tf.keras.layers.Conv2D usage

I am new to CNN and tensorflow. It has been about 2 days since working through Geron's Hands-on TF book. I'd appreciate if someone can help me.

Goal: Understand how and why keras official documentation (https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D) uses Conv2D (m,n...) notation.

Problem: I wrote two sets of code. I understood the first one, which uses explicit set of declaration for number of filters and kernel_size.

TF and Python Version:

sys.version  Out: '3.7.9 (default, Aug 31 2020, 17:10:11) [MSC v.1916 64 bit (AMD64)]'  tf.__version__  Out: '2.3.0'  

Code 1:

import tensorflow as tf    input_shape = (4, 30, 60, 3) #Sample 30x60 images with RGB channel. `batch_size` = 4    a1=tf.keras.layers.Conv2D(filters=10,kernel_size=(3,3), input_shape=input_shape[1:])  a1(tf.random.normal(input_shape)).shape  a1.filters  a1.kernel_size    model = tf.keras.Sequential()  model.add(a1)  model.output_shape  model.summary()  

Output:

Out[99]: TensorShape([4, 28, 58, 10]) #we are not using padding. So, the shape of tensor is 4 batch x 28x58 x 10 filters  Out[99]: 10 # number of filters  Out[99]: (3, 3) #kernel size   Out[99]: (None, 28, 58, 10) #this is the feature map for one image  Model: "sequential_24"  _________________________________________________________________  Layer (type)                 Output Shape              Param #     =================================================================  conv2d_34 (Conv2D)           (None, 28, 58, 10)        280         =================================================================  Total params: 280  Trainable params: 280  Non-trainable params: 0  _________________________________________________________________  

I am good with above output. I've added my thoughts above.

Code 2: Now, I modified the code above to not explicitly mention kernel_size and filters as per official documentation above.

a2=tf.keras.layers.Conv2D(10,3,3, input_shape=input_shape[1:]) #here's the change.  a2(tf.random.normal(input_shape)).shape  a2.filters  a2.kernel_size    model = tf.keras.Sequential()  model.add(a2)  model.output_shape  model.summary()  

Output:

Out[100]: TensorShape([4, 10, 20, 10])  Out[100]: 10  Out[100]: (3, 3)  Out[100]: (None, 10, 20, 10)  Model: "sequential_25"  _________________________________________________________________  Layer (type)                 Output Shape              Param #     =================================================================  conv2d_35 (Conv2D)           (None, 10, 20, 10)        280         =================================================================  Total params: 280  Trainable params: 280  Non-trainable params: 0  _________________________________________________________________  

As we can see, the only difference is that Code 1 uses Conv2D(filters=10,kernel_size=(3,3),... while Code 2 uses Conv2D(10,3,3,.... Moreover filters and kernel_size are also the same. However, the output_shape are completely different.

Why is this? Can someone please explain? I couldn't find anything on keras official documentation (https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D).

https://stackoverflow.com/questions/65417415/tf-keras-layers-conv2d-usage December 23, 2020 at 08:03AM