Visualize CNN Filter

#Import required libraries import numpy as np import pandas as pd import tensorflow as tf from tensorflow import keras import matplotlib.pyplot as plt from tensorflow.keras.layers import Conv2D, MaxPooling2D from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Flatten

# Load the MNIST dataset from keras dataset mnist = tf.keras.datasets.mnist

# Load the data and split (X_train,y_train),(X_test,y_test) = mnist.load_data()

# Check the shape of training and testing data X_train.shape, y_train.shape, X_test.shape, y_test.shape

# Visualize one single data to check the digit plt.imshow(X_train[0])

# Create a simple 10 filters CNN layer model = Sequential() model.add(Conv2D(10,(3,3),input_shape=(28,28,1),activation='relu'))

# Grab a single data and then predict to get weights data = X_train[0] # reshape the data to 1,28,28,1 data = data.reshape(1,28,28,1)

# Get the weights yhat = model.predict(data)

# Check the shape of yhat yhat.shape

# Reshape the yhat to display it or visualize it yhat = yhat.reshape(26,26,10)

# Visualize all the 10 filters for i in range(10): plt.imshow(yhat[:,:,i]) plt.show()