{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "pUWCd_Ch5J49"
},
"source": [
"# Character-level recurrent sequence-to-sequence model\n",
"\n",
"**Author:** [fchollet](https://twitter.com/fchollet)
\n",
"**Date created:** 2017/09/29
\n",
"**Last modified:** 2020/04/26
\n",
"**Description:** Character-level recurrent sequence-to-sequence model."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "y2uZhuQ-5J5B"
},
"source": [
"## Introduction\n",
"\n",
"This example demonstrates how to implement a basic character-level\n",
"recurrent sequence-to-sequence model. We apply it to translating\n",
"short English sentences into short French sentences,\n",
"character-by-character. Note that it is fairly unusual to\n",
"do character-level machine translation, as word-level\n",
"models are more common in this domain.\n",
"\n",
"**Summary of the algorithm**\n",
"\n",
"- We start with input sequences from a domain (e.g. English sentences)\n",
" and corresponding target sequences from another domain\n",
" (e.g. French sentences).\n",
"- An encoder LSTM turns input sequences to 2 state vectors\n",
" (we keep the last LSTM state and discard the outputs).\n",
"- A decoder LSTM is trained to turn the target sequences into\n",
" the same sequence but offset by one timestep in the future,\n",
" a training process called \"teacher forcing\" in this context.\n",
" It uses as initial state the state vectors from the encoder.\n",
" Effectively, the decoder learns to generate `targets[t+1...]`\n",
" given `targets[...t]`, conditioned on the input sequence.\n",
"- In inference mode, when we want to decode unknown input sequences, we:\n",
" - Encode the input sequence into state vectors\n",
" - Start with a target sequence of size 1\n",
" (just the start-of-sequence character)\n",
" - Feed the state vectors and 1-char target sequence\n",
" to the decoder to produce predictions for the next character\n",
" - Sample the next character using these predictions\n",
" (we simply use argmax).\n",
" - Append the sampled character to the target sequence\n",
" - Repeat until we generate the end-of-sequence character or we\n",
" hit the character limit.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ymvVW7f55J5C"
},
"source": [
"## Setup\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "IKzDuATV5J5C"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KsdDP8835J5D"
},
"source": [
"## Download the data\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "QjrXitpv5J5E",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a5c71e87-b3c7-419e-d987-5f2551c0e236"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['Archive: fra-eng.zip',\n",
" ' inflating: _about.txt ',\n",
" ' inflating: fra.txt ']"
]
},
"metadata": {},
"execution_count": 2
}
],
"source": [
"!!curl -O http://www.manythings.org/anki/fra-eng.zip\n",
"!!unzip fra-eng.zip\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4Qi0m1NC5J5E"
},
"source": [
"## Configuration\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "UB6qEq0b5J5F"
},
"outputs": [],
"source": [
"batch_size = 64 # Batch size for training.\n",
"epochs = 100 # Number of epochs to train for.\n",
"latent_dim = 256 # Latent dimensionality of the encoding space.\n",
"num_samples = 10000 # Number of samples to train on.\n",
"# Path to the data txt file on disk.\n",
"data_path = \"fra.txt\"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "50hqcmjH5J5F"
},
"source": [
"## Prepare the data\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "XIoa7eHS5J5G",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "583ed656-723a-4c36-eede-259afa77ffba"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Number of samples: 10000\n",
"Number of unique input tokens: 71\n",
"Number of unique output tokens: 92\n",
"Max sequence length for inputs: 15\n",
"Max sequence length for outputs: 59\n"
]
}
],
"source": [
"# Vectorize the data.\n",
"input_texts = []\n",
"target_texts = []\n",
"input_characters = set()\n",
"target_characters = set()\n",
"with open(data_path, \"r\", encoding=\"utf-8\") as f:\n",
" lines = f.read().split(\"\\n\")\n",
"for line in lines[: min(num_samples, len(lines) - 1)]:\n",
" input_text, target_text, _ = line.split(\"\\t\")\n",
" # We use \"tab\" as the \"start sequence\" character\n",
" # for the targets, and \"\\n\" as \"end sequence\" character.\n",
" target_text = \"\\t\" + target_text + \"\\n\"\n",
" input_texts.append(input_text)\n",
" target_texts.append(target_text)\n",
" for char in input_text:\n",
" if char not in input_characters:\n",
" input_characters.add(char)\n",
" for char in target_text:\n",
" if char not in target_characters:\n",
" target_characters.add(char)\n",
"\n",
"input_characters = sorted(list(input_characters))\n",
"target_characters = sorted(list(target_characters))\n",
"num_encoder_tokens = len(input_characters)\n",
"num_decoder_tokens = len(target_characters)\n",
"max_encoder_seq_length = max([len(txt) for txt in input_texts])\n",
"max_decoder_seq_length = max([len(txt) for txt in target_texts])\n",
"\n",
"print(\"Number of samples:\", len(input_texts))\n",
"print(\"Number of unique input tokens:\", num_encoder_tokens)\n",
"print(\"Number of unique output tokens:\", num_decoder_tokens)\n",
"print(\"Max sequence length for inputs:\", max_encoder_seq_length)\n",
"print(\"Max sequence length for outputs:\", max_decoder_seq_length)\n",
"\n",
"input_token_index = dict([(char, i) for i, char in enumerate(input_characters)])\n",
"target_token_index = dict([(char, i) for i, char in enumerate(target_characters)])\n",
"\n",
"encoder_input_data = np.zeros(\n",
" (len(input_texts), max_encoder_seq_length, num_encoder_tokens), dtype=\"float32\"\n",
")\n",
"decoder_input_data = np.zeros(\n",
" (len(input_texts), max_decoder_seq_length, num_decoder_tokens), dtype=\"float32\"\n",
")\n",
"decoder_target_data = np.zeros(\n",
" (len(input_texts), max_decoder_seq_length, num_decoder_tokens), dtype=\"float32\"\n",
")\n",
"\n",
"for i, (input_text, target_text) in enumerate(zip(input_texts, target_texts)):\n",
" for t, char in enumerate(input_text):\n",
" encoder_input_data[i, t, input_token_index[char]] = 1.0\n",
" encoder_input_data[i, t + 1 :, input_token_index[\" \"]] = 1.0\n",
" for t, char in enumerate(target_text):\n",
" # decoder_target_data is ahead of decoder_input_data by one timestep\n",
" decoder_input_data[i, t, target_token_index[char]] = 1.0\n",
" if t > 0:\n",
" # decoder_target_data will be ahead by one timestep\n",
" # and will not include the start character.\n",
" decoder_target_data[i, t - 1, target_token_index[char]] = 1.0\n",
" decoder_input_data[i, t + 1 :, target_token_index[\" \"]] = 1.0\n",
" decoder_target_data[i, t:, target_token_index[\" \"]] = 1.0\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Nmmia38F5J5H"
},
"source": [
"## Build the model\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "xUBfSVSH5J5H"
},
"outputs": [],
"source": [
"# Define an input sequence and process it.\n",
"encoder_inputs = keras.Input(shape=(None, num_encoder_tokens))\n",
"encoder = keras.layers.LSTM(latent_dim, return_state=True)\n",
"encoder_outputs, state_h, state_c = encoder(encoder_inputs)\n",
"\n",
"# We discard `encoder_outputs` and only keep the states.\n",
"encoder_states = [state_h, state_c]\n",
"\n",
"# Set up the decoder, using `encoder_states` as initial state.\n",
"decoder_inputs = keras.Input(shape=(None, num_decoder_tokens))\n",
"\n",
"# We set up our decoder to return full output sequences,\n",
"# and to return internal states as well. We don't use the\n",
"# return states in the training model, but we will use them in inference.\n",
"decoder_lstm = keras.layers.LSTM(latent_dim, return_sequences=True, return_state=True)\n",
"decoder_outputs, _, _ = decoder_lstm(decoder_inputs, initial_state=encoder_states)\n",
"decoder_dense = keras.layers.Dense(num_decoder_tokens, activation=\"softmax\")\n",
"decoder_outputs = decoder_dense(decoder_outputs)\n",
"\n",
"# Define the model that will turn\n",
"# `encoder_input_data` & `decoder_input_data` into `decoder_target_data`\n",
"model = keras.Model([encoder_inputs, decoder_inputs], decoder_outputs)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MYvCCy4i5J5I"
},
"source": [
"## Train the model\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "3kgt3bnl5J5I",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f347151f-3666-4f10-8a05-6949a2361301"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/100\n",
"125/125 [==============================] - 8s 19ms/step - loss: 1.1334 - accuracy: 0.7368 - val_loss: 1.0400 - val_accuracy: 0.7264\n",
"Epoch 2/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.8081 - accuracy: 0.7805 - val_loss: 0.8330 - val_accuracy: 0.7693\n",
"Epoch 3/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.6407 - accuracy: 0.8185 - val_loss: 0.6837 - val_accuracy: 0.8008\n",
"Epoch 4/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.5614 - accuracy: 0.8366 - val_loss: 0.6254 - val_accuracy: 0.8138\n",
"Epoch 5/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.5160 - accuracy: 0.8490 - val_loss: 0.5773 - val_accuracy: 0.8346\n",
"Epoch 6/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.4815 - accuracy: 0.8589 - val_loss: 0.5527 - val_accuracy: 0.8383\n",
"Epoch 7/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.4538 - accuracy: 0.8659 - val_loss: 0.5317 - val_accuracy: 0.8430\n",
"Epoch 8/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.4314 - accuracy: 0.8716 - val_loss: 0.5120 - val_accuracy: 0.8484\n",
"Epoch 9/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.4118 - accuracy: 0.8768 - val_loss: 0.5096 - val_accuracy: 0.8493\n",
"Epoch 10/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.3945 - accuracy: 0.8818 - val_loss: 0.4892 - val_accuracy: 0.8545\n",
"Epoch 11/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.3785 - accuracy: 0.8864 - val_loss: 0.4884 - val_accuracy: 0.8550\n",
"Epoch 12/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.3637 - accuracy: 0.8905 - val_loss: 0.4725 - val_accuracy: 0.8597\n",
"Epoch 13/100\n",
"125/125 [==============================] - 2s 14ms/step - loss: 0.3498 - accuracy: 0.8946 - val_loss: 0.4674 - val_accuracy: 0.8624\n",
"Epoch 14/100\n",
"125/125 [==============================] - 2s 15ms/step - loss: 0.3370 - accuracy: 0.8981 - val_loss: 0.4597 - val_accuracy: 0.8644\n",
"Epoch 15/100\n",
"125/125 [==============================] - 2s 14ms/step - loss: 0.3244 - accuracy: 0.9020 - val_loss: 0.4533 - val_accuracy: 0.8661\n",
"Epoch 16/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.3124 - accuracy: 0.9056 - val_loss: 0.4569 - val_accuracy: 0.8655\n",
"Epoch 17/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.3012 - accuracy: 0.9088 - val_loss: 0.4515 - val_accuracy: 0.8688\n",
"Epoch 18/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.2904 - accuracy: 0.9118 - val_loss: 0.4440 - val_accuracy: 0.8704\n",
"Epoch 19/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.2803 - accuracy: 0.9154 - val_loss: 0.4473 - val_accuracy: 0.8697\n",
"Epoch 20/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.2703 - accuracy: 0.9179 - val_loss: 0.4470 - val_accuracy: 0.8709\n",
"Epoch 21/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.2611 - accuracy: 0.9212 - val_loss: 0.4447 - val_accuracy: 0.8725\n",
"Epoch 22/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.2519 - accuracy: 0.9235 - val_loss: 0.4457 - val_accuracy: 0.8721\n",
"Epoch 23/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.2436 - accuracy: 0.9262 - val_loss: 0.4503 - val_accuracy: 0.8723\n",
"Epoch 24/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.2356 - accuracy: 0.9283 - val_loss: 0.4506 - val_accuracy: 0.8732\n",
"Epoch 25/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.2275 - accuracy: 0.9309 - val_loss: 0.4531 - val_accuracy: 0.8733\n",
"Epoch 26/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.2201 - accuracy: 0.9328 - val_loss: 0.4524 - val_accuracy: 0.8749\n",
"Epoch 27/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.2132 - accuracy: 0.9353 - val_loss: 0.4615 - val_accuracy: 0.8736\n",
"Epoch 28/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.2064 - accuracy: 0.9370 - val_loss: 0.4609 - val_accuracy: 0.8740\n",
"Epoch 29/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1999 - accuracy: 0.9390 - val_loss: 0.4612 - val_accuracy: 0.8750\n",
"Epoch 30/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1933 - accuracy: 0.9411 - val_loss: 0.4701 - val_accuracy: 0.8734\n",
"Epoch 31/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1877 - accuracy: 0.9427 - val_loss: 0.4718 - val_accuracy: 0.8747\n",
"Epoch 32/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1816 - accuracy: 0.9443 - val_loss: 0.4749 - val_accuracy: 0.8747\n",
"Epoch 33/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1763 - accuracy: 0.9462 - val_loss: 0.4805 - val_accuracy: 0.8746\n",
"Epoch 34/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1711 - accuracy: 0.9477 - val_loss: 0.4855 - val_accuracy: 0.8742\n",
"Epoch 35/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1661 - accuracy: 0.9494 - val_loss: 0.4849 - val_accuracy: 0.8745\n",
"Epoch 36/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1612 - accuracy: 0.9505 - val_loss: 0.4939 - val_accuracy: 0.8739\n",
"Epoch 37/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1566 - accuracy: 0.9518 - val_loss: 0.5005 - val_accuracy: 0.8734\n",
"Epoch 38/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1517 - accuracy: 0.9536 - val_loss: 0.5021 - val_accuracy: 0.8748\n",
"Epoch 39/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1476 - accuracy: 0.9548 - val_loss: 0.5051 - val_accuracy: 0.8744\n",
"Epoch 40/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1434 - accuracy: 0.9561 - val_loss: 0.5081 - val_accuracy: 0.8740\n",
"Epoch 41/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1396 - accuracy: 0.9573 - val_loss: 0.5173 - val_accuracy: 0.8745\n",
"Epoch 42/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1356 - accuracy: 0.9584 - val_loss: 0.5199 - val_accuracy: 0.8745\n",
"Epoch 43/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1318 - accuracy: 0.9591 - val_loss: 0.5236 - val_accuracy: 0.8738\n",
"Epoch 44/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1290 - accuracy: 0.9602 - val_loss: 0.5382 - val_accuracy: 0.8731\n",
"Epoch 45/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1250 - accuracy: 0.9616 - val_loss: 0.5393 - val_accuracy: 0.8736\n",
"Epoch 46/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1218 - accuracy: 0.9624 - val_loss: 0.5392 - val_accuracy: 0.8734\n",
"Epoch 47/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1189 - accuracy: 0.9633 - val_loss: 0.5483 - val_accuracy: 0.8742\n",
"Epoch 48/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1159 - accuracy: 0.9642 - val_loss: 0.5486 - val_accuracy: 0.8740\n",
"Epoch 49/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1127 - accuracy: 0.9652 - val_loss: 0.5606 - val_accuracy: 0.8734\n",
"Epoch 50/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1104 - accuracy: 0.9654 - val_loss: 0.5610 - val_accuracy: 0.8738\n",
"Epoch 51/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1075 - accuracy: 0.9664 - val_loss: 0.5674 - val_accuracy: 0.8735\n",
"Epoch 52/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1050 - accuracy: 0.9673 - val_loss: 0.5702 - val_accuracy: 0.8731\n",
"Epoch 53/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1027 - accuracy: 0.9679 - val_loss: 0.5756 - val_accuracy: 0.8732\n",
"Epoch 54/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.1004 - accuracy: 0.9684 - val_loss: 0.5783 - val_accuracy: 0.8736\n",
"Epoch 55/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0978 - accuracy: 0.9691 - val_loss: 0.5838 - val_accuracy: 0.8729\n",
"Epoch 56/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0955 - accuracy: 0.9700 - val_loss: 0.5851 - val_accuracy: 0.8736\n",
"Epoch 57/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0934 - accuracy: 0.9703 - val_loss: 0.5969 - val_accuracy: 0.8722\n",
"Epoch 58/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0913 - accuracy: 0.9709 - val_loss: 0.6024 - val_accuracy: 0.8723\n",
"Epoch 59/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0890 - accuracy: 0.9717 - val_loss: 0.6073 - val_accuracy: 0.8723\n",
"Epoch 60/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0873 - accuracy: 0.9720 - val_loss: 0.6113 - val_accuracy: 0.8731\n",
"Epoch 61/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0858 - accuracy: 0.9725 - val_loss: 0.6190 - val_accuracy: 0.8726\n",
"Epoch 62/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0836 - accuracy: 0.9732 - val_loss: 0.6139 - val_accuracy: 0.8731\n",
"Epoch 63/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0819 - accuracy: 0.9737 - val_loss: 0.6242 - val_accuracy: 0.8725\n",
"Epoch 64/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0803 - accuracy: 0.9740 - val_loss: 0.6318 - val_accuracy: 0.8709\n",
"Epoch 65/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0784 - accuracy: 0.9748 - val_loss: 0.6384 - val_accuracy: 0.8728\n",
"Epoch 66/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0768 - accuracy: 0.9749 - val_loss: 0.6392 - val_accuracy: 0.8721\n",
"Epoch 67/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0755 - accuracy: 0.9754 - val_loss: 0.6453 - val_accuracy: 0.8718\n",
"Epoch 68/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0741 - accuracy: 0.9758 - val_loss: 0.6492 - val_accuracy: 0.8716\n",
"Epoch 69/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0720 - accuracy: 0.9765 - val_loss: 0.6505 - val_accuracy: 0.8720\n",
"Epoch 70/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0711 - accuracy: 0.9768 - val_loss: 0.6605 - val_accuracy: 0.8720\n",
"Epoch 71/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0698 - accuracy: 0.9771 - val_loss: 0.6621 - val_accuracy: 0.8714\n",
"Epoch 72/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0685 - accuracy: 0.9774 - val_loss: 0.6656 - val_accuracy: 0.8721\n",
"Epoch 73/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0668 - accuracy: 0.9778 - val_loss: 0.6736 - val_accuracy: 0.8715\n",
"Epoch 74/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0654 - accuracy: 0.9782 - val_loss: 0.6759 - val_accuracy: 0.8713\n",
"Epoch 75/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0642 - accuracy: 0.9786 - val_loss: 0.6830 - val_accuracy: 0.8717\n",
"Epoch 76/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0633 - accuracy: 0.9789 - val_loss: 0.6856 - val_accuracy: 0.8705\n",
"Epoch 77/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0623 - accuracy: 0.9792 - val_loss: 0.6924 - val_accuracy: 0.8714\n",
"Epoch 78/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0608 - accuracy: 0.9795 - val_loss: 0.6958 - val_accuracy: 0.8709\n",
"Epoch 79/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0601 - accuracy: 0.9798 - val_loss: 0.7000 - val_accuracy: 0.8712\n",
"Epoch 80/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0589 - accuracy: 0.9799 - val_loss: 0.6989 - val_accuracy: 0.8719\n",
"Epoch 81/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0577 - accuracy: 0.9804 - val_loss: 0.7021 - val_accuracy: 0.8704\n",
"Epoch 82/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0571 - accuracy: 0.9806 - val_loss: 0.7111 - val_accuracy: 0.8705\n",
"Epoch 83/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0562 - accuracy: 0.9808 - val_loss: 0.7124 - val_accuracy: 0.8715\n",
"Epoch 84/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0549 - accuracy: 0.9812 - val_loss: 0.7160 - val_accuracy: 0.8711\n",
"Epoch 85/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0541 - accuracy: 0.9815 - val_loss: 0.7220 - val_accuracy: 0.8707\n",
"Epoch 86/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0537 - accuracy: 0.9817 - val_loss: 0.7173 - val_accuracy: 0.8711\n",
"Epoch 87/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0521 - accuracy: 0.9820 - val_loss: 0.7312 - val_accuracy: 0.8702\n",
"Epoch 88/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0514 - accuracy: 0.9822 - val_loss: 0.7252 - val_accuracy: 0.8718\n",
"Epoch 89/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0507 - accuracy: 0.9825 - val_loss: 0.7324 - val_accuracy: 0.8703\n",
"Epoch 90/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0503 - accuracy: 0.9824 - val_loss: 0.7375 - val_accuracy: 0.8696\n",
"Epoch 91/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0493 - accuracy: 0.9829 - val_loss: 0.7417 - val_accuracy: 0.8699\n",
"Epoch 92/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0485 - accuracy: 0.9831 - val_loss: 0.7448 - val_accuracy: 0.8712\n",
"Epoch 93/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0484 - accuracy: 0.9831 - val_loss: 0.7448 - val_accuracy: 0.8699\n",
"Epoch 94/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0470 - accuracy: 0.9834 - val_loss: 0.7461 - val_accuracy: 0.8709\n",
"Epoch 95/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0468 - accuracy: 0.9834 - val_loss: 0.7468 - val_accuracy: 0.8712\n",
"Epoch 96/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0463 - accuracy: 0.9838 - val_loss: 0.7601 - val_accuracy: 0.8701\n",
"Epoch 97/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0456 - accuracy: 0.9839 - val_loss: 0.7589 - val_accuracy: 0.8702\n",
"Epoch 98/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0448 - accuracy: 0.9840 - val_loss: 0.7604 - val_accuracy: 0.8709\n",
"Epoch 99/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0445 - accuracy: 0.9840 - val_loss: 0.7593 - val_accuracy: 0.8701\n",
"Epoch 100/100\n",
"125/125 [==============================] - 2s 13ms/step - loss: 0.0442 - accuracy: 0.9842 - val_loss: 0.7654 - val_accuracy: 0.8698\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses, lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_layer_call_fn while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"INFO:tensorflow:Assets written to: s2s/assets\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"INFO:tensorflow:Assets written to: s2s/assets\n",
"WARNING:absl: has the same name 'LSTMCell' as a built-in Keras object. Consider renaming to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
"WARNING:absl: has the same name 'LSTMCell' as a built-in Keras object. Consider renaming to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
]
}
],
"source": [
"# early_stopping_patience = 10\n",
"\n",
"# # Add early stopping\n",
"# early_stopping = keras.callbacks.EarlyStopping(\n",
"# monitor=\"val_accuracy\", patience=early_stopping_patience, restore_best_weights=True\n",
"# )\n",
"\n",
"model.compile(\n",
" optimizer=\"rmsprop\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"]\n",
")\n",
"model.fit(\n",
" [encoder_input_data, decoder_input_data],\n",
" decoder_target_data,\n",
" batch_size=batch_size,\n",
" epochs=epochs,\n",
" validation_split=0.2,\n",
" # callbacks=[early_stopping]\n",
")\n",
"# Save model\n",
"model.save(\"s2s\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HxkS8_Pf5J5I"
},
"source": [
"## Run inference (sampling)\n",
"\n",
"1. encode input and retrieve initial decoder state\n",
"2. run one step of decoder with this initial state\n",
"and a \"start of sequence\" token as target.\n",
"Output will be the next target token.\n",
"3. Repeat with the current target token and current states\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "-KKcZuAa5J5I"
},
"outputs": [],
"source": [
"# Define sampling models\n",
"# Restore the model and construct the encoder and decoder.\n",
"model = keras.models.load_model(\"s2s\")\n",
"\n",
"encoder_inputs = model.input[0] # input_1\n",
"encoder_outputs, state_h_enc, state_c_enc = model.layers[2].output # lstm_1\n",
"encoder_states = [state_h_enc, state_c_enc]\n",
"encoder_model = keras.Model(encoder_inputs, encoder_states)\n",
"\n",
"decoder_inputs = model.input[1] # input_2\n",
"decoder_state_input_h = keras.Input(shape=(latent_dim,))\n",
"decoder_state_input_c = keras.Input(shape=(latent_dim,))\n",
"decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]\n",
"decoder_lstm = model.layers[3]\n",
"decoder_outputs, state_h_dec, state_c_dec = decoder_lstm(\n",
" decoder_inputs, initial_state=decoder_states_inputs\n",
")\n",
"decoder_states = [state_h_dec, state_c_dec]\n",
"decoder_dense = model.layers[4]\n",
"decoder_outputs = decoder_dense(decoder_outputs)\n",
"decoder_model = keras.Model(\n",
" [decoder_inputs] + decoder_states_inputs, [decoder_outputs] + decoder_states\n",
")\n",
"\n",
"# Reverse-lookup token index to decode sequences back to\n",
"# something readable.\n",
"reverse_input_char_index = dict((i, char) for char, i in input_token_index.items())\n",
"reverse_target_char_index = dict((i, char) for char, i in target_token_index.items())\n",
"\n",
"\n",
"def decode_sequence(input_seq):\n",
" # Encode the input as state vectors.\n",
" states_value = encoder_model.predict(input_seq)\n",
"\n",
" # Generate empty target sequence of length 1.\n",
" target_seq = np.zeros((1, 1, num_decoder_tokens))\n",
" # Populate the first character of target sequence with the start character.\n",
" target_seq[0, 0, target_token_index[\"\\t\"]] = 1.0\n",
"\n",
" # Sampling loop for a batch of sequences\n",
" # (to simplify, here we assume a batch of size 1).\n",
" stop_condition = False\n",
" decoded_sentence = \"\"\n",
" while not stop_condition:\n",
" output_tokens, h, c = decoder_model.predict([target_seq] + states_value)\n",
"\n",
" # Sample a token\n",
" sampled_token_index = np.argmax(output_tokens[0, -1, :])\n",
" sampled_char = reverse_target_char_index[sampled_token_index]\n",
" decoded_sentence += sampled_char\n",
"\n",
" # Exit condition: either hit max length\n",
" # or find stop character.\n",
" if sampled_char == \"\\n\" or len(decoded_sentence) > max_decoder_seq_length:\n",
" stop_condition = True\n",
"\n",
" # Update the target sequence (of length 1).\n",
" target_seq = np.zeros((1, 1, num_decoder_tokens))\n",
" target_seq[0, 0, sampled_token_index] = 1.0\n",
"\n",
" # Update states\n",
" states_value = [h, c]\n",
" return decoded_sentence\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pLvBXjXg5J5J"
},
"source": [
"You can now generate decoded sentences as such:\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "7fG4EDSX5J5J",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "84f4486e-fc08-4269-fed2-48628b568240"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"-\n",
"Input sentence: Go.\n",
"Decoded sentence: Bouge !\n",
"\n",
"-\n",
"Input sentence: Go.\n",
"Decoded sentence: Bouge !\n",
"\n",
"-\n",
"Input sentence: Go.\n",
"Decoded sentence: Bouge !\n",
"\n",
"-\n",
"Input sentence: Hi.\n",
"Decoded sentence: Salut.\n",
"\n",
"-\n",
"Input sentence: Hi.\n",
"Decoded sentence: Salut.\n",
"\n",
"-\n",
"Input sentence: Run!\n",
"Decoded sentence: Courez !\n",
"\n",
"-\n",
"Input sentence: Run!\n",
"Decoded sentence: Courez !\n",
"\n",
"-\n",
"Input sentence: Run!\n",
"Decoded sentence: Courez !\n",
"\n",
"-\n",
"Input sentence: Run!\n",
"Decoded sentence: Courez !\n",
"\n",
"-\n",
"Input sentence: Run!\n",
"Decoded sentence: Courez !\n",
"\n",
"-\n",
"Input sentence: Run!\n",
"Decoded sentence: Courez !\n",
"\n",
"-\n",
"Input sentence: Run!\n",
"Decoded sentence: Courez !\n",
"\n",
"-\n",
"Input sentence: Run!\n",
"Decoded sentence: Courez !\n",
"\n",
"-\n",
"Input sentence: Run.\n",
"Decoded sentence: Courez !\n",
"\n",
"-\n",
"Input sentence: Run.\n",
"Decoded sentence: Courez !\n",
"\n",
"-\n",
"Input sentence: Run.\n",
"Decoded sentence: Courez !\n",
"\n",
"-\n",
"Input sentence: Run.\n",
"Decoded sentence: Courez !\n",
"\n",
"-\n",
"Input sentence: Run.\n",
"Decoded sentence: Courez !\n",
"\n",
"-\n",
"Input sentence: Run.\n",
"Decoded sentence: Courez !\n",
"\n",
"-\n",
"Input sentence: Run.\n",
"Decoded sentence: Courez !\n",
"\n"
]
}
],
"source": [
"for seq_index in range(20):\n",
" # Take one sequence (part of the training set)\n",
" # for trying out decoding.\n",
" input_seq = encoder_input_data[seq_index : seq_index + 1]\n",
" decoded_sentence = decode_sequence(input_seq)\n",
" print(\"-\")\n",
" print(\"Input sentence:\", input_texts[seq_index])\n",
" print(\"Decoded sentence:\", decoded_sentence)\n"
]
},
{
"cell_type": "code",
"source": [
"import json"
],
"metadata": {
"id": "bqV-cbvJA5hd"
},
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"source": [
"with open(\"input_vocab.json\", \"w\", encoding='utf-8') as outfile:\n",
" json.dump(input_token_index, outfile, ensure_ascii=False)\n",
"with open(\"target_vocab.json\", \"w\", encoding='utf-8') as outfile:\n",
" json.dump(target_token_index, outfile, ensure_ascii=False)"
],
"metadata": {
"id": "jXPS4ycZ9A9o"
},
"execution_count": 13,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!pip install huggingface-hub\n",
"!sudo apt-get install git-lfs\n",
"!git-lfs install"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MCQ_ND66BXn9",
"outputId": "f58a6d0d-2c4b-4fb6-f44e-43b8167a5ded"
},
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting huggingface-hub\n",
" Downloading huggingface_hub-0.4.0-py3-none-any.whl (67 kB)\n",
"\u001b[?25l\r\u001b[K |█████ | 10 kB 35.3 MB/s eta 0:00:01\r\u001b[K |█████████▉ | 20 kB 24.7 MB/s eta 0:00:01\r\u001b[K |██████████████▊ | 30 kB 18.8 MB/s eta 0:00:01\r\u001b[K |███████████████████▋ | 40 kB 16.2 MB/s eta 0:00:01\r\u001b[K |████████████████████████▌ | 51 kB 8.3 MB/s eta 0:00:01\r\u001b[K |█████████████████████████████▍ | 61 kB 9.6 MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 67 kB 4.1 MB/s \n",
"\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (2.23.0)\n",
"Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (3.13)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (3.4.2)\n",
"Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (21.3)\n",
"Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (4.10.1)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (3.10.0.2)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from huggingface-hub) (4.62.3)\n",
"Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.9->huggingface-hub) (3.0.7)\n",
"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->huggingface-hub) (3.7.0)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub) (2.10)\n",
"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub) (3.0.4)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub) (2021.10.8)\n",
"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->huggingface-hub) (1.24.3)\n",
"Installing collected packages: huggingface-hub\n",
"Successfully installed huggingface-hub-0.4.0\n",
"Reading package lists... Done\n",
"Building dependency tree \n",
"Reading state information... Done\n",
"The following packages were automatically installed and are no longer required:\n",
" cuda-command-line-tools-10-0 cuda-command-line-tools-10-1\n",
" cuda-command-line-tools-11-0 cuda-compiler-10-0 cuda-compiler-10-1\n",
" cuda-compiler-11-0 cuda-cuobjdump-10-0 cuda-cuobjdump-10-1\n",
" cuda-cuobjdump-11-0 cuda-cupti-10-0 cuda-cupti-10-1 cuda-cupti-11-0\n",
" cuda-cupti-dev-11-0 cuda-documentation-10-0 cuda-documentation-10-1\n",
" cuda-documentation-11-0 cuda-documentation-11-1 cuda-gdb-10-0 cuda-gdb-10-1\n",
" cuda-gdb-11-0 cuda-gpu-library-advisor-10-0 cuda-gpu-library-advisor-10-1\n",
" cuda-libraries-10-0 cuda-libraries-10-1 cuda-libraries-11-0\n",
" cuda-memcheck-10-0 cuda-memcheck-10-1 cuda-memcheck-11-0 cuda-nsight-10-0\n",
" cuda-nsight-10-1 cuda-nsight-11-0 cuda-nsight-11-1 cuda-nsight-compute-10-0\n",
" cuda-nsight-compute-10-1 cuda-nsight-compute-11-0 cuda-nsight-compute-11-1\n",
" cuda-nsight-systems-10-1 cuda-nsight-systems-11-0 cuda-nsight-systems-11-1\n",
" cuda-nvcc-10-0 cuda-nvcc-10-1 cuda-nvcc-11-0 cuda-nvdisasm-10-0\n",
" cuda-nvdisasm-10-1 cuda-nvdisasm-11-0 cuda-nvml-dev-10-0 cuda-nvml-dev-10-1\n",
" cuda-nvml-dev-11-0 cuda-nvprof-10-0 cuda-nvprof-10-1 cuda-nvprof-11-0\n",
" cuda-nvprune-10-0 cuda-nvprune-10-1 cuda-nvprune-11-0 cuda-nvtx-10-0\n",
" cuda-nvtx-10-1 cuda-nvtx-11-0 cuda-nvvp-10-0 cuda-nvvp-10-1 cuda-nvvp-11-0\n",
" cuda-nvvp-11-1 cuda-samples-10-0 cuda-samples-10-1 cuda-samples-11-0\n",
" cuda-samples-11-1 cuda-sanitizer-11-0 cuda-sanitizer-api-10-1\n",
" cuda-toolkit-10-0 cuda-toolkit-10-1 cuda-toolkit-11-0 cuda-toolkit-11-1\n",
" cuda-tools-10-0 cuda-tools-10-1 cuda-tools-11-0 cuda-tools-11-1\n",
" cuda-visual-tools-10-0 cuda-visual-tools-10-1 cuda-visual-tools-11-0\n",
" cuda-visual-tools-11-1 default-jre dkms freeglut3 freeglut3-dev\n",
" keyboard-configuration libargon2-0 libcap2 libcryptsetup12\n",
" libdevmapper1.02.1 libfontenc1 libidn11 libip4tc0 libjansson4\n",
" libnvidia-cfg1-510 libnvidia-common-460 libnvidia-common-510\n",
" libnvidia-extra-510 libnvidia-fbc1-510 libnvidia-gl-510 libpam-systemd\n",
" libpolkit-agent-1-0 libpolkit-backend-1-0 libpolkit-gobject-1-0 libxfont2\n",
" libxi-dev libxkbfile1 libxmu-dev libxmu-headers libxnvctrl0 libxtst6\n",
" nsight-compute-2020.2.1 nsight-compute-2022.1.0 nsight-systems-2020.3.2\n",
" nsight-systems-2020.3.4 nsight-systems-2021.5.2 nvidia-dkms-510\n",
" nvidia-kernel-common-510 nvidia-kernel-source-510 nvidia-modprobe\n",
" nvidia-settings openjdk-11-jre policykit-1 policykit-1-gnome python3-xkit\n",
" screen-resolution-extra systemd systemd-sysv udev x11-xkb-utils\n",
" xserver-common xserver-xorg-core-hwe-18.04 xserver-xorg-video-nvidia-510\n",
"Use 'sudo apt autoremove' to remove them.\n",
"The following NEW packages will be installed:\n",
" git-lfs\n",
"0 upgraded, 1 newly installed, 0 to remove and 39 not upgraded.\n",
"Need to get 2,129 kB of archives.\n",
"After this operation, 7,662 kB of additional disk space will be used.\n",
"Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 git-lfs amd64 2.3.4-1 [2,129 kB]\n",
"Fetched 2,129 kB in 1s (1,551 kB/s)\n",
"debconf: unable to initialize frontend: Dialog\n",
"debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)\n",
"debconf: falling back to frontend: Readline\n",
"debconf: unable to initialize frontend: Readline\n",
"debconf: (This frontend requires a controlling tty.)\n",
"debconf: falling back to frontend: Teletype\n",
"dpkg-preconfigure: unable to re-open stdin: \n",
"Selecting previously unselected package git-lfs.\n",
"(Reading database ... 155113 files and directories currently installed.)\n",
"Preparing to unpack .../git-lfs_2.3.4-1_amd64.deb ...\n",
"Unpacking git-lfs (2.3.4-1) ...\n",
"Setting up git-lfs (2.3.4-1) ...\n",
"Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
"Error: Failed to call git rev-parse --git-dir --show-toplevel: \"fatal: not a git repository (or any of the parent directories): .git\\n\"\n",
"Git LFS initialized.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!huggingface-cli login"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "vr7EzjdvBzHT",
"outputId": "69fa8334-c203-4000-d970-1a8b8f9f1ba6"
},
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
" _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n",
" _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
" _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n",
" _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n",
" _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n",
"\n",
" To login, `huggingface_hub` now requires a token generated from https://huggingface.co/settings/token.\n",
" (Deprecated, will be removed in v0.3.0) To login with username and password instead, interrupt with Ctrl+C.\n",
" \n",
"Token: \n",
"Login successful\n",
"Your token has been saved to /root/.huggingface/token\n",
"\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
"You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
"\n",
"git config --global credential.helper store\u001b[0m\n"
]
}
]
},
{
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"from huggingface_hub.keras_mixin import push_to_hub_keras\n",
"push_to_hub_keras(model = model, repo_url = \"https://huggingface.co/keras-io/char-lstm-seq2seq\", organization = \"keras-io\")"
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"text": [
"Cloning https://huggingface.co/keras-io/char-lstm-seq2seq into local empty directory.\n",
"WARNING:huggingface_hub.repository:Cloning https://huggingface.co/keras-io/char-lstm-seq2seq into local empty directory.\n",
"WARNING:absl:Found untraced functions such as lstm_cell_2_layer_call_fn, lstm_cell_2_layer_call_and_return_conditional_losses, lstm_cell_3_layer_call_fn, lstm_cell_3_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn while saving (showing 5 of 10). These functions will not be directly callable after loading.\n"
]
},
{
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"text": [
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]
},
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"INFO:tensorflow:Assets written to: char-lstm-seq2seq/assets\n",
"WARNING:absl: has the same name 'LSTMCell' as a built-in Keras object. Consider renaming to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
"WARNING:absl: has the same name 'LSTMCell' as a built-in Keras object. Consider renaming to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
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"Upload file saved_model.pb: 0%| | 3.39k/1.38M [00:00 main\n",
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"WARNING:huggingface_hub.repository:To https://huggingface.co/keras-io/char-lstm-seq2seq\n",
" df51a58..69c5bbb main -> main\n",
"\n"
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},
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