How to change input shape in Sequential model in Keras

I have a sequential model that I built in Keras. I try to figure out how to change the shape of the input. In the following example

model = Sequential()
model.add(Dense(32, input_shape=(500,)))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='rmsprop',
      loss='categorical_crossentropy',
      metrics=['accuracy'])

let's say that I want to build a new model with different input shape, conceptual this should looks like this:

model1 = model
model1.layers[0] = Dense(32, input_shape=(250,))

is there a way to modify the model input shape?

Solution

Think about what changing the input shape in that situation would mean.

Your first model

model.add(Dense(32, input_shape=(500,)))

Has a dense layer that really is a 500x32 matrix.

If you changed your input to 250 elements, your layers's matrix and input dimension would mismatch.

If, however, what you were trying to achieve was to reuse your last layer's trained parameters from your first 500 element input model, you could get those weights by get_weights. Then you could rebuild a new model and set values at the new model with set_weights.

model1 = Sequential()
model1.add(Dense(32, input_shape=(250,)))
model1.add(Dense(10, activation='softmax'))
model1.layers[1].set_weights(model1.layers[1].get_weights())

Keep in mind that model1 first layer (aka model1.layers[0]) would still be untrained