Convolution and pooling operations in TensorFlow

Now that we have seen how convolutional and pooling operations are performed theoretically, let's see how we can perform these operation hands-on using TensorFlow. So let's get started.

Applying pooling operations in TensorFlow

Using TensorFlow, asubsampling layer can normally be represented by a max_pool operation by maintaining the initial parameters of the layer. For max_pool, it has the following signature in TensorFlow:

tf.nn.max_pool(value, ksize, strides, padding, data_format, name) 

Now let's learn how to create a function that utilizes the preceding signature and returns a tensor with type tf.float32, that is, the max pooled output tensor:

import tensorflow as tf
 
def maxpool2d(x, k=2): 
   return tf.nn.max_pool(x,  
               ksize=[1, k, k, 1],  
               strides=[1, k, k, 1],  
               padding='SAME') 

In the preceding code segment, the parameters can be described as follows:

• value: This is a 4D tensor of float32 elements...