initial commit, adds notes.md for instructions, adds data, adds python code

tests/test_siamese.py

@@ -0,0 +1,80 @@
+
+"""
+Tests for the siamese neural network module
+"""
+
+import numpy as np
+import keras
+from keras import Model, Input
+from keras.layers import Concatenate, Dense, BatchNormalization, Activation
+
+from siamese import SiameseNetwork
+
+
+def test_siamese():
+    """
+    Test that all components the siamese network work correctly by executing a
+    training run against generated data.
+    """
+
+    num_classes = 5
+    input_shape = (3,)
+    epochs = 1000
+
+    # Generate some data
+    x_train = np.random.rand(100, 3)
+    y_train = np.random.randint(num_classes, size=100)
+
+    x_test = np.random.rand(30, 3)
+    y_test = np.random.randint(num_classes, size=30)
+
+    # Define base and head model
+    def create_base_model(input_shape):
+        model_input = Input(shape=input_shape)
+
+        embedding = Dense(4)(model_input)
+        embedding = BatchNormalization()(embedding)
+        embedding = Activation(activation='relu')(embedding)
+
+        return Model(model_input, embedding)
+
+    def create_head_model(embedding_shape):
+        embedding_a = Input(shape=embedding_shape)
+        embedding_b = Input(shape=embedding_shape)
+
+        head = Concatenate()([embedding_a, embedding_b])
+        head = Dense(4)(head)
+        head = BatchNormalization()(head)
+        head = Activation(activation='sigmoid')(head)
+
+        head = Dense(1)(head)
+        head = BatchNormalization()(head)
+        head = Activation(activation='sigmoid')(head)
+
+        return Model([embedding_a, embedding_b], head)
+
+    # Create siamese neural network
+    base_model = create_base_model(input_shape)
+    head_model = create_head_model(base_model.output_shape)
+    siamese_network = SiameseNetwork(base_model, head_model)
+
+    # Prepare siamese network for training
+    siamese_network.compile(loss='binary_crossentropy',
+                            optimizer=keras.optimizers.adam())
+
+    # Evaluate network before training to establish a baseline
+    score_before = siamese_network.evaluate_generator(
+        x_train, y_train, batch_size=64
+    )
+
+    # Train network
+    siamese_network.fit(x_train, y_train,
+                        validation_data=(x_test, y_test),
+                        batch_size=64,
+                        epochs=epochs)
+
+    # Evaluate network
+    score_after = siamese_network.evaluate(x_train, y_train, batch_size=64)
+
+    # Ensure that the training loss score improved as a result of the training
+    assert(score_before > score_after)