1 import numpy as np 2 from keras.datasets import mnist 3 from keras.utils import np_utils 4 from keras.models import Sequential 5 from keras.layers import Dense,Dropout 6 from keras.optimizers import SGD
# 载入数据 (x_train,y_train),(x_test,y_test) = mnist.load_data() # (60000,28,28) print('x_shape:',x_train.shape) # (60000) print('y_shape:',y_train.shape) # (60000,28,28)->(60000,784) x_train = x_train.reshape(x_train.shape[0],-1)/255.0 x_test = x_test.reshape(x_test.shape[0],-1)/255.0 # 换one hot格式 y_train = np_utils.to_categorical(y_train,num_classes=10) y_test = np_utils.to_categorical(y_test,num_classes=10) # 创建模型 model = Sequential([ Dense(units=200,input_dim=784,bias_initializer='one',activation='tanh'), Dropout(0.4), Dense(units=100,bias_initializer='one',activation='tanh'), Dropout(0.4), Dense(units=10,bias_initializer='one',activation='softmax') ]) # 定义优化器 sgd = SGD(lr=0.2) # 定义优化器，loss function，训练过程中计算准确率 model.compile( optimizer = sgd, loss = 'categorical_crossentropy', metrics=['accuracy'], ) # 训练模型 model.fit(x_train,y_train,batch_size=32,epochs=10) # 评估模型 loss,accuracy = model.evaluate(x_test,y_test) print(' test loss',loss) print('test accuracy',accuracy) loss,accuracy = model.evaluate(x_train,y_train) print('train loss',loss) print('train accuracy',accuracy)
