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.

# Convolution and pooling operations in TensorFlow

# Applying pooling operations in TensorFlow

Using TensorFlow, a subsampling 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...