Consider the Output of the Following Output Produced by Calling

Consider the output of the following output produced by calling a Keras model's summary method: _________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
Conv2d_1 (Conv2D) (None, 26, 26, 64) 640
_________________________________________________________________
Max_pooling2d_1 (MaxPooling2 (None, 13, 13, 64) 0
_________________________________________________________________
Conv2d_2 (Conv2D) (None, 11, 11, 128) 73856
_________________________________________________________________
Max_pooling2d_2 (MaxPooling2 (None, 5, 5, 128) 0
_________________________________________________________________
Flatten_1 (Flatten) (None, 3200) 0
_________________________________________________________________
Dense_1 (Dense) (None, 128) 409728
_________________________________________________________________
Dense_2 (Dense) (None, 10) 1290
=================================================================
Total params: 485,514
Trainable params: 485,514
Non-trainable params: 0
_________________________________________________________________
Which of the following statements is false?

A) A model's summary method shows you the model's layers.
B) The parameters are the weights that the network learns during training. Our relatively small convnet, needs to learn nearly 500,000 parameters.
C) In the Output Shape column, None simply means that the model does not know in advance how many training samples you're going to provide-this is known only when you start the training.
D) By default, Keras trains only the parameters that most affect prediction accuracy.