keras迁移学习 使用vgg16进行手写数字识别

一个简单的迁移学习案例：使用keras 将vgg16用于手写数字识别

# -*- coding: utf-8 -*-"""Created on Tue Nov 21 22:26:20 2017@author: www"""from keras.models import Modelfrom keras.layers import Dense, Flatten, Dropoutimport cv2from keras import datasetsfrom keras.applications.vgg16 import VGG16from keras.optimizers import SGDfrom keras.datasets import mnistimport numpy as np#迁移学习 使用VGG16进行手写数字识别#只迁移网络结构，不迁移权重model_vgg = VGG16(include_top=False, weights='imagenet', input_shape=(224,224,3))model = Flatten(name='Flatten')(model_vgg.output)moel = Dense(10, activation='softmax')(model)model_vgg_mnist = Model(inputs=model_vgg.input, outputs=model, name='vgg16')model_vgg_mnist.summary()#迁移学习；网络结构与权重#ishape = 224model_vgg = VGG16(include_top=False, weights='imagenet', input_shape=(ishape, ishape, 3))for layers in model_vgg.layers:    layers.trainable = Falsemodel = Flatten()(model_vgg.output)model = Dense(10, activation='softmax')(model)model_vgg_mnist_pretrain = Model(inputs=model_vgg.input, outputs=model, name='vgg16_pretrain')model_vgg_mnist_pretrain.summary()#==============================================================================# Total params: 14,965,578.0# Trainable params: 250,890.0# Non-trainable params: 14,714,688.0#==============================================================================sgd = SGD(lr=0.05, decay=1e-5)model_vgg_mnist_pretrain.compile(optimizer=sgd, loss='categorical_crossentropy',                                 metrics=['accuracy'])(X_train,y_train),(X_test,y_test) = mnist.load_data()#转成VGG16需要的格式X_train = [cv2.cvtColor(cv2.resize(i,(ishape,ishape)), cv2.COLOR_GRAY2BGR) for i in X_train]X_train = np.concatenate([arr[np.newaxis] for arr in X_train]).astype('float32')X_test  = [cv2.cvtColor(cv2.resize(i,(ishape,ishape)), cv2.COLOR_GRAY2BGR) for i in X_test ]X_test  = np.concatenate([arr[np.newaxis] for arr in X_test] ).astype('float32')#预处理X_train.shapeX_test.shapeX_train /= X_train/255X_test /= X_test/255np.where(X_train[0] != 0)#哑编码def train_y(y):    y_one = np.zeros(10)    y_one[y] = 1    return y_one    y_train_one = np.array([train_y(y_train[i]) for i in range(len(y_train))])y_test_one  = np.array([train_y(y_test [i]) for i in range(len(y_test ))])#模型训练model_vgg_mnist_pretrain.fit(X_train, y_train_one, validation_data=(X_test, y_test_one),                              epochs=200, batch_size=128)