Changing input layer size in Keras pertained model

Question

I'm using Inception model in Keras with the pre-trained weights of image net.

The problem is that default input size for this model is 299x299 as per Keras documentation. While my images are 230 * 350 and I don't want to resize them as it will distort the image. So I am trying to find a method to change input layer size.

Below is code is what I tried so far, however I am doubting that the image net weights are being preserved as I thing the architecture will change when I change input size.

Any ideas ..

input = Input(shape=(230, 350, 3), name='image_input')
base_model = InceptionV3(weights='imagenet', include_top=False, input_tensor=input)

x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(64, activation='relu')(x)
predictions = Dense(1, activation='sigmoid')(x)

model = Model(inputs=input, outputs=predictions)

for layer in base_model.layers:
    layer.trainable = True

model.compile(loss='binary_crossentropy',
              optimizer=Adam(lr=0.0001),
              metrics=['accuracy'])

Answer 1

Inception V3 is a fully convolutional model. You use the global pooling on the top of convolutional encoder, so slight deviation from the 299x299 should not be a big deal. If you do not have error messages with your code, it must be absolutely fine to use it like this.