from siamese import SiameseNetwork
# Construct Siamese network
model, base_network = SiameseNetwork()
model.load_weights('/book/working/models/siamese.h5')
def intermediate(embs):
return base_network.predict(embs)
siamese_callbacks = [
EarlyStopping(monitor='val_acc', patience=10, verbose=0),
ModelCheckpoint(siamese_checkpoint_path,
monitor='val_acc',
save_best_only=True,
verbose=0)
]
siamese_network.fit(x_train,
y_train,
validation_data=(x_test, y_test),
batch_size=1000,
epochs=epochs,
callbacks=siamese_callbacks)
siamese_network.load_weights(siamese_checkpoint_path)
embedding = base_model.outputs[-1]
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
# Add softmax layer to the pre-trained embedding network
embedding = Dense(num_classes)(embedding)
embedding = BatchNormalization()(embedding)
embedding = Activation(activation='sigmoid')(embedding)
model = Model(base_model.inputs[0], embedding)
model.compile(loss=keras.losses.binary_crossentropy,
optimizer=keras.optimizers.adam(),
metrics=['accuracy'])