Keras学习手册（二）

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)