迁移学习实例
代码注释
'''Transfer learning toy example. 迁移学习实例 1 - Train a simple convnet on the MNIST dataset the first 5 digits [0..4]. 1 - 基于MINIST数据集,训练简单卷积网络,前5个数字[0..4]. 2 - Freeze convolutional layers and fine-tune dense layers for the classification of digits [5..9]. 2 - 为[5..9]数字分类,冻结卷积层并微调全连接层 Get to 99.8% test accuracy after 5 epochs for the first five digits classifier and 99.2% for the last five digits after transfer + fine-tuning. 5个周期后,前5个数字分类测试准确率99.8% ,同时通过迁移+微调,后5个数字测试准确率99.2% ''' from __future__ import print_function import datetime import keras from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2D from keras import backend as K now = datetime.datetime.now batch_size = 128 num_classes = 5 epochs = 5 # input image dimensions # 输入图像维度 img_rows, img_cols = 28, 28 # number of convolutional filters to use # 使用的卷积过滤器数量 filters = 32 # size of pooling area for max pooling # 最大值池化的池化区域大小 pool_size = 2 # convolution kernel size # 卷积核大小 kernel_size = 3 if K.image_data_format() == 'channels_first': input_shape = (1, img_rows, img_cols) else: input_shape = (img_rows, img_cols, 1) def train_model(model, train, test, num_classes): x_train = train[0].reshape((train[0].shape[0],) + input_shape) x_test = test[0].reshape((test[0].shape[0],) + input_shape) x_train = x_train.astype('float32') x_test = x_test.astype('float32') x_train /= 255 x_test /= 255 print('x_train shape:', x_train.shape) print(x_train.shape[0], 'train samples') print(x_test.shape[0], 'test samples') # convert class vectors to binary class matrices # 类别向量转为多分类矩阵 y_train = keras.utils.to_categorical(train[1], num_classes) y_test = keras.utils.to_categorical(test[1], num_classes) model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy']) t = now() model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test)) print('Training time: %s' % (now() - t)) score = model.evaluate(x_test, y_test, verbose=0) print('Test score:', score[0]) print('Test accuracy:', score[1]) # the data, shuffled and split between train and test sets # 筛选(数据顺序打乱)、划分训练集和测试集 (x_train, y_train), (x_test, y_test) = mnist.load_data() # create two datasets one with digits below 5 and one with 5 and above # 创建2个数据集,一个数字小于5,另一个数学大于等与5 x_train_lt5 = x_train[y_train < 5] y_train_lt5 = y_train[y_train < 5] x_test_lt5 = x_test[y_test < 5] y_test_lt5 = y_test[y_test < 5] x_train_gte5 = x_train[y_train >= 5] y_train_gte5 = y_train[y_train >= 5] - 5 x_test_gte5 = x_test[y_test >= 5] y_test_gte5 = y_test[y_test >= 5] - 5 # define two groups of layers: feature (convolutions) and classification (dense) # 定义2组层:特征(卷积)和分类(全连接) feature_layers = [ Conv2D(filters, kernel_size, padding='valid', input_shape=input_shape), Activation('relu'), Conv2D(filters, kernel_size), Activation('relu'), MaxPooling2D(pool_size=pool_size), Dropout(0.25), Flatten(), ] classification_layers = [ Dense(128), Activation('relu'), Dropout(0.5), Dense(num_classes), Activation('softmax') ] # create complete model # 创建完整模型 model = Sequential(feature_layers + classification_layers) # train model for 5-digit classification [0..4] # 为5数字分类[0..4]训练模型 train_model(model, (x_train_lt5, y_train_lt5), (x_test_lt5, y_test_lt5), num_classes) # freeze feature layers and rebuild model # 冻结特征层并重建模型 for l in feature_layers: l.trainable = False # transfer: train dense layers for new classification task [5..9] # 迁移:训练全连接层为[5..9]分类任务 train_model(model, (x_train_gte5, y_train_gte5), (x_test_gte5, y_test_gte5), num_classes)
代码执行
Keras详细介绍
中文:http://keras-cn.readthedocs.io/en/latest/
实例下载
https://github.com/keras-team/keras
https://github.com/keras-team/keras/tree/master/examples
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