Is there any method to plot vector(matrix) values after each NN layer

Question

I modified the existing activation function and using it in the Convolutional layer of the Neural Network. I would like to know how does it perform compared to the existing activation function.Is there any method/function to plot in a graph the results(matrix values) after each Neural network layer,so that I could customise my activation function according to the values for better results?

model = Sequential()
e = Embedding(vocab_size, 100, weights=[embedding_matrix], input_length=max_length, trainable=False)
model.add(e)
model.add(Conv1D(64,kernel_size,padding='valid',activation=newactivation,strides=1))
model.add(MaxPooling1D(pool_size=pool_size))
model.add(Conv1D(256,kernel_size,padding='valid',activation=newactivation,strides=1))
model.add(MaxPooling1D(pool_size=pool_size))
model.add(Bidirectional(GRU(gru_output_size, dropout=0.2, recurrent_dropout=0.2)))
model.add(Bidirectional(LSTM(lstm_output_size)))
model.add(Dense(nclass, activation='softmax'))
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])
print(model.summary())
model.fit(padded_docs,y_train, epochs=epoch_size, verbose=0)
loss, accuracy = model.evaluate(tpadded_docs, y_test, verbose=0)    

Answer 1

I cannot comment yet so I post this as an answer:

Refer to the Keras FAQ: "How can I obtain the output of an intermediate layer?"

It shows you how you can access the output of each layer. If you are using the version that uses the keras function , you can even access the output of the model in the learning phase (if your model contains layer that behave differently in training vs. testing).