Datasets:
HenryAI
/
KerasCodeExamples.txt

Dataset card Data Studio Files
xet
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4.99k
Total params: 9,505
Trainable params: 9,505
Non-trainable params: 0
_________________________________________________________________
Train the model
Please note that we are using x_train as both the input and the target since this is a reconstruction model.
history = model.fit(
x_train,
x_train,
epochs=50,
batch_size=128,
validation_split=0.1,
callbacks=[
keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=5, mode=\"min\")
],
)
Epoch 1/50
27/27 [==============================] - 2s 35ms/step - loss: 0.5868 - val_loss: 0.1225
Epoch 2/50
27/27 [==============================] - 1s 29ms/step - loss: 0.0882 - val_loss: 0.0404
Epoch 3/50
27/27 [==============================] - 1s 30ms/step - loss: 0.0594 - val_loss: 0.0359
Epoch 4/50
27/27 [==============================] - 1s 29ms/step - loss: 0.0486 - val_loss: 0.0287
Epoch 5/50
27/27 [==============================] - 1s 30ms/step - loss: 0.0398 - val_loss: 0.0231
Epoch 6/50
27/27 [==============================] - 1s 31ms/step - loss: 0.0337 - val_loss: 0.0208
Epoch 7/50
27/27 [==============================] - 1s 31ms/step - loss: 0.0299 - val_loss: 0.0182
Epoch 8/50
27/27 [==============================] - 1s 31ms/step - loss: 0.0271 - val_loss: 0.0187
Epoch 9/50
27/27 [==============================] - 1s 32ms/step - loss: 0.0251 - val_loss: 0.0190
Epoch 10/50
27/27 [==============================] - 1s 31ms/step - loss: 0.0235 - val_loss: 0.0179
Epoch 11/50
27/27 [==============================] - 1s 32ms/step - loss: 0.0224 - val_loss: 0.0189
Epoch 12/50
27/27 [==============================] - 1s 33ms/step - loss: 0.0214 - val_loss: 0.0199
Epoch 13/50
27/27 [==============================] - 1s 33ms/step - loss: 0.0206 - val_loss: 0.0194
Epoch 14/50
27/27 [==============================] - 1s 32ms/step - loss: 0.0199 - val_loss: 0.0208
Epoch 15/50
27/27 [==============================] - 1s 35ms/step - loss: 0.0192 - val_loss: 0.0204
Let's plot training and validation loss to see how the training went.
plt.plot(history.history[\"loss\"], label=\"Training Loss\")
plt.plot(history.history[\"val_loss\"], label=\"Validation Loss\")
plt.legend()
plt.show()
png
Detecting anomalies
We will detect anomalies by determining how well our model can reconstruct the input data.
Find MAE loss on training samples.
Find max MAE loss value. This is the worst our model has performed trying to reconstruct a sample. We will make this the threshold for anomaly detection.
If the reconstruction loss for a sample is greater than this threshold value then we can infer that the model is seeing a pattern that it isn't familiar with. We will label this sample as an anomaly.
# Get train MAE loss.
x_train_pred = model.predict(x_train)
train_mae_loss = np.mean(np.abs(x_train_pred - x_train), axis=1)
plt.hist(train_mae_loss, bins=50)
plt.xlabel(\"Train MAE loss\")
plt.ylabel(\"No of samples\")
plt.show()
# Get reconstruction loss threshold.
threshold = np.max(train_mae_loss)
print(\"Reconstruction error threshold: \", threshold)
png
Reconstruction error threshold: 0.1195600905852785
Compare recontruction
Just for fun, let's see how our model has recontructed the first sample. This is the 288 timesteps from day 1 of our training dataset.
# Checking how the first sequence is learnt
plt.plot(x_train[0])
plt.plot(x_train_pred[0])
plt.show()
png
Prepare test data
df_test_value = (df_daily_jumpsup - training_mean) / training_std
fig, ax = plt.subplots()
df_test_value.plot(legend=False, ax=ax)
plt.show()
# Create sequences from test values.
x_test = create_sequences(df_test_value.values)
print(\"Test input shape: \", x_test.shape)
# Get test MAE loss.
x_test_pred = model.predict(x_test)
test_mae_loss = np.mean(np.abs(x_test_pred - x_test), axis=1)
test_mae_loss = test_mae_loss.reshape((-1))