Hello. I m learning cnn’s and i have missing point about MobileNet V2 base output shape.
I m using this link as my reference: Transfer learning and fine-tuning  |  TensorFlow Core 1
In the code I print the summary of base_model :
“base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,
include_top=False,
weights=‘imagenet’)
base_model.summary()”
In last line of layers “output shape” is (None, 5, 5, 1280)
This is the output shape of base_model. So I expected to see (1,5,5,1280) shaped output for one image. However, when ı run:
" feature_batch = base_model(image)
print(feature_batch.shape)"
output is (32,5,5,1280) why there are 32 different layers in first dimension and why that 32 is not included in output shape of last layer?