81 lines
2.4 KiB
Python
81 lines
2.4 KiB
Python
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"""
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Tests for the siamese neural network module
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"""
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import numpy as np
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import keras
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from keras import Model, Input
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from keras.layers import Concatenate, Dense, BatchNormalization, Activation
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from siamese import SiameseNetwork
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def test_siamese():
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"""
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Test that all components the siamese network work correctly by executing a
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training run against generated data.
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"""
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num_classes = 5
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input_shape = (3,)
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epochs = 1000
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# Generate some data
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x_train = np.random.rand(100, 3)
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y_train = np.random.randint(num_classes, size=100)
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x_test = np.random.rand(30, 3)
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y_test = np.random.randint(num_classes, size=30)
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# Define base and head model
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def create_base_model(input_shape):
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model_input = Input(shape=input_shape)
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embedding = Dense(4)(model_input)
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embedding = BatchNormalization()(embedding)
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embedding = Activation(activation='relu')(embedding)
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return Model(model_input, embedding)
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def create_head_model(embedding_shape):
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embedding_a = Input(shape=embedding_shape)
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embedding_b = Input(shape=embedding_shape)
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head = Concatenate()([embedding_a, embedding_b])
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head = Dense(4)(head)
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head = BatchNormalization()(head)
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head = Activation(activation='sigmoid')(head)
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head = Dense(1)(head)
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head = BatchNormalization()(head)
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head = Activation(activation='sigmoid')(head)
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return Model([embedding_a, embedding_b], head)
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# Create siamese neural network
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base_model = create_base_model(input_shape)
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head_model = create_head_model(base_model.output_shape)
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siamese_network = SiameseNetwork(base_model, head_model)
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# Prepare siamese network for training
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siamese_network.compile(loss='binary_crossentropy',
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optimizer=keras.optimizers.adam())
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# Evaluate network before training to establish a baseline
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score_before = siamese_network.evaluate_generator(
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x_train, y_train, batch_size=64
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)
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# Train network
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siamese_network.fit(x_train, y_train,
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validation_data=(x_test, y_test),
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batch_size=64,
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epochs=epochs)
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# Evaluate network
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score_after = siamese_network.evaluate(x_train, y_train, batch_size=64)
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# Ensure that the training loss score improved as a result of the training
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assert(score_before > score_after)
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