activation='elu',
padding='same',
input_shape=(max_no_tokens, vector_size)))
model.add(Conv1D(32, kernel_size=3, activation='elu', padding='same'))
model.add(Conv1D(32, kernel_size=3, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=3))
model.add(Bidirectional(LSTM(512, dropout=0.2, recurrent_dropout=0.3)))
model.add(Dense(512, activation='sigmoid'))
model.add(Dropout(0.2))
model.add(Dense(512, activation='sigmoid'))
model.add(Dropout(0.25))
model.add(Dense(512, activation='sigmoid'))
model.add(Dropout(0.25))
model.add(Dense(2, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=0.0001, decay=1e-6),
metrics=['accuracy'])
tensorboard = TensorBoard(log_dir='logs/',
histogram_freq=0,
write_graph=True,
write_images=True)
model.summary()
# Traininig
model.fit(x_train,
y_train,
batch_size=batch_size,
shuffle=True,
epochs=no_epochs,
validation_data=(x_test, y_test),
