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blackboxes.py
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blackboxes.py
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import keras
def build_resnet(n_timesteps, n_features):
n_feature_maps = 64
input_layer = keras.layers.Input((n_timesteps, 1))
# BLOCK 1
conv_x = keras.layers.Conv1D(filters=n_feature_maps, kernel_size=8, padding='same')(input_layer)
conv_x = keras.layers.normalization.BatchNormalization()(conv_x)
conv_x = keras.layers.Activation('relu')(conv_x)
conv_y = keras.layers.Conv1D(filters=n_feature_maps, kernel_size=5, padding='same')(conv_x)
conv_y = keras.layers.normalization.BatchNormalization()(conv_y)
conv_y = keras.layers.Activation('relu')(conv_y)
conv_z = keras.layers.Conv1D(filters=n_feature_maps, kernel_size=3, padding='same')(conv_y)
conv_z = keras.layers.normalization.BatchNormalization()(conv_z)
# expand channels for the sum
shortcut_y = keras.layers.Conv1D(filters=n_feature_maps, kernel_size=1, padding='same')(input_layer)
shortcut_y = keras.layers.normalization.BatchNormalization()(shortcut_y)
output_block_1 = keras.layers.add([shortcut_y, conv_z])
output_block_1 = keras.layers.Activation('relu')(output_block_1)
# BLOCK 2
conv_x = keras.layers.Conv1D(filters=n_feature_maps*2, kernel_size=8, padding='same')(output_block_1)
conv_x = keras.layers.normalization.BatchNormalization()(conv_x)
conv_x = keras.layers.Activation('relu')(conv_x)
conv_y = keras.layers.Conv1D(filters=n_feature_maps*2, kernel_size=5, padding='same')(conv_x)
conv_y = keras.layers.normalization.BatchNormalization()(conv_y)
conv_y = keras.layers.Activation('relu')(conv_y)
conv_z = keras.layers.Conv1D(filters=n_feature_maps*2, kernel_size=3, padding='same')(conv_y)
conv_z = keras.layers.normalization.BatchNormalization()(conv_z)
# expand channels for the sum
shortcut_y = keras.layers.Conv1D(filters=n_feature_maps*2, kernel_size=1, padding='same')(output_block_1)
shortcut_y = keras.layers.normalization.BatchNormalization()(shortcut_y)
output_block_2 = keras.layers.add([shortcut_y, conv_z])
output_block_2 = keras.layers.Activation('relu')(output_block_2)
# BLOCK 3
conv_x = keras.layers.Conv1D(filters=n_feature_maps*2, kernel_size=8, padding='same')(output_block_2)
conv_x = keras.layers.normalization.BatchNormalization()(conv_x)
conv_x = keras.layers.Activation('relu')(conv_x)
conv_y = keras.layers.Conv1D(filters=n_feature_maps*2, kernel_size=5, padding='same')(conv_x)
conv_y = keras.layers.normalization.BatchNormalization()(conv_y)
conv_y = keras.layers.Activation('relu')(conv_y)
conv_z = keras.layers.Conv1D(filters=n_feature_maps*2, kernel_size=3, padding='same')(conv_y)
conv_z = keras.layers.normalization.BatchNormalization()(conv_z)
# no need to expand channels because they are equal
shortcut_y = keras.layers.normalization.BatchNormalization()(output_block_2)
output_block_3 = keras.layers.add([shortcut_y, conv_z])
output_block_3 = keras.layers.Activation('relu')(output_block_3)
# FINAL
gap_layer = keras.layers.GlobalAveragePooling1D()(output_block_3)
output_layer = keras.layers.Dense(n_features, activation='softmax')(gap_layer)
model = keras.models.Model(inputs=input_layer, outputs=output_layer)
model.compile(loss='sparse_categorical_crossentropy', optimizer=keras.optimizers.Adam(), metrics=['accuracy'])
return model
def build_simple_CNN(n_timesteps, n_outputs):
model = keras.models.Sequential()
model.add(keras.layers.Conv1D(filters=16, kernel_size=8, activation='relu', input_shape=(n_timesteps,1)))
model.add(keras.layers.normalization.BatchNormalization())
model.add(keras.layers.Activation('relu'))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv1D(filters=32, kernel_size=5, activation='relu'))
model.add(keras.layers.normalization.BatchNormalization())
model.add(keras.layers.Activation('relu'))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu'))
model.add(keras.layers.normalization.BatchNormalization())
model.add(keras.layers.Activation('relu'))
model.add(keras.layers.Dropout(0.3))
model.add(keras.layers.GlobalAveragePooling1D())
#model.add(Dense(30, activation='relu'))
model.add(keras.layers.Dense(n_outputs, activation='sigmoid'))
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
def build_resnet_dropout(n_timesteps, n_features):
n_feature_maps = 64
input_layer = keras.layers.Input((n_timesteps, 1))
# BLOCK 1
conv_x = keras.layers.Conv1D(filters=n_feature_maps, kernel_size=8, padding='same')(input_layer)
conv_x = keras.layers.normalization.BatchNormalization()(conv_x)
conv_x = keras.layers.Activation('relu')(conv_x)
conv_y = keras.layers.Conv1D(filters=n_feature_maps, kernel_size=5, padding='same')(conv_x)
conv_y = keras.layers.normalization.BatchNormalization()(conv_y)
conv_y = keras.layers.Activation('relu')(conv_y)
conv_z = keras.layers.Conv1D(filters=n_feature_maps, kernel_size=3, padding='same')(conv_y)
conv_z = keras.layers.normalization.BatchNormalization()(conv_z)
# expand channels for the sum
shortcut_y = keras.layers.Conv1D(filters=n_feature_maps, kernel_size=1, padding='same')(input_layer)
shortcut_y = keras.layers.normalization.BatchNormalization()(shortcut_y)
output_block_1 = keras.layers.add([shortcut_y, conv_z])
output_block_1 = keras.layers.Activation('relu')(output_block_1)
output_block_1 = keras.layers.Dropout(0.3)(output_block_1)
# BLOCK 2
conv_x = keras.layers.Conv1D(filters=n_feature_maps*2, kernel_size=8, padding='same')(output_block_1)
conv_x = keras.layers.normalization.BatchNormalization()(conv_x)
conv_x = keras.layers.Activation('relu')(conv_x)
conv_y = keras.layers.Conv1D(filters=n_feature_maps*2, kernel_size=5, padding='same')(conv_x)
conv_y = keras.layers.normalization.BatchNormalization()(conv_y)
conv_y = keras.layers.Activation('relu')(conv_y)
conv_z = keras.layers.Conv1D(filters=n_feature_maps*2, kernel_size=3, padding='same')(conv_y)
conv_z = keras.layers.normalization.BatchNormalization()(conv_z)
# expand channels for the sum
shortcut_y = keras.layers.Conv1D(filters=n_feature_maps*2, kernel_size=1, padding='same')(output_block_1)
shortcut_y = keras.layers.normalization.BatchNormalization()(shortcut_y)
output_block_2 = keras.layers.add([shortcut_y, conv_z])
output_block_2 = keras.layers.Activation('relu')(output_block_2)
output_block_2 = keras.layers.Dropout(0.3)(output_block_2)
# BLOCK 3
conv_x = keras.layers.Conv1D(filters=n_feature_maps*2, kernel_size=8, padding='same')(output_block_2)
conv_x = keras.layers.normalization.BatchNormalization()(conv_x)
conv_x = keras.layers.Activation('relu')(conv_x)
conv_y = keras.layers.Conv1D(filters=n_feature_maps*2, kernel_size=5, padding='same')(conv_x)
conv_y = keras.layers.normalization.BatchNormalization()(conv_y)
conv_y = keras.layers.Activation('relu')(conv_y)
conv_z = keras.layers.Conv1D(filters=n_feature_maps*2, kernel_size=3, padding='same')(conv_y)
conv_z = keras.layers.normalization.BatchNormalization()(conv_z)
# no need to expand channels because they are equal
shortcut_y = keras.layers.normalization.BatchNormalization()(output_block_2)
output_block_3 = keras.layers.add([shortcut_y, conv_z])
output_block_3 = keras.layers.Activation('relu')(output_block_3)
output_block_3 = keras.layers.Dropout(0.3)(output_block_3)
# FINAL
gap_layer = keras.layers.GlobalAveragePooling1D()(output_block_3)
output_layer = keras.layers.Dense(n_features, activation='softmax')(gap_layer)
model = keras.models.Model(inputs=input_layer, outputs=output_layer)
model.compile(loss='sparse_categorical_crossentropy', optimizer=keras.optimizers.Adam(), metrics=['accuracy'])
return model