Transfer learning with Resnet50 fail with Exception

Hi, I am using Resnet50 to do transfer learning. The backend is tensorflow. I tried to stack three more layers on top of the Resnet but fail with following error:

Exception: The shape of the input to "Flatten" is not fully defined (got (None, None, 2048). 
Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.

The code for stacking two models are as following:

    model = ResNet50(include_top=False, weights='imagenet')

    top_model = Sequential()
    top_model.add(Flatten(input_shape=model.output_shape[1:]))
    top_model.add(Dense(256, activation='relu'))
    top_model.add(Dropout(0.5))
    top_model.add(Dense(1, activation='sigmoid'))
    top_model.load_weights(top_model_weights_path)

    model = Model(input=model.input, output=top_model(model.output))

[MrXu]

I solved the problem after reading through source code for Resnet50.py. If anyone encounter the same problem, please add the input_shape parameter when you call Resnet50():

model = ResNet50(include_top=False, weights='imagenet', input_tensor=Input(shape=(224,224,3)))

If you are using Keras ImageDataGenerator, the input shape will be automatically inferred.

[alonalj]

Regarding the error:

input size must be at least 197x197,

Had the same issue, but disappeared after upgrading keras to 2.2.4 (with backend tensorflow 1.12.0 in case it matters).