Datasets:
HenryAI
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KerasCodeExamples.txt

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Computes the triplet loss using the three embeddings produced by the
Siamese Network.
The triplet loss is defined as:
L(A, P, N) = max(‖f(A) - f(P)‖² - ‖f(A) - f(N)‖² + margin, 0)
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def __init__(self, siamese_network, margin=0.5):
super(SiameseModel, self).__init__()
self.siamese_network = siamese_network
self.margin = margin
self.loss_tracker = metrics.Mean(name=\"loss\")
def call(self, inputs):
return self.siamese_network(inputs)
def train_step(self, data):
# GradientTape is a context manager that records every operation that
# you do inside. We are using it here to compute the loss so we can get
# the gradients and apply them using the optimizer specified in
# `compile()`.
with tf.GradientTape() as tape:
loss = self._compute_loss(data)
# Storing the gradients of the loss function with respect to the
# weights/parameters.
gradients = tape.gradient(loss, self.siamese_network.trainable_weights)
# Applying the gradients on the model using the specified optimizer
self.optimizer.apply_gradients(
zip(gradients, self.siamese_network.trainable_weights)
)
# Let's update and return the training loss metric.
self.loss_tracker.update_state(loss)
return {\"loss\": self.loss_tracker.result()}
def test_step(self, data):
loss = self._compute_loss(data)
# Let's update and return the loss metric.
self.loss_tracker.update_state(loss)
return {\"loss\": self.loss_tracker.result()}
def _compute_loss(self, data):
# The output of the network is a tuple containing the distances
# between the anchor and the positive example, and the anchor and
# the negative example.
ap_distance, an_distance = self.siamese_network(data)
# Computing the Triplet Loss by subtracting both distances and
# making sure we don't get a negative value.
loss = ap_distance - an_distance
loss = tf.maximum(loss + self.margin, 0.0)
return loss
@property
def metrics(self):
# We need to list our metrics here so the `reset_states()` can be
# called automatically.
return [self.loss_tracker]
Training
We are now ready to train our model.
siamese_model = SiameseModel(siamese_network)
siamese_model.compile(optimizer=optimizers.Adam(0.0001))
siamese_model.fit(train_dataset, epochs=10, validation_data=val_dataset)
Epoch 1/10
151/151 [==============================] - 277s 2s/step - loss: 0.5014 - val_loss: 0.3719
Epoch 2/10
151/151 [==============================] - 276s 2s/step - loss: 0.3884 - val_loss: 0.3632
Epoch 3/10
151/151 [==============================] - 287s 2s/step - loss: 0.3711 - val_loss: 0.3509
Epoch 4/10
151/151 [==============================] - 295s 2s/step - loss: 0.3585 - val_loss: 0.3287
Epoch 5/10
151/151 [==============================] - 299s 2s/step - loss: 0.3420 - val_loss: 0.3301
Epoch 6/10
151/151 [==============================] - 297s 2s/step - loss: 0.3181 - val_loss: 0.3419
Epoch 7/10
151/151 [==============================] - 290s 2s/step - loss: 0.3131 - val_loss: 0.3201
Epoch 8/10
151/151 [==============================] - 295s 2s/step - loss: 0.3102 - val_loss: 0.3152
Epoch 9/10
151/151 [==============================] - 286s 2s/step - loss: 0.2905 - val_loss: 0.2937
Epoch 10/10
151/151 [==============================] - 270s 2s/step - loss: 0.2921 - val_loss: 0.2952
<tensorflow.python.keras.callbacks.History at 0x7fc69064bd10>
Inspecting what the network has learned
At this point, we can check how the network learned to separate the embeddings depending on whether they belong to similar images.
We can use cosine similarity to measure the similarity between embeddings.
Let's pick a sample from the dataset to check the similarity between the embeddings generated for each image.
sample = next(iter(train_dataset))
visualize(*sample)