import numpy as np import pandas as pd from keras.utils import np_utils from keras.datasets import mnist (x_train_image, y_train_label),(x_test_image,y_test_label)=mnist.load_data() print('train data=',len(x_train_image)) print('tesy data=',len(x_test_image))
print('x_train_image:',x_train_image.shape) print('x_test_image:',x_test_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_label[0]
def plot_images_labels_prediction(images,labels,prediction,idx,num): fig=plt.gcf() fig.set_size_inches(12,14) if num>25: numn=25 for i in range(0, num): ax=plt.subplot(5,5,i+1) ax.imshow(images[idx],cmap='binary') title="label="+str(labels[idx]) if len(prediction)>0: title+=",predict="+str(prediction[idx]) ax.set_title(title,fontsize=15) ax.set_xticks([]) ax.set_yticks([]) idx+=1 plt.imshow
plot_images_labels_prediction(x_train_image,y_train_label,[],0,10)
plot_images_labels_prediction(x_test_image,y_test_label,[],0,10)
x_Train=x_train_image.reshape(60000,784).astype('float32') x_Test=x_test_image.reshape(10000,784).astype('float32') print(x_Train.shape) print(x_Test.shape)
y_TrainOneHot=np_utils.to_categorical(y_train_label) y_TestOneHot=np_utils.to_categorical(y_test_label)