Datasets:
HenryAI
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KerasCodeExamples.txt

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1862/1862 [==============================] - 5s 3ms/step - loss: 0.5105 - sparse_categorical_accuracy: 0.7809
Epoch 23/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5089 - sparse_categorical_accuracy: 0.7813
Epoch 24/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5074 - sparse_categorical_accuracy: 0.7823
Epoch 25/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5061 - sparse_categorical_accuracy: 0.7821
Epoch 26/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5048 - sparse_categorical_accuracy: 0.7832
Epoch 27/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5037 - sparse_categorical_accuracy: 0.7837
Epoch 28/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5017 - sparse_categorical_accuracy: 0.7846
Epoch 29/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.5010 - sparse_categorical_accuracy: 0.7851
Epoch 30/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.4991 - sparse_categorical_accuracy: 0.7861
Epoch 31/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.4989 - sparse_categorical_accuracy: 0.7849
Epoch 32/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.4979 - sparse_categorical_accuracy: 0.7865
Epoch 33/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.4961 - sparse_categorical_accuracy: 0.7867
Epoch 34/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.4955 - sparse_categorical_accuracy: 0.7871
Epoch 35/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.4946 - sparse_categorical_accuracy: 0.7871
Epoch 36/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.4946 - sparse_categorical_accuracy: 0.7873
Epoch 37/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.4925 - sparse_categorical_accuracy: 0.7877
Epoch 38/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.4920 - sparse_categorical_accuracy: 0.7884
Epoch 39/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.4910 - sparse_categorical_accuracy: 0.7887
Epoch 40/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.4909 - sparse_categorical_accuracy: 0.7883
Epoch 41/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.4906 - sparse_categorical_accuracy: 0.7890
Epoch 42/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.4883 - sparse_categorical_accuracy: 0.7892
Epoch 43/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.4883 - sparse_categorical_accuracy: 0.7896
Epoch 44/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.4875 - sparse_categorical_accuracy: 0.7908
Epoch 45/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.4866 - sparse_categorical_accuracy: 0.7900
Epoch 46/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.4864 - sparse_categorical_accuracy: 0.7902
Epoch 47/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.4862 - sparse_categorical_accuracy: 0.7909
Epoch 48/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.4849 - sparse_categorical_accuracy: 0.7908
Epoch 49/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.4843 - sparse_categorical_accuracy: 0.7910
Epoch 50/50
1862/1862 [==============================] - 5s 3ms/step - loss: 0.4841 - sparse_categorical_accuracy: 0.7921
Model training finished
Test accuracy: 80.61%
The deep and cross model achieves ~81% test accuracy.
Conclusion
You can use Keras Preprocessing Layers to easily handle categorical features with different encoding mechanisms, including one-hot encoding and feature embedding. In addition, different model architectures — like wide, deep, and cross networks — have different advantages, with respect to different dataset properties. You can explore using them independently or combining them to achieve the best result for your dataset.
Detect anomalies in a timeseries using an Autoencoder.
Introduction
This script demonstrates how you can use a reconstruction convolutional autoencoder model to detect anomalies in timeseries data.
Setup
import numpy as np
import pandas as pd
from tensorflow import keras
from tensorflow.keras import layers
from matplotlib import pyplot as plt
Load the data
We will use the Numenta Anomaly Benchmark(NAB) dataset. It provides artifical timeseries data containing labeled anomalous periods of behavior. Data are ordered, timestamped, single-valued metrics.
We will use the art_daily_small_noise.csv file for training and the art_daily_jumpsup.csv file for testing. The simplicity of this dataset allows us to demonstrate anomaly detection effectively.
master_url_root = \"https://raw.githubusercontent.com/numenta/NAB/master/data/\"
df_small_noise_url_suffix = \"artificialNoAnomaly/art_daily_small_noise.csv\"
df_small_noise_url = master_url_root + df_small_noise_url_suffix
df_small_noise = pd.read_csv(
df_small_noise_url, parse_dates=True, index_col=\"timestamp\"
)
df_daily_jumpsup_url_suffix = \"artificialWithAnomaly/art_daily_jumpsup.csv\"
df_daily_jumpsup_url = master_url_root + df_daily_jumpsup_url_suffix
df_daily_jumpsup = pd.read_csv(
df_daily_jumpsup_url, parse_dates=True, index_col=\"timestamp\"
)
Quick look at the data
print(df_small_noise.head())
print(df_daily_jumpsup.head())
value
timestamp
2014-04-01 00:00:00 18.324919