import numpy as np from keras.datasets import mnist from keras.utils import np_utils from keras.models import Sequential from keras.layers import Dense from keras.layers.recurrent import SimpleRNN from keras.optimizers import Adam # 数据长
import numpy as np from keras.datasets import mnist from keras.utils import np_utils from keras.models import Sequential from keras.layers import Dense from keras.layers.recurrent import SimpleRNN from keras.optimizers import Adam
# 数据长度-一行有28个像素 input_size = 28 # 序列长度-一共有28行 time_steps = 28 # 隐藏层cell个数 cell_size = 50 # 载入数据 (x_train,y_train),(x_test,y_test) = mnist.load_data() # (60000,28,28) x_train = x_train/255.0 x_test = x_test/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)#one hot # 创建模型 model = Sequential() # 循环神经网络 model.add(SimpleRNN( units = cell_size, # 输出 input_shape = (time_steps,input_size), #输入 )) # 输出层 model.add(Dense(10,activation=‘softmax‘)) # 定义优化器 adam = Adam(lr=1e-4) # 定义优化器，loss function，训练过程中计算准确率 model.compile(optimizer=adam,loss=‘categorical_crossentropy‘,metrics=[‘accuracy‘]) # 训练模型 model.fit(x_train,y_train,batch_size=64,epochs=10) # 评估模型 loss,accuracy = model.evaluate(x_test,y_test) print(‘test loss‘,loss) print(‘test accuracy‘,accuracy)
