My question is a different from these questions as it's related to a built-in model.
Keras cnn model output shape doesn't match model summary
Keras - CNN Model Summary Diemension Interpretation
I'm trying to examine the built-in Xception model for transfer learning and used model.summary():
Here are the first few layers of the Xception model
Layer (type) Output Shape Param Connected to
==================================================================================================
input_1 (InputLayer) (None, None, None, 3 0
__________________________________________________________________________________________________
block1_conv1 (Conv2D) (None, None, None, 3 864 input_1[0][0]
__________________________________________________________________________________________________
block1_conv1_bn (BatchNormaliza (None, None, None, 3 128 block1_conv1[0][0]
______________________________________________________________________________________
My concern is with the depth channel. Shouldn't the depth output of the block1_conv1 (Conv2D) be 32 and not 3?
If I figure out the output via get_layer, I get the correct output:
base_model.get_layer(index = 1).output
<tf.Tensor 'block1_conv1/convolution:0' shape=(?, ?, ?, 32) dtype=float32>
base_model = Xception(weights='imagenet', include_top=False)
print(base_model.summary())