plot.py

def visualize_images(df, img_size, number_of_images, name):
    plt.figure(figsize=(8,8))
    
    n_rows = df.shape[0]
    f = plt.figure(figsize=(15,15)) # defining a figure
    reshaped_df = df.reshape(df.shape[0], img_size, img_size)
    number_of_rows = number_of_images/5 if number_of_images%5 == 0 else (number_of_images/5) +1
    for i in range(number_of_images):        
        f.add_subplot(number_of_rows, 5, i+1, xticks=[], yticks=[])
        #plt.figure(figsize = (7,7))
        plt.title(np.argmax(name[i]), color = 'blue', fontdict = {'size' : '25'})
        plt.imshow(reshaped_df[i], cmap='gray')

        
def visualize_input(img, ax):
    img = img.reshape(32, 32)
    ax.imshow(img, cmap='gray')
    width, height = img.shape
    thresh = img.max()/2.5
    for x in range(width):
        for y in range(height):
            ax.annotate(str(round(img[x][y],2)), xy=(y,x),
                        horizontalalignment='center',
                        verticalalignment='center',
                        color='white' if img[x][y]<thresh else 'black')

fig = plt.figure(figsize = (15,15)) 
ax = fig.add_subplot(111, xticks=[], yticks=[])
visualize_input(X_train[0], ax)