零基础从20K到25K的深度学习之路--1为啥要学机器学习.

#导入相关的包

import numpy as np

np.random.seed(1337)  # for reproducibility

from keras.datasets import mnist

from keras.utils import np_utils

from keras.models import Sequential

from keras.layers import Dense, Activation

from keras.optimizers import RMSprop

      

# step1 数据采集。下载minst的数据集  

(X_train, y_train), (X_test, y_test) =    mnist.load_data()  

 

# step2 数据预处理   

X_train = X_train.reshape(X_train.shape[0], -1) /    255.   # normalize   

X_test = X_test.reshape(X_test.shape[0], -1) /    255.      # normalize   

y_train = np_utils.to_categorical(y_train,    num_classes=10)   

y_test = np_utils.to_categorical(y_test,    num_classes=10)

model = Sequential([   

                Dense(32,input_dim=784),

        Activation('relu'),

        Dense(10),

        Activation('softmax'),

])

   

rmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08,    decay=0.0)

model.compile(optimizer=rmsprop,loss='categorical_crossentropy',metrics=['accuracy'])

print('Training ------------')



# step3 开始训练模型

model.fit(X_train, y_train, epochs=2,    batch_size=32)  

print('\nTesting ------------')

    

# step4 验证模型   

loss, accuracy = model.evaluate(X_test, y_test)   

print('test loss: ', loss)   

print('test accuracy: ', accuracy)

   

# step5 根据训练结果，来优化模型。