# Matplotlib Show Images Processed by CNN Networks – Deep Learning Tutorial

By | August 20, 2020

We often use CNN networks to process images in deep learning. Here is an example:

Implement TensorFlow CNN Networks for MNIST Handwritten Digits Classification

However, how to see the effect of images processd by CNN networks? In this tutorial, we will introduce the way to use python matplotlib to display.

## How python matplotlib display images

We can use matplotlib.pyplot.imshow() function to display images. Here is the tutorial:

Understand matplotlib.pyplot.imshow(): Display Data as an Image – Matplotlib Tutorial

Then we will introduce the way to use python matplotlib to display images processed by tensorflow cnn networks.

We will use an image in MNIST dataset to make an example.

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import os
import numpy as np
import matplotlib.pyplot as plt

mnist = input_data.read_data_sets(os.getcwd() + "/MNIST-data/", one_hot=True)

x, y = mnist.train.next_batch(10)
print(y[2])
x = x[2]

We will find:

y[2] is: [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.], which means y[2] = 6

x[2] contains the data of image

We will use a cnn network to process x[2] and display the processed image using matplotlib.

## Build a tensorflow cnn network to process image

x = tf.convert_to_tensor(x)
X_images = tf.reshape(x, [-1, 28, 28, 1])

conv1_Weights = tf.Variable(tf.truncated_normal([5, 5, 1, 3], stddev=0.1), name='conv1_Weights')
# out_channels = 3
conv1_biases = tf.Variable(tf.constant(0.1, shape=[3]), name='conv1_biases')
#[batch, out_height, out_width, out_channels]
#out_channels = 3
conv1_conv2d = tf.nn.conv2d(X_images, conv1_Weights, strides=[1, 1, 1, 1], padding='SAME') + conv1_biases
conv1_activated = tf.nn.relu(conv1_conv2d)
#[batch, out_height, out_width, channels]
# channels = 3
conv1_pooled = tf.nn.max_pool(conv1_activated, ksize=[1, 2, 2, 1], strides=[1, 1, 1, 1], padding='SAME')


You should understand how to use tf.nn.conv2d() and tf.nn.max_pool(). Here are tutorials:

Understand tf.nn.conv2d(): Compute a 2-D Convolution in TensorFlow – TensorFlow Tutorial

Understand TensorFlow tf.nn.max_pool(): Implement Max Pooling for Convolutional Network – TensorFlow Tutorial

We must set the out_channels of cnn filter is 1, 3 or 4, because the matplotlib.pyplot.imshow() function only can receive (M,N), (M, N, 3) or (M, N, 4) data.

Then we can display conv1_pooled, which contains the data of image processed by cnn network.

## Display Image using matplotlib.pyplot.imshow()

First, we use numpy.squeeze() to remove the axis with length = 1. Here is the tutorial:

Understand numpy.squeeze(): Remove Dimensions of Length 1 – NumPy Tutorial

Then we can display the processed image.

init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
data = sess.run(conv1_pooled)
#the shape of data is (1, 14, 14, 32)
print(np.shape(data))
data = np.squeeze(data)
print(np.shape(data))

plt.imshow(data, cmap='Greys')
plt.show()

The image is:

Although we set cmap=’Greys’, we can find the image is colorful. Why?

Because the out_channels of cnn filter is 3, which means the processed image contains red, green and blue.

If we set the out_channels of cnn filter is 1.

conv1_Weights = tf.Variable(tf.truncated_normal([5, 5, 1, 1], stddev=0.1), name='conv1_Weights')
# out_channels = 32
conv1_biases = tf.Variable(tf.constant(0.1, shape=[1]), name='conv1_biases')

The processed image will be grey.