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HenryAI
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KerasCodeExamples.txt

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margin_square = tf.math.square(tf.math.maximum(margin - (y_pred), 0))
return tf.math.reduce_mean(
(1 - y_true) * square_pred + (y_true) * margin_square
)
return contrastive_loss
Compile the model with the contrastive loss
siamese.compile(loss=loss(margin=margin), optimizer=\"RMSprop\", metrics=[\"accuracy\"])
siamese.summary()
Model: \"model_1\"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_2 (InputLayer) [(None, 28, 28, 1)] 0
__________________________________________________________________________________________________
input_3 (InputLayer) [(None, 28, 28, 1)] 0
__________________________________________________________________________________________________
model (Functional) (None, 10) 5318 input_2[0][0]
input_3[0][0]
__________________________________________________________________________________________________
lambda (Lambda) (None, 1) 0 model[0][0]
model[1][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 1) 4 lambda[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (None, 1) 2 batch_normalization_2[0][0]
==================================================================================================
Total params: 5,324
Trainable params: 4,808
Non-trainable params: 516
__________________________________________________________________________________________________
Train the model
history = siamese.fit(
[x_train_1, x_train_2],
labels_train,
validation_data=([x_val_1, x_val_2], labels_val),
batch_size=batch_size,
epochs=epochs,
)
Epoch 1/10
3750/3750 [==============================] - 25s 6ms/step - loss: 0.1993 - accuracy: 0.6626 - val_loss: 0.0525 - val_accuracy: 0.9331
Epoch 2/10
3750/3750 [==============================] - 23s 6ms/step - loss: 0.0611 - accuracy: 0.9187 - val_loss: 0.0277 - val_accuracy: 0.9644
Epoch 3/10
3750/3750 [==============================] - 24s 6ms/step - loss: 0.0455 - accuracy: 0.9409 - val_loss: 0.0214 - val_accuracy: 0.9719
Epoch 4/10
3750/3750 [==============================] - 27s 7ms/step - loss: 0.0386 - accuracy: 0.9506 - val_loss: 0.0198 - val_accuracy: 0.9743
Epoch 5/10
3750/3750 [==============================] - 45s 12ms/step - loss: 0.0362 - accuracy: 0.9529 - val_loss: 0.0169 - val_accuracy: 0.9783
Epoch 6/10
2497/3750 [==================>...........] - ETA: 10s - loss: 0.0343 - accuracy: 0.9552
Visualize results
def plt_metric(history, metric, title, has_valid=True):
\"\"\"Plots the given 'metric' from 'history'.
Arguments:
history: history attribute of History object returned from Model.fit.
metric: Metric to plot, a string value present as key in 'history'.
title: A string to be used as title of plot.
has_valid: Boolean, true if valid data was passed to Model.fit else false.
Returns:
None.
\"\"\"
plt.plot(history[metric])
if has_valid:
plt.plot(history[\"val_\" + metric])
plt.legend([\"train\", \"validation\"], loc=\"upper left\")
plt.title(title)
plt.ylabel(metric)
plt.xlabel(\"epoch\")
plt.show()
# Plot the accuracy
plt_metric(history=history.history, metric=\"accuracy\", title=\"Model accuracy\")
# Plot the constrastive loss
plt_metric(history=history.history, metric=\"loss\", title=\"Constrastive Loss\")
png
png
Evaluate the model
results = siamese.evaluate([x_test_1, x_test_2], labels_test)
print(\"test loss, test acc:\", results)
625/625 [==============================] - 3s 4ms/step - loss: 0.0150 - accuracy: 0.9810
test loss, test acc: [0.015001337975263596, 0.9810000061988831]
Visualize the predictions
predictions = siamese.predict([x_test_1, x_test_2])
visualize(pairs_test, labels_test, to_show=3, predictions=predictions, test=True)
png
Training a Siamese Network to compare the similarity of images using a triplet loss function.
Introduction
A Siamese Network is a type of network architecture that contains two or more identical subnetworks used to generate feature vectors for each input and compare them.
Siamese Networks can be applied to different use cases, like detecting duplicates, finding anomalies, and face recognition.