def bilstm_model(self):
embedding_layer = Embedding(self.num_words,
self.embedding_dim,
embeddings_initializer=Constant(
self.embedding_matrix),
input_length=self.max_sequence_length,
trainable=False)
sequence_input = Input(shape=(self.max_sequence_length, ),
dtype='int32')
embedded_sequences = embedding_layer(sequence_input)
x = Bidirectional(LSTM(128, dropout=0.2,
recurrent_dropout=0.2))(embedded_sequences)
x = Dropout(0.2)(x)
x = Dense(256, activation='relu')(x)
x = Dropout(0.2)(x)
x = Dense(128, activation='relu')(x)
x = Dropout(0.2)(x)
predictions = Dense(8, activation='softmax')(x)
model = Model(sequence_input, predictions)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['acc', f1])
model._name = 'bi_model'
return model
def init_resnet_model(input_shape, num_classes):
num_hidden = 64
inp = Input(input_shape)
# block 1
conv_x = Conv1D(num_hidden, 8, padding='same')(inp)
conv_x = BatchNormalization()(conv_x)
conv_x = Activation('relu')(conv_x)
conv_y = Conv1D(num_hidden, 5, padding='same')(conv_x)
conv_y = BatchNormalization()(conv_y)
conv_y = Activation('relu')(conv_y)
conv_z = Conv1D(num_hidden, 3, padding='same')(conv_y)
conv_z = BatchNormalization()(conv_z)
## expand channels
shortcut_1 = Conv1D(num_hidden, 1, padding='same')(inp)
shortcut_1 = BatchNormalization()(shortcut_1)
outp_1 = Add()([shortcut_1, conv_z])
outp_1 = Activation('relu')(outp_1)
# block 2
conv_x = Conv1D(2 * num_hidden, 8, padding='same')(outp_1)
conv_x = BatchNormalization()(conv_x)
conv_x = Activation('relu')(conv_x)
conv_y = Conv1D(2 * num_hidden, 5, padding='same')(conv_x)
conv_y = BatchNormalization()(conv_y)
conv_y = Activation('relu')(conv_y)
conv_z = Conv1D(2 * num_hidden, 3, padding='same')(conv_y)
conv_z = BatchNormalization()(conv_z)
## expand channels
shortcut_2 = Conv1D(2 * num_hidden, 1, padding='same')(outp_1)
shortcut_2 = BatchNormalization()(shortcut_2)
outp_2 = Add()([shortcut_2, conv_z])
outp_2 = Activation('relu')(outp_2)
# block 3
conv_x = keras.layers.Conv1D(2 * num_hidden, 8, padding='same')(outp_2)
conv_x = BatchNormalization()(conv_x)
conv_x = Activation('relu')(conv_x)
conv_y = Conv1D(2 * num_hidden, 5, padding='same')(conv_x)
conv_y = BatchNormalization()(conv_y)
conv_y = Activation('relu')(conv_y)
conv_z = Conv1D(2 * num_hidden, 3, padding='same')(conv_y)
conv_z = BatchNormalization()(conv_z)
## channels are equal now
shortcut_y = BatchNormalization()(outp_2)
outp_3 = Add()([shortcut_2, conv_z])
outp_3 = Activation('relu')(outp_3)
# pooling and prediction layer
gap = GlobalAveragePooling1D()(outp_3)
outp = Dense(num_classes, activation='softmax')(gap)
model = Model(inp, outp)
model._name = 'ResNet'
return model
