Keras_model.py

# build the model
keras_model = Sequential()
keras_model.add(Embedding(vocab_size, output_dim = emb_dim, input_length=max_len))
keras_model.add(Dropout(dropout_rate))
keras_model.add(Conv1D(50, 3, activation='relu', padding='same', strides=1))
keras_model.add(MaxPool1D())
keras_model.add(Dropout(dropout_rate))
keras_model.add(Conv1D(100, 3, activation='relu', padding='same', strides=1))
keras_model.add(MaxPool1D())
keras_model.add(Dropout(dropout_rate))
keras_model.add(Conv1D(200, 3, activation='relu', padding='same', strides=1))
keras_model.add(GlobalMaxPool1D())
keras_model.add(Dropout(dropout_rate))
keras_model.add(Dense(100))
keras_model.add(Activation('relu'))
keras_model.add(Dropout(dropout_rate))
keras_model.add(Dense(n_labels))
keras_model.add(Activation('softmax'))

keras_model.compile(loss=loss, metrics=[metric], optimizer=opt)
keras_model.summary()