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def triplet_loss(y_true, y_pred, alpha = 0.4):
"""
Implementation of the triplet loss function
Arguments:
y_true -- true labels, required when you define a loss in Keras, you don't need it in this function.
y_pred -- python list containing three objects:
anchor -- the encodings for the anchor data
positive -- the encodings for the positive data (similar to anchor)
negative -- the encodings for the negative data (different from anchor)
Returns:
loss -- real number, value of the loss
"""
anchor = y_pred[:,0:3]
positive = y_pred[:,3:6]
negative = y_pred[:,6:9]
# distance between the anchor and the positive
pos_dist = K.sum(K.square(anchor-positive),axis=1)
# distance between the anchor and the negative
neg_dist = K.sum(K.square(anchor-negative),axis=1)
# compute loss
basic_loss = pos_dist-neg_dist+alpha
loss = K.maximum(basic_loss,0.0)
return loss
def create_base_network(in_dims, out_dims):
"""
Base network to be shared.
"""
model = Sequential()
model.add(BatchNormalization(input_shape=in_dims))
model.add(LSTM(512, return_sequences=True, dropout=0.2, recurrent_dropout=0.2, implementation=2))
model.add(LSTM(512, return_sequences=False, dropout=0.2, recurrent_dropout=0.2, implementation=2))
model.add(BatchNormalization())
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dense(out_dims, activation='linear'))
model.add(BatchNormalization())
return model
in_dims = (N_MINS, n_feat)
out_dims = N_FACTORS
# Network definition
with tf.device(tf_device):
# Create the 3 inputs
anchor_in = Input(shape=in_dims)
pos_in = Input(shape=in_dims)
neg_in = Input(shape=in_dims)
# Share base network with the 3 inputs
base_network = create_base_network(in_dims, out_dims)
anchor_out = base_network(anchor_in)
pos_out = base_network(pos_in)
neg_out = base_network(neg_in)
merged_vector = concatenate([anchor_out, pos_out, neg_out], axis=-1)
# Define the trainable model
model = Model(inputs=[anchor_in, pos_in, neg_in], outputs=merged_vector)
model.compile(optimizer=Adam(),
loss=triplet_loss)
# Training the model
model.fit(train_data, y_dummie, batch_size=256, epochs=10)
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