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 6 from keras.optimizers import SGD 7 from keras.regularizers import l2
# 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',kernel_regularizer=l2(0.0003)), Dense(units=100,bias_initializer='one',activation='tanh',kernel_regularizer=l2(0.0003)), Dense(units=10,bias_initializer='one',activation='softmax',kernel_regularizer=l2(0.0003)) ]) # 定义优化器 = the SGD SGD (LR = 0.2 ) # define optimizer, loss function, the calculation accuracy of the training process model.compile ( Optimizer = SGD, Loss = ' categorical_crossentropy ' , metrics = [ ' Accuracy ' ], ) # training model model. Fit (x_train, y_train, the batch_size = 32, = 10 epochs ) # assessment model Loss, Accuracy = model.evaluate (x_test, android.permission.FACTOR.) Print ( ' \ NTEST Loss ' , Loss) Print ( ' Test Accuracy ' , Accuracy) loss,accuracy = model.evaluate(x_train,y_train) print('train loss',loss) print('train accuracy',accuracy)