官网实例详解4.34（mnist_transfer_cnn.py）-keras学习笔记
迁移学习实例
Keras实例目录
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
英文：https://keras.io/
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
完整项目下载
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