CNN.py

DROPOUT_RATE = 0.3
LEARNING_RATE = 0.00005
NUM_EPOCHS = 10
BATCH_SIZE = 128

#Create CNN Layers
sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')
embedding_layer = Embedding(len(tokenizer.word_index) + 1,
                                EMBEDDINGS_DIMENSION,
                                weights=[embedding_matrix],
                                input_length=MAX_SEQUENCE_LENGTH,
                                trainable=False)
x = embedding_layer(sequence_input)
x = Conv1D(128, 2, activation='relu', padding='same')(x)
x = MaxPooling1D(5, padding='same')(x)
x = Conv1D(128, 3, activation='relu', padding='same')(x)
x = MaxPooling1D(5, padding='same')(x)
x = Conv1D(128, 4, activation='relu', padding='same')(x)
x = MaxPooling1D(40, padding='same')(x)
x = Flatten()(x)
x = Dropout(DROPOUT_RATE)(x)
x = Dense(128, activation='relu')(x)
preds = Dense(2, activation='softmax')(x)

# Compile model.
print('compiling model')

# model = Model(input_layer, output_layer)
model = Model(sequence_input, preds)
model.compile(loss='categorical_crossentropy', optimizer=RMSprop(lr=LEARNING_RATE), metrics=['acc'])

keras.utils.plot_model(model, "multi_input_and_output_model.png", show_shapes=True)