101: Average Pool | TensorFlow | Tutorial

Welcome to this tutorial on using TensorFlow to implement average pooling in a neural network. In this tutorial, we will cover the basics of average pooling, how it works, and how to implement it using TensorFlow.

What is Average Pooling?

Pooling is a technique used in convolutional neural networks (CNNs) to reduce the spatial dimensions of the feature maps. Average pooling is one type of pooling operation where a sliding window is applied to the input feature map and the average value within the window is taken as the output.

How does Average Pooling work?

Imagine you have a feature map with dimensions 4×4 and you apply a 2×2 average pooling operation with a stride of 2. The pooling operation will slide a 2×2 window across the feature map and at each position, the average of the values within the window will be taken. This will result in a new feature map with dimensions 2×2.

Implementing Average Pooling in TensorFlow

Now let’s see how we can implement average pooling in TensorFlow. Below is a simple example of how to apply average pooling to a convolutional layer in a TensorFlow model:


import tensorflow as tf
x = tf.placeholder(tf.float32, [None, 28, 28, 1])
conv_layer = tf.layers.conv2d(x, filters=32, kernel_size=(3,3), padding='same', activation=tf.nn.relu)
average_pooling = tf.layers.average_pooling2d(conv_layer, pool_size=(2,2), strides=2)


In this example, we first define a placeholder for the input data. We then apply a convolutional layer to the input data followed by an average pooling layer. The average pooling layer takes the output of the convolutional layer as input and applies the pooling operation with a pool size of 2×2 and a stride of 2.

Conclusion

In this tutorial, we learned about average pooling, how it works, and how to implement it in a TensorFlow model. Average pooling is a useful technique for reducing the spatial dimensions of feature maps in neural networks, helping to improve computational efficiency and reduce overfitting. We hope you found this tutorial helpful and that you can now apply average pooling in your own TensorFlow projects!