How to change input shape in Sequential model in Keras

Question

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?

Answer 1

Here is another solution without defining each layer of the model from scratch. The key for me was to use "_layers" instead of "layers". The latter only seems to return a copy.

import keras
import numpy as np

def get_model():
    old_input_shape = (20, 20, 3)
    model = keras.models.Sequential()
    model.add(keras.layers.Conv2D(9, (3, 3), padding="same", input_shape=old_input_shape))
    model.add(keras.layers.MaxPooling2D((2, 2)))
    model.add(keras.layers.Flatten())
    model.add(keras.layers.Dense(1, activation="sigmoid"))
    model.compile(loss='binary_crossentropy', optimizer=keras.optimizers.Adam(lr=0.0001), metrics=['acc'], )
    model.summary()
    return model

def change_model(model, new_input_shape=(None, 40, 40, 3)):
    # replace input shape of first layer
    model._layers[1].batch_input_shape = new_input_shape

    # feel free to modify additional parameters of other layers, for example...
    model._layers[2].pool_size = (8, 8)
    model._layers[2].strides = (8, 8)

    # rebuild model architecture by exporting and importing via json
    new_model = keras.models.model_from_json(model.to_json())
    new_model.summary()

    # copy weights from old model to new one
    for layer in new_model.layers:
        try:
            layer.set_weights(model.get_layer(name=layer.name).get_weights())
        except:
            print("Could not transfer weights for layer {}".format(layer.name))

    # test new model on a random input image
    X = np.random.rand(10, 40, 40, 3)
    y_pred = new_model.predict(X)
    print(y_pred)

    return new_model

if __name__ == '__main__':
    model = get_model()
    new_model = change_model(model)

Answer 2

Somewhat related, so hopefully someone will find this useful: If you have an existing model where the input is a placeholder that looks like (None, None, None, 3) for example, you can load the model, replace the first layer with a concretely shaped input. Transformation of this kind is very useful when for example you want to use your model in iOS CoreML (In my case the input of the model was a MLMultiArray instead of CVPixelBuffer, and the model compilation failed)

from keras.models import load_model
from keras import backend as K
from keras.engine import InputLayer
import coremltools

model = load_model('your_model.h5')

# Create a new input layer to replace the (None,None,None,3) input layer :
input_layer = InputLayer(input_shape=(272, 480, 3), name="input_1")

# Save and convert :
model.layers[0] = input_layer
model.save("reshaped_model.h5")    
coreml_model = coremltools.converters.keras.convert('reshaped_model.h5')    
coreml_model.save('MyPredictor.mlmodel')