Predicting Noisy Images - Explore the Data

First, we will print the shape of the variables we created in the previous step:

print(X_train.<< your code goes here >>)
print(y_train.shape)
print(<< your code goes here >>.shape)
print(y_test.shape)

Next, we will use the showImage() function we created earlier to view the first image in the training set and it's corresponsing label:

<< your code goes here >>(X_train[0])
<< your code goes here >>[0]

Finally, we will view few more images from the dataset:

plt.figure(figsize=(10,10))
for i in range(15):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    array_image = X_train[i].reshape(28, 28)
    plt.imshow(array_image, cmap = matplotlib.cm.binary, interpolation="nearest")
plt.show()