I have implemented a simple neural network with keras that takes an input of 50 values and returns a classification of '0' or '1'. I believe the model is expecting an input shape of (50, 1). I'd like to add another 50 data values for each input, but I'd like them to be associated with the original 50 respective inputs. So instead of making the input of shape (100, 1), I guess I'd like to make it of shape (50, 2). I would like the neural network to know from the start that each input feature has two values associated with it, instead of it thinking there are 100 separate input features. Here's what I have so far:
model = Sequential() model.add(Dense(50, input_dim=50, kernel_initializer='normal', activation='relu')) model.add(Dense(100, kernel_initializer='normal', activation='relu')) model.add(Dense(1, kernel_initializer='normal', activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
Can anyone show me the way the alter this structure to accept my new input shape?