import numpy as np
import pandas as pd
from keras.utils import np_utils
np.random.seed(10)#随机10
from keras.datasets import mnist
(X_train_image,Y_train_image),(X_test_label,Y_test_label) =mnist.load_data()
print(X_train_image.shape)
import matplotlib.pyplot as plt
def plot_image(image):
fig =plt.gcf()
fig.set_size_inches(2,2)
plt.imshow(image,cmap='binary')
plt.show()
plot_image(X_train_image[0])
Y_train_image[0]
import matplotlib.pyplot as plt
def plot_images_labels_prediction(images,labels,prediction,idx,num=10):
fig =plt.gcf()
fig.set_size_inches(12,14)
if num>25:
num=25
for i in range(0,num):
ax =plt.subplot(5,5,1+i)
ax.imshow(images[idx],cmap='binary')
title="labels="+str(labels[idx])
if len(prediction)>0:
title+=",prediction="+str(prediction[idx])
ax.set_title(title,fontsize=10)
ax.set_xticks([])
ax.set_yticks([])
idx+=1
plt.show()
plot_images_labels_prediction(X_train_image,Y_train_image,[],0)