test_digit.py

from keras import backend as K
import numpy as p


output = model.predict_classes(x_test, verbose=1)


wrong_classified = []
wrong_classified_value = []
wrong_classified_real = []


for i in range(0, 10000):
    if output[i] != numpy.argmax(y_test[i]):
        wrong_classified.append( x_test[i] )
        wrong_classified_value.append( output[i] )
        wrong_classified_real.append( numpy.argmax(y_test[i]) )
        

for idx, val in enumerate( wrong_classified_value ):
        print( idx , " - wrong classified ---> ", val , "Real value --> ",wrong_classified_real[idx])


def image_data(arr):
    two_d = (np.reshape(arr, (28, 28)) * 255).astype(np.uint8)
    return two_d


sub_plot_rows = 3
sub_plot_columns = 6
f, axarr = plt.subplots(sub_plot_rows,sub_plot_columns)

for i in range(0, sub_plot_rows):
    k = 0;
    for j in range(0, sub_plot_columns):
        title = str(wrong_classified_real[ k  * 2 + j]) + " - " + str( wrong_classified_value[k * 2 + j]) 
        axarr[i,j].get_xaxis().set_visible(False)
        axarr[i,j].get_yaxis().set_visible(False)
        
        axarr[i,j].set_title( title );
        axarr[i,j].imshow(image_data(wrong_classified[ k * 2 + j]), interpolation='nearest' )