: 0.2529 - val_accuracy: 0.8475\n", "Epoch 770/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0646 - accuracy: 1.0000\n", "Epoch 770: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 947ms/step - loss: 0.0646 - accuracy: 1.0000 - val_loss: 0.2518 - val_accuracy: 0.8475\n", "Epoch 771/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1405 - accuracy: 0.9500\n", "Epoch 771: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 851ms/step - loss: 0.1405 - accuracy: 0.9500 - val_loss: 0.2505 - val_accuracy: 0.8475\n", "Epoch 772/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1141 - accuracy: 0.9531\n", "Epoch 772: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 953ms/step - loss: 0.1141 - accuracy: 0.9531 - val_loss: 0.2495 - val_accuracy: 0.8475\n", "Epoch 773/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0894 - accuracy: 0.9844\n", "Epoch 773: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 964ms/step - loss: 0.0894 - accuracy: 0.9844 - val_loss: 0.2490 - val_accuracy: 0.8475\n", "Epoch 774/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1010 - accuracy: 0.9875\n", "Epoch 774: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1010 - accuracy: 0.9875 - val_loss: 0.2502 - val_accuracy: 0.8475\n", "Epoch 775/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1218 - accuracy: 0.9500\n", "Epoch 775: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1218 - accuracy: 0.9500 - val_loss: 0.2521 - val_accuracy: 0.8475\n", "Epoch 776/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0885 - accuracy: 0.9750\n", "Epoch 776: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 825ms/step - loss: 0.0885 - accuracy: 0.9750 - val_loss: 0.2556 - val_accuracy: 0.8475\n", "Epoch 777/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1032 - accuracy: 0.9750\n", "Epoch 777: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1032 - accuracy: 0.9750 - val_loss: 0.2587 - val_accuracy: 0.8475\n", "Epoch 778/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1003 - accuracy: 0.9453\n", "Epoch 778: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 946ms/step - loss: 0.1003 - accuracy: 0.9453 - val_loss: 0.2619 - val_accuracy: 0.8475\n", "Epoch 779/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0924 - accuracy: 0.9500\n", "Epoch 779: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 830ms/step - loss: 0.0924 - accuracy: 0.9500 - val_loss: 0.2652 - val_accuracy: 0.8475\n", "Epoch 780/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1120 - accuracy: 0.9688\n", "Epoch 780: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1120 - accuracy: 0.9688 - val_loss: 0.2678 - val_accuracy: 0.8475\n", "Epoch 781/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1270 - accuracy: 0.9531\n", "Epoch 781: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 962ms/step - loss: 0.1270 - accuracy: 0.9531 - val_loss: 0.2701 - val_accuracy: 0.8475\n", "Epoch 782/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0972 - accuracy: 0.9531\n", "Epoch 782: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 953ms/step - loss: 0.0972 - accuracy: 0.9531 - val_loss: 0.2720 - val_accuracy: 0.8475\n", "Epoch 783/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1113 - accuracy: 0.9688\n", "Epoch 783: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1113 - accuracy: 0.9688 - val_loss: 0.2752 - val_accuracy: 0.8305\n", "Epoch 784/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0787 - accuracy: 0.9500\n", "Epoch 784: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 819ms/step - loss: 0.0787 - accuracy: 0.9500 - val_loss: 0.2774 - val_accuracy: 0.8305\n", "Epoch 785/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1063 - accuracy: 0.9875\n", "Epoch 785: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 816ms/step - loss: 0.1063 - accuracy: 0.9875 - val_loss: 0.2791 - val_accuracy: 0.8305\n", "Epoch 786/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0988 - accuracy: 0.9688\n", "Epoch 786: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0988 - accuracy: 0.9688 - val_loss: 0.2820 - val_accuracy: 0.8305\n", "Epoch 787/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1266 - accuracy: 0.9250\n", "Epoch 787: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1266 - accuracy: 0.9250 - val_loss: 0.2833 - val_accuracy: 0.8136\n", "Epoch 788/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1121 - accuracy: 0.9688\n", "Epoch 788: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1121 - accuracy: 0.9688 - val_loss: 0.2839 - val_accuracy: 0.8136\n", "Epoch 789/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1159 - accuracy: 0.9375\n", "Epoch 789: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1159 - accuracy: 0.9375 - val_loss: 0.2841 - val_accuracy: 0.8136\n", "Epoch 790/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1131 - accuracy: 0.9625\n", "Epoch 790: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 853ms/step - loss: 0.1131 - accuracy: 0.9625 - val_loss: 0.2837 - val_accuracy: 0.8475\n", "Epoch 791/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0619 - accuracy: 1.0000\n", "Epoch 791: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0619 - accuracy: 1.0000 - val_loss: 0.2837 - val_accuracy: 0.8475\n", "Epoch 792/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0737 - accuracy: 1.0000\n", "Epoch 792: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0737 - accuracy: 1.0000 - val_loss: 0.2861 - val_accuracy: 0.8475\n", "Epoch 793/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1128 - accuracy: 0.9750\n", "Epoch 793: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1128 - accuracy: 0.9750 - val_loss: 0.2885 - val_accuracy: 0.8305\n", "Epoch 794/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0624 - accuracy: 1.0000\n", "Epoch 794: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0624 - accuracy: 1.0000 - val_loss: 0.2914 - val_accuracy: 0.8305\n", "Epoch 795/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0935 - accuracy: 0.9609\n", "Epoch 795: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0935 - accuracy: 0.9609 - val_loss: 0.2928 - val_accuracy: 0.8305\n", "Epoch 796/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0912 - accuracy: 0.9625\n", "Epoch 796: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 881ms/step - loss: 0.0912 - accuracy: 0.9625 - val_loss: 0.2941 - val_accuracy: 0.8305\n", "Epoch 797/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0922 - accuracy: 0.9766\n", "Epoch 797: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0922 - accuracy: 0.9766 - val_loss: 0.2936 - val_accuracy: 0.8475\n", "Epoch 798/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1466 - accuracy: 0.9375\n", "Epoch 798: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1466 - accuracy: 0.9375 - val_loss: 0.2921 - val_accuracy: 0.8475\n", "Epoch 799/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0982 - accuracy: 0.9453\n", "Epoch 799: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0982 - accuracy: 0.9453 - val_loss: 0.2880 - val_accuracy: 0.8475\n", "Epoch 800/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0642 - accuracy: 1.0000\n", "Epoch 800: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 980ms/step - loss: 0.0642 - accuracy: 1.0000 - val_loss: 0.2839 - val_accuracy: 0.8644\n", "Epoch 801/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1012 - accuracy: 0.9875\n", "Epoch 801: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1012 - accuracy: 0.9875 - val_loss: 0.2809 - val_accuracy: 0.8644\n", "Epoch 802/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0896 - accuracy: 0.9750\n", "Epoch 802: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 842ms/step - loss: 0.0896 - accuracy: 0.9750 - val_loss: 0.2776 - val_accuracy: 0.8644\n", "Epoch 803/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1111 - accuracy: 0.9750\n", "Epoch 803: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 905ms/step - loss: 0.1111 - accuracy: 0.9750 - val_loss: 0.2753 - val_accuracy: 0.8644\n", "Epoch 804/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1032 - accuracy: 0.9688\n", "Epoch 804: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 959ms/step - loss: 0.1032 - accuracy: 0.9688 - val_loss: 0.2732 - val_accuracy: 0.8644\n", "Epoch 805/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1012 - accuracy: 0.9609\n", "Epoch 805: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1012 - accuracy: 0.9609 - val_loss: 0.2717 - val_accuracy: 0.8644\n", "Epoch 806/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1017 - accuracy: 0.9688\n", "Epoch 806: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 960ms/step - loss: 0.1017 - accuracy: 0.9688 - val_loss: 0.2710 - val_accuracy: 0.8644\n", "Epoch 807/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0986 - accuracy: 0.9688\n", "Epoch 807: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 946ms/step - loss: 0.0986 - accuracy: 0.9688 - val_loss: 0.2702 - val_accuracy: 0.8644\n", "Epoch 808/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1174 - accuracy: 0.9688\n", "Epoch 808: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1174 - accuracy: 0.9688 - val_loss: 0.2693 - val_accuracy: 0.8644\n", "Epoch 809/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0800 - accuracy: 0.9750\n", "Epoch 809: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0800 - accuracy: 0.9750 - val_loss: 0.2683 - val_accuracy: 0.8475\n", "Epoch 810/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1655 - accuracy: 0.8875\n", "Epoch 810: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 849ms/step - loss: 0.1655 - accuracy: 0.8875 - val_loss: 0.2673 - val_accuracy: 0.8475\n", "Epoch 811/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0940 - accuracy: 0.9750\n", "Epoch 811: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0940 - accuracy: 0.9750 - val_loss: 0.2662 - val_accuracy: 0.8475\n", "Epoch 812/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0860 - accuracy: 0.9750\n", "Epoch 812: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0860 - accuracy: 0.9750 - val_loss: 0.2628 - val_accuracy: 0.8475\n", "Epoch 813/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0997 - accuracy: 0.9297\n", "Epoch 813: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 976ms/step - loss: 0.0997 - accuracy: 0.9297 - val_loss: 0.2612 - val_accuracy: 0.8475\n", "Epoch 814/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1229 - accuracy: 0.9625\n", "Epoch 814: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 847ms/step - loss: 0.1229 - accuracy: 0.9625 - val_loss: 0.2585 - val_accuracy: 0.8475\n", "Epoch 815/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1036 - accuracy: 0.9500\n", "Epoch 815: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 834ms/step - loss: 0.1036 - accuracy: 0.9500 - val_loss: 0.2557 - val_accuracy: 0.8475\n", "Epoch 816/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0913 - accuracy: 0.9609\n", "Epoch 816: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 980ms/step - loss: 0.0913 - accuracy: 0.9609 - val_loss: 0.2546 - val_accuracy: 0.8475\n", "Epoch 817/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1231 - accuracy: 0.9375\n", "Epoch 817: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1231 - accuracy: 0.9375 - val_loss: 0.2543 - val_accuracy: 0.8475\n", "Epoch 818/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0968 - accuracy: 0.9750\n", "Epoch 818: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0968 - accuracy: 0.9750 - val_loss: 0.2539 - val_accuracy: 0.8475\n", "Epoch 819/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0983 - accuracy: 0.9688\n", "Epoch 819: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0983 - accuracy: 0.9688 - val_loss: 0.2527 - val_accuracy: 0.8475\n", "Epoch 820/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0990 - accuracy: 0.9766\n", "Epoch 820: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 965ms/step - loss: 0.0990 - accuracy: 0.9766 - val_loss: 0.2513 - val_accuracy: 0.8475\n", "Epoch 821/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0738 - accuracy: 0.9750\n", "Epoch 821: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0738 - accuracy: 0.9750 - val_loss: 0.2507 - val_accuracy: 0.8475\n", "Epoch 822/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1152 - accuracy: 0.9609\n", "Epoch 822: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1152 - accuracy: 0.9609 - val_loss: 0.2488 - val_accuracy: 0.8475\n", "Epoch 823/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0756 - accuracy: 0.9625\n", "Epoch 823: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0756 - accuracy: 0.9625 - val_loss: 0.2470 - val_accuracy: 0.8475\n", "Epoch 824/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0963 - accuracy: 0.9844\n", "Epoch 824: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0963 - accuracy: 0.9844 - val_loss: 0.2454 - val_accuracy: 0.8475\n", "Epoch 825/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1150 - accuracy: 0.9688\n", "Epoch 825: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1150 - accuracy: 0.9688 - val_loss: 0.2448 - val_accuracy: 0.8475\n", "Epoch 826/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1223 - accuracy: 0.9500\n", "Epoch 826: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1223 - accuracy: 0.9500 - val_loss: 0.2419 - val_accuracy: 0.8644\n", "Epoch 827/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0789 - accuracy: 0.9688\n", "Epoch 827: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0789 - accuracy: 0.9688 - val_loss: 0.2401 - val_accuracy: 0.8644\n", "Epoch 828/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0897 - accuracy: 0.9750\n", "Epoch 828: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0897 - accuracy: 0.9750 - val_loss: 0.2401 - val_accuracy: 0.8644\n", "Epoch 829/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1105 - accuracy: 0.9531\n", "Epoch 829: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 938ms/step - loss: 0.1105 - accuracy: 0.9531 - val_loss: 0.2408 - val_accuracy: 0.8644\n", "Epoch 830/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0924 - accuracy: 0.9609\n", "Epoch 830: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0924 - accuracy: 0.9609 - val_loss: 0.2409 - val_accuracy: 0.8644\n", "Epoch 831/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0712 - accuracy: 0.9688\n", "Epoch 831: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0712 - accuracy: 0.9688 - val_loss: 0.2412 - val_accuracy: 0.8644\n", "Epoch 832/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0620 - accuracy: 0.9750\n", "Epoch 832: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 811ms/step - loss: 0.0620 - accuracy: 0.9750 - val_loss: 0.2411 - val_accuracy: 0.8644\n", "Epoch 833/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1238 - accuracy: 0.9297\n", "Epoch 833: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 949ms/step - loss: 0.1238 - accuracy: 0.9297 - val_loss: 0.2420 - val_accuracy: 0.8644\n", "Epoch 834/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0821 - accuracy: 0.9844\n", "Epoch 834: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0821 - accuracy: 0.9844 - val_loss: 0.2424 - val_accuracy: 0.8644\n", "Epoch 835/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1200 - accuracy: 0.9375\n", "Epoch 835: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 958ms/step - loss: 0.1200 - accuracy: 0.9375 - val_loss: 0.2430 - val_accuracy: 0.8644\n", "Epoch 836/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1401 - accuracy: 0.9375\n", "Epoch 836: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 933ms/step - loss: 0.1401 - accuracy: 0.9375 - val_loss: 0.2434 - val_accuracy: 0.8644\n", "Epoch 837/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0621 - accuracy: 0.9922\n", "Epoch 837: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0621 - accuracy: 0.9922 - val_loss: 0.2446 - val_accuracy: 0.8644\n", "Epoch 838/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1004 - accuracy: 0.9500\n", "Epoch 838: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 817ms/step - loss: 0.1004 - accuracy: 0.9500 - val_loss: 0.2464 - val_accuracy: 0.8644\n", "Epoch 839/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0905 - accuracy: 0.9766\n", "Epoch 839: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0905 - accuracy: 0.9766 - val_loss: 0.2481 - val_accuracy: 0.8644\n", "Epoch 840/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1004 - accuracy: 0.9500\n", "Epoch 840: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 887ms/step - loss: 0.1004 - accuracy: 0.9500 - val_loss: 0.2505 - val_accuracy: 0.8644\n", "Epoch 841/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1146 - accuracy: 0.9750\n", "Epoch 841: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1146 - accuracy: 0.9750 - val_loss: 0.2507 - val_accuracy: 0.8644\n", "Epoch 842/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0898 - accuracy: 0.9844\n", "Epoch 842: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0898 - accuracy: 0.9844 - val_loss: 0.2503 - val_accuracy: 0.8644\n", "Epoch 843/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1224 - accuracy: 0.9375\n", "Epoch 843: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1224 - accuracy: 0.9375 - val_loss: 0.2509 - val_accuracy: 0.8644\n", "Epoch 844/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0545 - accuracy: 0.9875\n", "Epoch 844: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 848ms/step - loss: 0.0545 - accuracy: 0.9875 - val_loss: 0.2514 - val_accuracy: 0.8644\n", "Epoch 845/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1240 - accuracy: 0.9250\n", "Epoch 845: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1240 - accuracy: 0.9250 - val_loss: 0.2505 - val_accuracy: 0.8644\n", "Epoch 846/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1128 - accuracy: 0.9750\n", "Epoch 846: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1128 - accuracy: 0.9750 - val_loss: 0.2508 - val_accuracy: 0.8644\n", "Epoch 847/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0841 - accuracy: 0.9500\n", "Epoch 847: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0841 - accuracy: 0.9500 - val_loss: 0.2514 - val_accuracy: 0.8644\n", "Epoch 848/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0703 - accuracy: 0.9844\n", "Epoch 848: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 928ms/step - loss: 0.0703 - accuracy: 0.9844 - val_loss: 0.2520 - val_accuracy: 0.8644\n", "Epoch 849/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0979 - accuracy: 0.9531\n", "Epoch 849: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0979 - accuracy: 0.9531 - val_loss: 0.2536 - val_accuracy: 0.8644\n", "Epoch 850/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0953 - accuracy: 0.9750\n", "Epoch 850: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 842ms/step - loss: 0.0953 - accuracy: 0.9750 - val_loss: 0.2552 - val_accuracy: 0.8644\n", "Epoch 851/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0794 - accuracy: 0.9750\n", "Epoch 851: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 833ms/step - loss: 0.0794 - accuracy: 0.9750 - val_loss: 0.2572 - val_accuracy: 0.8644\n", "Epoch 852/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0963 - accuracy: 0.9688\n", "Epoch 852: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0963 - accuracy: 0.9688 - val_loss: 0.2586 - val_accuracy: 0.8644\n", "Epoch 853/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0843 - accuracy: 0.9625\n", "Epoch 853: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0843 - accuracy: 0.9625 - val_loss: 0.2596 - val_accuracy: 0.8644\n", "Epoch 854/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1328 - accuracy: 0.9453\n", "Epoch 854: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1328 - accuracy: 0.9453 - val_loss: 0.2612 - val_accuracy: 0.8644\n", "Epoch 855/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1115 - accuracy: 0.9453\n", "Epoch 855: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1115 - accuracy: 0.9453 - val_loss: 0.2625 - val_accuracy: 0.8644\n", "Epoch 856/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0815 - accuracy: 0.9750\n", "Epoch 856: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 881ms/step - loss: 0.0815 - accuracy: 0.9750 - val_loss: 0.2628 - val_accuracy: 0.8644\n", "Epoch 857/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0965 - accuracy: 0.9609\n", "Epoch 857: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0965 - accuracy: 0.9609 - val_loss: 0.2621 - val_accuracy: 0.8644\n", "Epoch 858/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0653 - accuracy: 0.9844\n", "Epoch 858: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0653 - accuracy: 0.9844 - val_loss: 0.2615 - val_accuracy: 0.8644\n", "Epoch 859/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0777 - accuracy: 0.9844\n", "Epoch 859: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 947ms/step - loss: 0.0777 - accuracy: 0.9844 - val_loss: 0.2625 - val_accuracy: 0.8644\n", "Epoch 860/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0645 - accuracy: 0.9750\n", "Epoch 860: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0645 - accuracy: 0.9750 - val_loss: 0.2642 - val_accuracy: 0.8644\n", "Epoch 861/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0972 - accuracy: 0.9531\n", "Epoch 861: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0972 - accuracy: 0.9531 - val_loss: 0.2652 - val_accuracy: 0.8644\n", "Epoch 862/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0886 - accuracy: 0.9750\n", "Epoch 862: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 864ms/step - loss: 0.0886 - accuracy: 0.9750 - val_loss: 0.2662 - val_accuracy: 0.8644\n", "Epoch 863/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0888 - accuracy: 0.9625\n", "Epoch 863: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0888 - accuracy: 0.9625 - val_loss: 0.2676 - val_accuracy: 0.8644\n", "Epoch 864/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0918 - accuracy: 0.9297\n", "Epoch 864: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 955ms/step - loss: 0.0918 - accuracy: 0.9297 - val_loss: 0.2694 - val_accuracy: 0.8644\n", "Epoch 865/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0777 - accuracy: 0.9750\n", "Epoch 865: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 840ms/step - loss: 0.0777 - accuracy: 0.9750 - val_loss: 0.2710 - val_accuracy: 0.8644\n", "Epoch 866/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0713 - accuracy: 0.9844\n", "Epoch 866: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0713 - accuracy: 0.9844 - val_loss: 0.2715 - val_accuracy: 0.8644\n", "Epoch 867/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0677 - accuracy: 0.9750\n", "Epoch 867: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 840ms/step - loss: 0.0677 - accuracy: 0.9750 - val_loss: 0.2721 - val_accuracy: 0.8644\n", "Epoch 868/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0762 - accuracy: 0.9625\n", "Epoch 868: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0762 - accuracy: 0.9625 - val_loss: 0.2707 - val_accuracy: 0.8644\n", "Epoch 869/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0939 - accuracy: 0.9875\n", "Epoch 869: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 871ms/step - loss: 0.0939 - accuracy: 0.9875 - val_loss: 0.2699 - val_accuracy: 0.8644\n", "Epoch 870/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0782 - accuracy: 0.9875\n", "Epoch 870: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 839ms/step - loss: 0.0782 - accuracy: 0.9875 - val_loss: 0.2694 - val_accuracy: 0.8644\n", "Epoch 871/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0965 - accuracy: 0.9531\n", "Epoch 871: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 962ms/step - loss: 0.0965 - accuracy: 0.9531 - val_loss: 0.2689 - val_accuracy: 0.8644\n", "Epoch 872/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0861 - accuracy: 0.9625\n", "Epoch 872: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0861 - accuracy: 0.9625 - val_loss: 0.2691 - val_accuracy: 0.8644\n", "Epoch 873/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0783 - accuracy: 0.9609\n", "Epoch 873: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 937ms/step - loss: 0.0783 - accuracy: 0.9609 - val_loss: 0.2699 - val_accuracy: 0.8644\n", "Epoch 874/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1119 - accuracy: 0.9688\n", "Epoch 874: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1119 - accuracy: 0.9688 - val_loss: 0.2719 - val_accuracy: 0.8644\n", "Epoch 875/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0761 - accuracy: 0.9500\n", "Epoch 875: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0761 - accuracy: 0.9500 - val_loss: 0.2753 - val_accuracy: 0.8644\n", "Epoch 876/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0681 - accuracy: 0.9875\n", "Epoch 876: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 824ms/step - loss: 0.0681 - accuracy: 0.9875 - val_loss: 0.2789 - val_accuracy: 0.8644\n", "Epoch 877/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0823 - accuracy: 0.9844\n", "Epoch 877: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0823 - accuracy: 0.9844 - val_loss: 0.2809 - val_accuracy: 0.8644\n", "Epoch 878/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0974 - accuracy: 0.9750\n", "Epoch 878: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 921ms/step - loss: 0.0974 - accuracy: 0.9750 - val_loss: 0.2807 - val_accuracy: 0.8644\n", "Epoch 879/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0780 - accuracy: 0.9750\n", "Epoch 879: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0780 - accuracy: 0.9750 - val_loss: 0.2798 - val_accuracy: 0.8644\n", "Epoch 880/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0934 - accuracy: 0.9609\n", "Epoch 880: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0934 - accuracy: 0.9609 - val_loss: 0.2805 - val_accuracy: 0.8644\n", "Epoch 881/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0931 - accuracy: 0.9609\n", "Epoch 881: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0931 - accuracy: 0.9609 - val_loss: 0.2824 - val_accuracy: 0.8644\n", "Epoch 882/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0906 - accuracy: 0.9688\n", "Epoch 882: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 947ms/step - loss: 0.0906 - accuracy: 0.9688 - val_loss: 0.2839 - val_accuracy: 0.8644\n", "Epoch 883/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1245 - accuracy: 0.9141\n", "Epoch 883: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1245 - accuracy: 0.9141 - val_loss: 0.2849 - val_accuracy: 0.8644\n", "Epoch 884/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0833 - accuracy: 0.9500\n", "Epoch 884: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0833 - accuracy: 0.9500 - val_loss: 0.2872 - val_accuracy: 0.8644\n", "Epoch 885/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0882 - accuracy: 0.9766\n", "Epoch 885: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 981ms/step - loss: 0.0882 - accuracy: 0.9766 - val_loss: 0.2888 - val_accuracy: 0.8644\n", "Epoch 886/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0874 - accuracy: 0.9844\n", "Epoch 886: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 970ms/step - loss: 0.0874 - accuracy: 0.9844 - val_loss: 0.2896 - val_accuracy: 0.8644\n", "Epoch 887/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0693 - accuracy: 0.9750\n", "Epoch 887: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 837ms/step - loss: 0.0693 - accuracy: 0.9750 - val_loss: 0.2900 - val_accuracy: 0.8644\n", "Epoch 888/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1022 - accuracy: 0.9375\n", "Epoch 888: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 819ms/step - loss: 0.1022 - accuracy: 0.9375 - val_loss: 0.2897 - val_accuracy: 0.8644\n", "Epoch 889/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0957 - accuracy: 0.9750\n", "Epoch 889: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 844ms/step - loss: 0.0957 - accuracy: 0.9750 - val_loss: 0.2891 - val_accuracy: 0.8644\n", "Epoch 890/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1106 - accuracy: 0.9531\n", "Epoch 890: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1106 - accuracy: 0.9531 - val_loss: 0.2846 - val_accuracy: 0.8644\n", "Epoch 891/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0942 - accuracy: 0.9609\n", "Epoch 891: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0942 - accuracy: 0.9609 - val_loss: 0.2803 - val_accuracy: 0.8644\n", "Epoch 892/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1219 - accuracy: 0.9453\n", "Epoch 892: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1219 - accuracy: 0.9453 - val_loss: 0.2752 - val_accuracy: 0.8644\n", "Epoch 893/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0828 - accuracy: 0.9750\n", "Epoch 893: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0828 - accuracy: 0.9750 - val_loss: 0.2698 - val_accuracy: 0.8644\n", "Epoch 894/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1041 - accuracy: 0.9375\n", "Epoch 894: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1041 - accuracy: 0.9375 - val_loss: 0.2643 - val_accuracy: 0.8644\n", "Epoch 895/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0839 - accuracy: 0.9500\n", "Epoch 895: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 834ms/step - loss: 0.0839 - accuracy: 0.9500 - val_loss: 0.2609 - val_accuracy: 0.8644\n", "Epoch 896/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1266 - accuracy: 0.9375\n", "Epoch 896: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 972ms/step - loss: 0.1266 - accuracy: 0.9375 - val_loss: 0.2591 - val_accuracy: 0.8644\n", "Epoch 897/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0911 - accuracy: 0.9531\n", "Epoch 897: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0911 - accuracy: 0.9531 - val_loss: 0.2583 - val_accuracy: 0.8475\n", "Epoch 898/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1015 - accuracy: 0.9500\n", "Epoch 898: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 866ms/step - loss: 0.1015 - accuracy: 0.9500 - val_loss: 0.2576 - val_accuracy: 0.8475\n", "Epoch 899/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0907 - accuracy: 0.9766\n", "Epoch 899: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0907 - accuracy: 0.9766 - val_loss: 0.2573 - val_accuracy: 0.8475\n", "Epoch 900/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0948 - accuracy: 0.9609\n", "Epoch 900: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0948 - accuracy: 0.9609 - val_loss: 0.2570 - val_accuracy: 0.8475\n", "Epoch 901/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1040 - accuracy: 0.9750\n", "Epoch 901: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 819ms/step - loss: 0.1040 - accuracy: 0.9750 - val_loss: 0.2567 - val_accuracy: 0.8475\n", "Epoch 902/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1039 - accuracy: 0.9141\n", "Epoch 902: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1039 - accuracy: 0.9141 - val_loss: 0.2574 - val_accuracy: 0.8475\n", "Epoch 903/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0861 - accuracy: 0.9625\n", "Epoch 903: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 829ms/step - loss: 0.0861 - accuracy: 0.9625 - val_loss: 0.2590 - val_accuracy: 0.8475\n", "Epoch 904/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0647 - accuracy: 0.9875\n", "Epoch 904: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0647 - accuracy: 0.9875 - val_loss: 0.2597 - val_accuracy: 0.8475\n", "Epoch 905/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0822 - accuracy: 0.9500\n", "Epoch 905: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0822 - accuracy: 0.9500 - val_loss: 0.2606 - val_accuracy: 0.8475\n", "Epoch 906/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0629 - accuracy: 0.9750\n", "Epoch 906: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 851ms/step - loss: 0.0629 - accuracy: 0.9750 - val_loss: 0.2621 - val_accuracy: 0.8475\n", "Epoch 907/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0631 - accuracy: 1.0000\n", "Epoch 907: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0631 - accuracy: 1.0000 - val_loss: 0.2651 - val_accuracy: 0.8475\n", "Epoch 908/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0794 - accuracy: 0.9875\n", "Epoch 908: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0794 - accuracy: 0.9875 - val_loss: 0.2677 - val_accuracy: 0.8475\n", "Epoch 909/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0681 - accuracy: 1.0000\n", "Epoch 909: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0681 - accuracy: 1.0000 - val_loss: 0.2719 - val_accuracy: 0.8475\n", "Epoch 910/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0788 - accuracy: 0.9531\n", "Epoch 910: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0788 - accuracy: 0.9531 - val_loss: 0.2756 - val_accuracy: 0.8475\n", "Epoch 911/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0893 - accuracy: 0.9531\n", "Epoch 911: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 923ms/step - loss: 0.0893 - accuracy: 0.9531 - val_loss: 0.2787 - val_accuracy: 0.8475\n", "Epoch 912/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1026 - accuracy: 0.9688\n", "Epoch 912: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1026 - accuracy: 0.9688 - val_loss: 0.2811 - val_accuracy: 0.8475\n", "Epoch 913/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0945 - accuracy: 0.9688\n", "Epoch 913: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 937ms/step - loss: 0.0945 - accuracy: 0.9688 - val_loss: 0.2832 - val_accuracy: 0.8305\n", "Epoch 914/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0744 - accuracy: 0.9750\n", "Epoch 914: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0744 - accuracy: 0.9750 - val_loss: 0.2846 - val_accuracy: 0.8305\n", "Epoch 915/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0825 - accuracy: 0.9500\n", "Epoch 915: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0825 - accuracy: 0.9500 - val_loss: 0.2836 - val_accuracy: 0.8305\n", "Epoch 916/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0687 - accuracy: 0.9875\n", "Epoch 916: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0687 - accuracy: 0.9875 - val_loss: 0.2818 - val_accuracy: 0.8305\n", "Epoch 917/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1094 - accuracy: 0.9500\n", "Epoch 917: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 841ms/step - loss: 0.1094 - accuracy: 0.9500 - val_loss: 0.2799 - val_accuracy: 0.8475\n", "Epoch 918/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0705 - accuracy: 0.9875\n", "Epoch 918: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 891ms/step - loss: 0.0705 - accuracy: 0.9875 - val_loss: 0.2781 - val_accuracy: 0.8475\n", "Epoch 919/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0739 - accuracy: 0.9750\n", "Epoch 919: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 844ms/step - loss: 0.0739 - accuracy: 0.9750 - val_loss: 0.2760 - val_accuracy: 0.8475\n", "Epoch 920/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0654 - accuracy: 0.9875\n", "Epoch 920: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 819ms/step - loss: 0.0654 - accuracy: 0.9875 - val_loss: 0.2761 - val_accuracy: 0.8475\n", "Epoch 921/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1149 - accuracy: 0.9453\n", "Epoch 921: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1149 - accuracy: 0.9453 - val_loss: 0.2791 - val_accuracy: 0.8305\n", "Epoch 922/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0815 - accuracy: 0.9750\n", "Epoch 922: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 840ms/step - loss: 0.0815 - accuracy: 0.9750 - val_loss: 0.2815 - val_accuracy: 0.8305\n", "Epoch 923/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1019 - accuracy: 0.9766\n", "Epoch 923: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1019 - accuracy: 0.9766 - val_loss: 0.2835 - val_accuracy: 0.8305\n", "Epoch 924/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0601 - accuracy: 1.0000\n", "Epoch 924: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0601 - accuracy: 1.0000 - val_loss: 0.2857 - val_accuracy: 0.8305\n", "Epoch 925/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1296 - accuracy: 0.9125\n", "Epoch 925: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 839ms/step - loss: 0.1296 - accuracy: 0.9125 - val_loss: 0.2871 - val_accuracy: 0.8305\n", "Epoch 926/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0943 - accuracy: 0.9766\n", "Epoch 926: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0943 - accuracy: 0.9766 - val_loss: 0.2907 - val_accuracy: 0.8305\n", "Epoch 927/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0939 - accuracy: 0.9766\n", "Epoch 927: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0939 - accuracy: 0.9766 - val_loss: 0.2958 - val_accuracy: 0.8305\n", "Epoch 928/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0990 - accuracy: 0.9625\n", "Epoch 928: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0990 - accuracy: 0.9625 - val_loss: 0.2993 - val_accuracy: 0.8136\n", "Epoch 929/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0945 - accuracy: 0.9609\n", "Epoch 929: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0945 - accuracy: 0.9609 - val_loss: 0.3029 - val_accuracy: 0.8136\n", "Epoch 930/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0748 - accuracy: 0.9844\n", "Epoch 930: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0748 - accuracy: 0.9844 - val_loss: 0.3062 - val_accuracy: 0.8136\n", "Epoch 931/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0828 - accuracy: 0.9766\n", "Epoch 931: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0828 - accuracy: 0.9766 - val_loss: 0.3082 - val_accuracy: 0.8136\n", "Epoch 932/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1561 - accuracy: 0.9500\n", "Epoch 932: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 902ms/step - loss: 0.1561 - accuracy: 0.9500 - val_loss: 0.3088 - val_accuracy: 0.8136\n", "Epoch 933/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0936 - accuracy: 0.9531\n", "Epoch 933: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 985ms/step - loss: 0.0936 - accuracy: 0.9531 - val_loss: 0.3044 - val_accuracy: 0.8136\n", "Epoch 934/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0693 - accuracy: 0.9750\n", "Epoch 934: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0693 - accuracy: 0.9750 - val_loss: 0.3002 - val_accuracy: 0.8136\n", "Epoch 935/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0751 - accuracy: 0.9688\n", "Epoch 935: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 958ms/step - loss: 0.0751 - accuracy: 0.9688 - val_loss: 0.2972 - val_accuracy: 0.8305\n", "Epoch 936/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0536 - accuracy: 0.9875\n", "Epoch 936: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 843ms/step - loss: 0.0536 - accuracy: 0.9875 - val_loss: 0.2937 - val_accuracy: 0.8305\n", "Epoch 937/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0572 - accuracy: 0.9875\n", "Epoch 937: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 857ms/step - loss: 0.0572 - accuracy: 0.9875 - val_loss: 0.2893 - val_accuracy: 0.8305\n", "Epoch 938/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0632 - accuracy: 0.9625\n", "Epoch 938: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0632 - accuracy: 0.9625 - val_loss: 0.2845 - val_accuracy: 0.8305\n", "Epoch 939/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1012 - accuracy: 0.9531\n", "Epoch 939: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1012 - accuracy: 0.9531 - val_loss: 0.2796 - val_accuracy: 0.8305\n", "Epoch 940/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0739 - accuracy: 0.9625\n", "Epoch 940: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 860ms/step - loss: 0.0739 - accuracy: 0.9625 - val_loss: 0.2747 - val_accuracy: 0.8475\n", "Epoch 941/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0882 - accuracy: 0.9531\n", "Epoch 941: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0882 - accuracy: 0.9531 - val_loss: 0.2706 - val_accuracy: 0.8475\n", "Epoch 942/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0617 - accuracy: 0.9844\n", "Epoch 942: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 983ms/step - loss: 0.0617 - accuracy: 0.9844 - val_loss: 0.2677 - val_accuracy: 0.8475\n", "Epoch 943/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0785 - accuracy: 0.9625\n", "Epoch 943: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0785 - accuracy: 0.9625 - val_loss: 0.2661 - val_accuracy: 0.8475\n", "Epoch 944/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0550 - accuracy: 0.9875\n", "Epoch 944: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0550 - accuracy: 0.9875 - val_loss: 0.2647 - val_accuracy: 0.8475\n", "Epoch 945/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0747 - accuracy: 0.9688\n", "Epoch 945: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0747 - accuracy: 0.9688 - val_loss: 0.2630 - val_accuracy: 0.8475\n", "Epoch 946/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0778 - accuracy: 0.9766\n", "Epoch 946: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0778 - accuracy: 0.9766 - val_loss: 0.2610 - val_accuracy: 0.8475\n", "Epoch 947/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1018 - accuracy: 0.9688\n", "Epoch 947: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1018 - accuracy: 0.9688 - val_loss: 0.2591 - val_accuracy: 0.8475\n", "Epoch 948/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0876 - accuracy: 0.9688\n", "Epoch 948: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0876 - accuracy: 0.9688 - val_loss: 0.2570 - val_accuracy: 0.8475\n", "Epoch 949/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1242 - accuracy: 0.9375\n", "Epoch 949: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 816ms/step - loss: 0.1242 - accuracy: 0.9375 - val_loss: 0.2563 - val_accuracy: 0.8644\n", "Epoch 950/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1184 - accuracy: 0.9297\n", "Epoch 950: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1184 - accuracy: 0.9297 - val_loss: 0.2557 - val_accuracy: 0.8644\n", "Epoch 951/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0717 - accuracy: 0.9750\n", "Epoch 951: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 841ms/step - loss: 0.0717 - accuracy: 0.9750 - val_loss: 0.2561 - val_accuracy: 0.8644\n", "Epoch 952/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0772 - accuracy: 0.9875\n", "Epoch 952: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 885ms/step - loss: 0.0772 - accuracy: 0.9875 - val_loss: 0.2571 - val_accuracy: 0.8644\n", "Epoch 953/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0977 - accuracy: 0.9500\n", "Epoch 953: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0977 - accuracy: 0.9500 - val_loss: 0.2591 - val_accuracy: 0.8475\n", "Epoch 954/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0724 - accuracy: 0.9750\n", "Epoch 954: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0724 - accuracy: 0.9750 - val_loss: 0.2622 - val_accuracy: 0.8475\n", "Epoch 955/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0957 - accuracy: 0.9750\n", "Epoch 955: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 838ms/step - loss: 0.0957 - accuracy: 0.9750 - val_loss: 0.2667 - val_accuracy: 0.8475\n", "Epoch 956/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0891 - accuracy: 0.9688\n", "Epoch 956: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0891 - accuracy: 0.9688 - val_loss: 0.2706 - val_accuracy: 0.8475\n", "Epoch 957/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1035 - accuracy: 0.9609\n", "Epoch 957: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1035 - accuracy: 0.9609 - val_loss: 0.2731 - val_accuracy: 0.8475\n", "Epoch 958/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0647 - accuracy: 0.9922\n", "Epoch 958: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0647 - accuracy: 0.9922 - val_loss: 0.2742 - val_accuracy: 0.8305\n", "Epoch 959/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0958 - accuracy: 0.9875\n", "Epoch 959: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 849ms/step - loss: 0.0958 - accuracy: 0.9875 - val_loss: 0.2751 - val_accuracy: 0.8305\n", "Epoch 960/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0807 - accuracy: 0.9750\n", "Epoch 960: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0807 - accuracy: 0.9750 - val_loss: 0.2768 - val_accuracy: 0.8305\n", "Epoch 961/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0948 - accuracy: 0.9625\n", "Epoch 961: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 819ms/step - loss: 0.0948 - accuracy: 0.9625 - val_loss: 0.2801 - val_accuracy: 0.8305\n", "Epoch 962/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0776 - accuracy: 0.9766\n", "Epoch 962: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0776 - accuracy: 0.9766 - val_loss: 0.2844 - val_accuracy: 0.8475\n", "Epoch 963/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1424 - accuracy: 0.9000\n", "Epoch 963: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1424 - accuracy: 0.9000 - val_loss: 0.2886 - val_accuracy: 0.8305\n", "Epoch 964/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0914 - accuracy: 0.9625\n", "Epoch 964: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0914 - accuracy: 0.9625 - val_loss: 0.2915 - val_accuracy: 0.8305\n", "Epoch 965/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0729 - accuracy: 0.9875\n", "Epoch 965: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0729 - accuracy: 0.9875 - val_loss: 0.2938 - val_accuracy: 0.8475\n", "Epoch 966/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0875 - accuracy: 0.9766\n", "Epoch 966: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0875 - accuracy: 0.9766 - val_loss: 0.2974 - val_accuracy: 0.8305\n", "Epoch 967/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0654 - accuracy: 0.9766\n", "Epoch 967: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 963ms/step - loss: 0.0654 - accuracy: 0.9766 - val_loss: 0.3005 - val_accuracy: 0.8305\n", "Epoch 968/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0662 - accuracy: 0.9844\n", "Epoch 968: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 931ms/step - loss: 0.0662 - accuracy: 0.9844 - val_loss: 0.3030 - val_accuracy: 0.8305\n", "Epoch 969/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0808 - accuracy: 0.9688\n", "Epoch 969: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 948ms/step - loss: 0.0808 - accuracy: 0.9688 - val_loss: 0.3052 - val_accuracy: 0.8305\n", "Epoch 970/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1014 - accuracy: 0.9531\n", "Epoch 970: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.1014 - accuracy: 0.9531 - val_loss: 0.3074 - val_accuracy: 0.8305\n", "Epoch 971/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0944 - accuracy: 0.9688\n", "Epoch 971: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0944 - accuracy: 0.9688 - val_loss: 0.3092 - val_accuracy: 0.8305\n", "Epoch 972/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0662 - accuracy: 0.9844\n", "Epoch 972: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0662 - accuracy: 0.9844 - val_loss: 0.3097 - val_accuracy: 0.8305\n", "Epoch 973/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0667 - accuracy: 0.9766\n", "Epoch 973: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 959ms/step - loss: 0.0667 - accuracy: 0.9766 - val_loss: 0.3094 - val_accuracy: 0.8305\n", "Epoch 974/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0818 - accuracy: 0.9688\n", "Epoch 974: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0818 - accuracy: 0.9688 - val_loss: 0.3085 - val_accuracy: 0.8305\n", "Epoch 975/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0910 - accuracy: 0.9688\n", "Epoch 975: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0910 - accuracy: 0.9688 - val_loss: 0.3087 - val_accuracy: 0.8305\n", "Epoch 976/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1308 - accuracy: 0.9375\n", "Epoch 976: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1308 - accuracy: 0.9375 - val_loss: 0.3068 - val_accuracy: 0.8305\n", "Epoch 977/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0767 - accuracy: 0.9750\n", "Epoch 977: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0767 - accuracy: 0.9750 - val_loss: 0.3051 - val_accuracy: 0.8305\n", "Epoch 978/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1055 - accuracy: 0.9500\n", "Epoch 978: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 848ms/step - loss: 0.1055 - accuracy: 0.9500 - val_loss: 0.3017 - val_accuracy: 0.8305\n", "Epoch 979/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0511 - accuracy: 1.0000\n", "Epoch 979: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 904ms/step - loss: 0.0511 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.8305\n", "Epoch 980/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0713 - accuracy: 0.9531\n", "Epoch 980: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 939ms/step - loss: 0.0713 - accuracy: 0.9531 - val_loss: 0.2944 - val_accuracy: 0.8305\n", "Epoch 981/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0922 - accuracy: 0.9609\n", "Epoch 981: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 972ms/step - loss: 0.0922 - accuracy: 0.9609 - val_loss: 0.2921 - val_accuracy: 0.8475\n", "Epoch 982/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0891 - accuracy: 0.9625\n", "Epoch 982: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0891 - accuracy: 0.9625 - val_loss: 0.2933 - val_accuracy: 0.8475\n", "Epoch 983/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0949 - accuracy: 0.9453\n", "Epoch 983: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 951ms/step - loss: 0.0949 - accuracy: 0.9453 - val_loss: 0.2925 - val_accuracy: 0.8475\n", "Epoch 984/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0539 - accuracy: 0.9922\n", "Epoch 984: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 995ms/step - loss: 0.0539 - accuracy: 0.9922 - val_loss: 0.2918 - val_accuracy: 0.8475\n", "Epoch 985/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0669 - accuracy: 0.9766\n", "Epoch 985: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0669 - accuracy: 0.9766 - val_loss: 0.2904 - val_accuracy: 0.8305\n", "Epoch 986/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0790 - accuracy: 0.9875\n", "Epoch 986: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 833ms/step - loss: 0.0790 - accuracy: 0.9875 - val_loss: 0.2900 - val_accuracy: 0.8305\n", "Epoch 987/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1056 - accuracy: 0.9750\n", "Epoch 987: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1056 - accuracy: 0.9750 - val_loss: 0.2854 - val_accuracy: 0.8475\n", "Epoch 988/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0730 - accuracy: 0.9875\n", "Epoch 988: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0730 - accuracy: 0.9875 - val_loss: 0.2825 - val_accuracy: 0.8475\n", "Epoch 989/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0671 - accuracy: 0.9922\n", "Epoch 989: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 985ms/step - loss: 0.0671 - accuracy: 0.9922 - val_loss: 0.2798 - val_accuracy: 0.8305\n", "Epoch 990/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0840 - accuracy: 0.9766\n", "Epoch 990: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0840 - accuracy: 0.9766 - val_loss: 0.2768 - val_accuracy: 0.8475\n", "Epoch 991/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0820 - accuracy: 0.9766\n", "Epoch 991: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 933ms/step - loss: 0.0820 - accuracy: 0.9766 - val_loss: 0.2731 - val_accuracy: 0.8475\n", "Epoch 992/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1183 - accuracy: 0.9250\n", "Epoch 992: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 842ms/step - loss: 0.1183 - accuracy: 0.9250 - val_loss: 0.2701 - val_accuracy: 0.8305\n", "Epoch 993/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1168 - accuracy: 0.9625\n", "Epoch 993: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.1168 - accuracy: 0.9625 - val_loss: 0.2679 - val_accuracy: 0.8305\n", "Epoch 994/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0559 - accuracy: 0.9922\n", "Epoch 994: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0559 - accuracy: 0.9922 - val_loss: 0.2664 - val_accuracy: 0.8305\n", "Epoch 995/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0766 - accuracy: 0.9688\n", "Epoch 995: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 950ms/step - loss: 0.0766 - accuracy: 0.9688 - val_loss: 0.2641 - val_accuracy: 0.8305\n", "Epoch 996/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0701 - accuracy: 0.9688\n", "Epoch 996: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0701 - accuracy: 0.9688 - val_loss: 0.2621 - val_accuracy: 0.8305\n", "Epoch 997/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0732 - accuracy: 0.9750\n", "Epoch 997: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 1s/step - loss: 0.0732 - accuracy: 0.9750 - val_loss: 0.2621 - val_accuracy: 0.8305\n", "Epoch 998/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0791 - accuracy: 0.9688\n", "Epoch 998: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 920ms/step - loss: 0.0791 - accuracy: 0.9688 - val_loss: 0.2632 - val_accuracy: 0.8305\n", "Epoch 999/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.1398 - accuracy: 0.9375\n", "Epoch 999: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 1s 866ms/step - loss: 0.1398 - accuracy: 0.9375 - val_loss: 0.2647 - val_accuracy: 0.8305\n", "Epoch 1000/1000\n", "2/2 [==============================] - ETA: 0s - loss: 0.0725 - accuracy: 0.9766\n", "Epoch 1000: saving model to training_1/cp.ckpt\n", "2/2 [==============================] - 2s 1s/step - loss: 0.0725 - accuracy: 0.9766 - val_loss: 0.2671 - val_accuracy: 0.8475\n" ] } ], "source": [ "checkpoint_path = \"training_1/cp.ckpt\"\n", "checkpoint_dir = os.path.dirname(checkpoint_path)\n", "\n", "# Create a callback that saves the model's weights\n", "cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n", " save_weights_only=True,\n", " verbose=1)\n", "\n", "history = model.fit(train_dir,\n", " steps_per_epoch = 2,\n", " epochs = 1000,\n", " validation_data = val_dir,\n", " callbacks=[cp_callback])" ] }, { "cell_type": "markdown", "source": [ "# Plot do Modelo " ], "metadata": { "id": "crrsKJ5vDcpU" } }, { "cell_type": "code", "source": [ "acc = history.history['accuracy']\n", "val_acc = history.history['val_accuracy']\n", "\n", "loss = history.history['loss']\n", "val_loss = history.history['val_loss']\n", "\n", "plt.figure(figsize=(18, 18))\n", "plt.subplot(2, 1, 1)\n", "plt.plot(acc, label='Training Accuracy')\n", "plt.plot(val_acc, label='Validation Accuracy')\n", "plt.legend(loc='lower right')\n", "plt.ylabel('Accuracy')\n", "plt.ylim([min(plt.ylim()),1])\n", "plt.title('Training and Validation Accuracy')\n", "\n", "plt.subplot(2, 1, 2)\n", "plt.plot(loss, label='Training Loss')\n", "plt.plot(val_loss, label='Validation Loss')\n", "plt.legend(loc='upper right')\n", "plt.ylabel('Cross Entropy')\n", "plt.ylim([0,1.0])\n", "plt.title('Training and Validation Loss')\n", "plt.xlabel('epoch')\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "yIUMzZBJIKF0", "outputId": "890f7f59-2b2a-4db2-dbe8-f7ebbd9e6324" }, "execution_count": 9, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "# Como podemos verificar, nosso modelo em determinados momentos atinge 100% de acurácia.\n", "\n", "Ao decorro do nosso treimaneto, podemos ver que nosso modelo perfoma bem. Diminuindo o Loss tanto em treino, quanto em validação e aumentando nossa acurácia" ], "metadata": { "id": "Xp631y14XvT0" } }, { "cell_type": "markdown", "source": [ "# Salvando o modelo" ], "metadata": { "id": "BqGha-2vNr5K" } }, { "cell_type": "code", "source": [ "!pip install pyyaml h5py # Required to save models in HDF5 format" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sk8oUyCZNfuQ", "outputId": "61c33f01-a16c-40a7-8401-79abbf1212b5" }, "execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (3.13)\n", "Requirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (3.1.0)\n", "Requirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.7/dist-packages (from h5py) (1.21.6)\n", "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py) (1.5.2)\n" ] } ] }, { "cell_type": "code", "source": [ "import os\n", "\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "\n", "print(tf.version.VERSION)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mvDuOiepNx3I", "outputId": "24707e40-7854-46cd-db79-0172333d2209" }, "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "2.8.2\n" ] } ] }, { "cell_type": "code", "source": [ "!mkdir -p saved_model\n", "model.save('saved_model/my_model')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HIXU0-EfNy5z", "outputId": "df62f339-cee5-47d8-ab66-bb66f62b05bd" }, "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "WARNING:absl:Function `_wrapped_model` contains input name(s) mobilenetv2_1.00_224_input with unsupported characters which will be renamed to mobilenetv2_1_00_224_input in the SavedModel.\n" ] } ] }, { "cell_type": "code", "source": [ "# my_model directory\n", "%ls saved_model\n", "\n", "# Contains an assets folder, saved_model.pb, and variables folder.\n", "%ls saved_model/my_model" ], "metadata": { "id": "ng2JHXxFNzqw", "outputId": "87815b39-253f-4a7b-dff5-786fa5fd5942", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[0m\u001b[01;34mmy_model\u001b[0m/\n", "\u001b[0m\u001b[01;34massets\u001b[0m/ keras_metadata.pb saved_model.pb \u001b[01;34mvariables\u001b[0m/\n" ] } ] }, { "cell_type": "code", "source": [ "new_model = tf.keras.models.load_model('saved_model/my_model')\n", "\n", "# Check its architecture\n", "new_model.summary()" ], "metadata": { "id": "O2TOYYrQN0pi", "outputId": "03dfddce-aa16-45cc-919e-7e18e7e6067b", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model: \"sequential\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " mobilenetv2_1.00_224 (Funct (None, 7, 7, 1280) 2257984 \n", " ional) \n", " \n", " global_average_pooling2d (G (None, 1280) 0 \n", " lobalAveragePooling2D) \n", " \n", " dense (Dense) (None, 128) 163968 \n", " \n", " dropout (Dropout) (None, 128) 0 \n", " \n", " dense_1 (Dense) (None, 3) 387 \n", " \n", "=================================================================\n", "Total params: 2,422,339\n", "Trainable params: 164,355\n", "Non-trainable params: 2,257,984\n", "_________________________________________________________________\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Salvando o modelo para Depois" ], "metadata": { "id": "GmvrnuTSN2vK" } }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "metadata": { "id": "xocw-4qPN2Kv", "outputId": "b1176155-1868-4003-ddb5-d9f0ca29a13c", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 15, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ] }, { "cell_type": "code", "source": [ "!mkdir /content/drive/MyDrive/Animal_Classification/model" ], "metadata": { "id": "mQzS-x-GN7JL", "outputId": "b1ca9544-fedf-4652-a037-d5c53741cfcc", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 16, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "mkdir: cannot create directory ‘/content/drive/MyDrive/Animal_Classification/model’: No such file or directory\n" ] } ] }, { "cell_type": "code", "source": [ "!cp -r saved_model /content/drive/MyDrive/Animal_Classification/model" ], "metadata": { "id": "dRAix5CdN8Ya", "outputId": "a76b42c2-df7c-42a9-c093-004d731534f2", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 17, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "cp: cannot create directory '/content/drive/MyDrive/Animal_Classification/model': No such file or directory\n" ] } ] }, { "cell_type": "code", "source": [ "!zip -r modelo.zip saved_model/my_model/ " ], "metadata": { "id": "g6VmOTKKOCNK", "outputId": "a3f82b4c-b427-4ebd-e76a-2828af66f66a", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 18, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " adding: saved_model/my_model/ (stored 0%)\n", " adding: saved_model/my_model/saved_model.pb (deflated 91%)\n", " adding: saved_model/my_model/keras_metadata.pb (deflated 96%)\n", " adding: saved_model/my_model/variables/ (stored 0%)\n", " adding: saved_model/my_model/variables/variables.index (deflated 75%)\n", " adding: saved_model/my_model/variables/variables.data-00000-of-00001 (deflated 8%)\n", " adding: saved_model/my_model/assets/ (stored 0%)\n" ] } ] }, { "cell_type": "code", "source": [ "!mkdir /content/drive/MyDrive/BananaStepps/model/weights" ], "metadata": { "id": "ESsj-IaGOEJO", "outputId": "914bffe0-8639-40a3-9181-09cf58accddb", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 19, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "mkdir: cannot create directory ‘/content/drive/MyDrive/BananaStepps/model/weights’: No such file or directory\n" ] } ] }, { "cell_type": "code", "source": [ "!cp -r /content/training_1 /content/drive/MyDrive/Animal_Classification/model/weights" ], "metadata": { "id": "D1-827cWOMcv", "outputId": "75e5a77c-cd84-416e-d171-7f44df037ce8", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 20, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "cp: cannot create directory '/content/drive/MyDrive/Animal_Classification/model/weights': No such file or directory\n" ] } ] }, { "cell_type": "code", "source": [ "!zip -r weights.zip /content/training_1" ], "metadata": { "id": "b0HrJY93OP55", "outputId": "86a7f829-9a2d-414c-d5b6-92d5d9685b36", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 21, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " adding: content/training_1/ (stored 0%)\n", " adding: content/training_1/checkpoint (deflated 38%)\n", " adding: content/training_1/cp.ckpt.data-00000-of-00001 (deflated 7%)\n", " adding: content/training_1/cp.ckpt.index (deflated 78%)\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Referências\n", "\n", "# https://www.kaggle.com/code/sunritjana/plant-disease-detection-mobilenetv2\n", "# https://www.kaggle.com/code/wldzia/tensorflow-using-mobilenet-v2\n", "# https://www.kaggle.com/code/pranshu15/tensorflow-keras-mobilenetv2-77\n", "# https://www.kaggle.com/code/abhishek123maurya/mobilenetv2-paddy-disease-classifier\n", "#https://github.com/vcasadei/Redes-Neurais-CESAR-School/blob/2021.2/5%20-%20Redes%20Neurais%20Convolucionais/5.6-Banana_Classification_with_MobileNetV2.ipynb" ], "metadata": { "id": "E8ZeX4GJMmYG" } } ], "metadata": { "kernelspec": { "display_name": "Python 3.10.4 64-bit", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.4" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "a93d1b4d18416fb8758c482120bd018d75a170535249f73f5dd8a7b9f59ad66c" } }, "colab": { "name": "Projeto_RNA_CESL_wandb.ipynb", "provenance": [] } }, "nbformat": 4, "nbformat_minor": 0 }