i=0
prop_class=[]
mis_class=[]

for i in range(len(Y_test)):
    if(np.argmax(Y_test[i])==np.argmax(Y_pred_tta[i])):
        prop_class.append(i)
    if(len(prop_class)==8):
        break

i=0
for i in range(len(Y_test)):
    if(not np.argmax(Y_test[i])==np.argmax(Y_pred_tta[i])):
        mis_class.append(i)
    if(len(mis_class)==8):
        break

w=60
h=40
fig=plt.figure(figsize=(18, 10))
columns = 4
rows = 2

def Transfername(namecode):
    if namecode==0:
        return "Benign"
    else:
        return "Malignant"

for i in range(len(prop_class)):
    ax = fig.add_subplot(rows, columns, i+1)
    ax.set_title("Predicted result:"+ Transfername(np.argmax(Y_pred_tta[prop_class[i]]))
                       +"\n"+"Actual result: "+ Transfername(np.argmax(Y_test[prop_class[i]])))
    plt.imshow(X_test[prop_class[i]], interpolation='nearest')
plt.show()