我们以MNIST手写数字识别为例
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.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，输入784个神经元，输出10个神经元 model = Sequential([ Dense(units=10,input_dim=784,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 = 'mse', metrics=['accuracy'], ) # 训练模型 model.fit(x_train,y_train,batch_size=64,epochs=5) # 评估模型 loss,accuracy = model.evaluate(x_test,y_test) print('\ntest loss',loss) print('accuracy',accuracy) # 保存模型 model.save('model.h5') # HDF5文件，pip install h5py
载入初次训练的模型，再训练
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.optimizers import SGD from keras.models import load_model # 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) # 载入模型 model = load_model('model.h5') # 评估模型 loss,accuracy = model.evaluate(x_test,y_test) print('\ntest loss',loss) print('accuracy',accuracy) # 训练模型 model.fit(x_train,y_train,batch_size=64,epochs=2) # 评估模型 loss,accuracy = model.evaluate(x_test,y_test) print('\ntest loss',loss) print('accuracy',accuracy) # 保存参数，载入参数 model.save_weights('my_model_weights.h5') model.load_weights('my_model_weights.h5') # 保存网络结构，载入网络结构 from keras.models import model_from_json json_string = model.to_json() model = model_from_json(json_string) print(json_string)
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