Keras使用ImageNet上预训练的模型方式

import keras
import numpy as np
from keras.applications import vgg16, inception_v3, resnet50, mobilenet
 
#Load the VGG model
vgg_model = vgg16.VGG16(weights='imagenet')
 
#Load the Inception_V3 model
inception_model = inception_v3.InceptionV3(weights='imagenet')
 
#Load the ResNet50 model
resnet_model = resnet50.ResNet50(weights='imagenet')
 
#Load the MobileNet model
mobilenet_model = mobilenet.MobileNet(weights='imagenet')

x_train = mnist.train.images
y_train = mnist.train.labels
x_test = mnist.test.images
y_test = mnist.test.labels

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D, ZeroPadding2D
from keras.layers.normalization import BatchNormalization
from keras.callbacks import ModelCheckpoint
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data #tensorflow已经包含了mnist案例的数据
batch_size = 64
num_classes = 10
epochs = 10
img_shape = (28,28,1)
# input dimensions
img_rows, img_cols = 28,28
# dataset input
#(x_train, y_train), (x_test, y_test) = mnist.load_data()
mnist = input_data.read_data_sets("./MNIST_data", one_hot = True) #导入已经下载好的数据集,"./MNIST_data"为存放mnist数据的目录
print(mnist.train.images.shape, mnist.train.labels.shape)
print(mnist.test.images.shape, mnist.test.labels.shape)
print(mnist.validation.images.shape, mnist.validation.labels.shape)
x_train = mnist.train.images
y_train = mnist.train.labels
x_test = mnist.test.images
y_test = mnist.test.labels
# data initialization
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
# Define the input layer
inputs = keras.Input(shape = [img_rows, img_cols, 1])
#Define the converlutional layer 1
conv1 = keras.layers.Conv2D(filters= 64, kernel_size= [11, 11], strides= [1, 1], activation= keras.activations.relu, use_bias= True, padding= 'same')(inputs)
# Define the pooling layer 1
pooling1 = keras.layers.AveragePooling2D(pool_size= [2, 2], strides= [2, 2], padding= 'valid')(conv1)
# Define the standardization layer 1
stand1 = keras.layers.BatchNormalization(axis= 1)(pooling1)
# Define the converlutional layer 2
conv2 = keras.layers.Conv2D(filters= 192, kernel_size= [5, 5], strides= [1, 1], activation= keras.activations.relu, use_bias= True, padding= 'same')(stand1)
# Defien the pooling layer 2
pooling2 = keras.layers.AveragePooling2D(pool_size= [2, 2], strides= [2, 2], padding= 'valid')(conv2)
# Define the standardization layer 2
stand2 = keras.layers.BatchNormalization(axis= 1)(pooling2)
# Define the converlutional layer 3
conv3 = keras.layers.Conv2D(filters= 384, kernel_size= [3, 3], strides= [1, 1], activation= keras.activations.relu, use_bias= True, padding= 'same')(stand2)
stand3 = keras.layers.BatchNormalization(axis=1)(conv3)
# Define the converlutional layer 4
conv4 = keras.layers.Conv2D(filters= 384, kernel_size= [3, 3], strides= [1, 1], activation= keras.activations.relu, use_bias= True, padding= 'same')(stand3)
stand4 = keras.layers.BatchNormalization(axis=1)(conv4)
# Define the converlutional layer 5
conv5 = keras.layers.Conv2D(filters= 256, kernel_size= [3, 3], strides= [1, 1], activation= keras.activations.relu, use_bias= True, padding= 'same')(stand4)
pooling5 = keras.layers.AveragePooling2D(pool_size= [2, 2], strides= [2, 2], padding= 'valid')(conv5)
stand5 = keras.layers.BatchNormalization(axis=1)(pooling5)
# Define the fully connected layer
flatten = keras.layers.Flatten()(stand5)
fc1 = keras.layers.Dense(4096, activation= keras.activations.relu, use_bias= True)(flatten)
drop1 = keras.layers.Dropout(0.5)(fc1)
fc2 = keras.layers.Dense(4096, activation= keras.activations.relu, use_bias= True)(drop1)
drop2 = keras.layers.Dropout(0.5)(fc2)
fc3 = keras.layers.Dense(10, activation= keras.activations.softmax, use_bias= True)(drop2)
# 基于Model方法构建模型
model = keras.Model(inputs= inputs, outputs = fc3)
# 编译模型
model.compile(optimizer= tf.train.AdamOptimizer(0.001),
loss= keras.losses.categorical_crossentropy,
metrics= ['accuracy'])
# 训练配置，仅供参考
model.fit(x_train, y_train, batch_size= batch_size, epochs= epochs, validation_data=(x_test,y_test))