Keras 和 Tensorflow的版本对应关系
tensorflow 2.0 - keras 2.2.4
tensorflow 1.9 - keras 2.2.0 对应keras-transformer==0.31.0
tensorflow 1.8 - keras 2.1.6
tensorflow 1.5 - keras 2.1.4
tensorflow 1.4 - keras 2.1.3
tensorflow 1.3 - keras 2.1.2
tensorflow 1.2 - keras 2.1.1
TensorFlow 1.14.0 + Keras 2.2.5 on Python 3.6.
TensorFlow 1.13.0 + Keras 2.2.4 on Python 3.6.
TensorFlow 1.12.0 + Keras 2.2.4 on Python 3.6.
TensorFlow 1.11.0 + Keras 2.2.4 on Python 3.6.
TensorFlow 1.10.0 + Keras 2.2.0 on Python 3.6.
TensorFlow 1.12.0 + Keras 2.2.4 on Python 2.
TensorFlow 1.11.0 + Keras 2.2.4 on Python 2.
保存和加载模型
- 完整保存使用model.save()完整地保存整个模型，将Keras模型和权重保存在一个HDF5文件中，该文件将包含：模型的结构，模型的参数以及优化器参数：用于继续训练过程
from __future__ import print_function
import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.optimizers import SGD
from keras.utils import np_utils
#随机数种子，重复性设置
np.random.seed(1671)
#网络结构和训练的参数
NB_EPOCH = 20
BATCH_SIZE = 128
VERBOSE = 1
NB_CLASSES = 10
OPTIMIZER = SGD()
N_HIDDEN = 128
VALIDATION_SPLIT = 0.2
RESHAPED = 784
#加载数据
def load_data(path="mnist.npz"):
f = np.load(path)
x_train, y_train = f['x_train'], f['y_train']
x_test, y_test = f['x_test'], f['y_test']
f.close()
return (x_train, y_train), (x_test, y_test)
#调用函数加载数据
(x_train, y_train), (x_test, y_test) = load_data()
#数据预处理
(x_train, y_train), (x_test, y_test) = load_data()
#数据变形、类型转换及归一化
x_train = x_train.reshape(60000, 784).astype('float32') / 255
x_test = x_test.reshape(10000, 784).astype('float32') / 255
#打印消息
print('Training samples:', x_train.shape)
print('Testing samples:', x_test.shape)
#将类别转换为one-hot编码
y_train = np_utils.to_categorical(y_train, NB_CLASSES)
y_test = np_utils.to_categorical(y_test, NB_CLASSES)
#定义网络结构
model = Sequential()
model.add(Dense(N_HIDDEN, input_shape=(RESHAPED, )))
model.add(Activation('relu'))
model.add(Dense(N_HIDDEN))
model.add(Activation('relu'))
model.add(Dense(NB_CLASSES))
model.add(Activation('softmax'))
#打印模型概述信息
model.summary()
#编译模型
model.compile(loss='categorical_crossentropy', optimizer=OPTIMIZER, metrics=['accuracy'])
#训练模型
history = model.fit(x_train, y_train, batch_size=BATCH_SIZE, epochs=NB_EPOCH, verbose=VERBOSE,
validation_split=VALIDATION_SPLIT)
#评估模型
score = model.evaluate(x_test, y_test, verbose=VERBOSE)
print('Test score:', score[0])
print('Test accuracy:', score[1])
# 保存模型
model.save('my_model.h5')
加载：
from keras.models import load_model
#加载整个模型
model = load_model('my_model.h5')
#训练模型
history = model.fit(x_train, y_train, batch_size=BATCH_SIZE, epochs=NB_EPOCH, verbose=VERBOSE,
validation_split=VALIDATION_SPLIT)
#评估模型
score = model.evaluate(x_test, y_test, verbose=VERBOSE)
- 保存模型结构和权重
from __future__ import print_function import numpy as np from keras.models import Sequential from keras.layers.core import Dense, Activation from keras.optimizers import SGD from keras.utils import np_utils # 定义网络结构 model = Sequential() model.add(Dense(N_HIDDEN, input_shape=(RESHAPED, ))) model.add(Activation('relu')) model.add(Dense(N_HIDDEN)) model.add(Activation('relu')) model.add(Dense(NB_CLASSES)) model.add(Activation('softmax')) # 打印模型概述信息 model.summary() # 编译模型 model.compile(loss='categorical_crossentropy', optimizer=OPTIMIZER, metrics=['accuracy']) # 训练模型 history = model.fit(x_train, y_train, batch_size=BATCH_SIZE, epochs=NB_EPOCH, verbose=VERBOSE, validation_split=VALIDATION_SPLIT) # 评估模型 score = model.evaluate(x_test, y_test, verbose=VERBOSE) print('Test score:', score[0]) print('Test accuracy:', score[1]) # 保存模型的结构 json_string = model.to_json() # 方式1 open('model_architecture_1.json', 'w').write(json_string) yaml_string = model.to_yaml() # 方式2 open('model_arthitecture_2.yaml', 'w').write(yaml_string) # 保存模型的权重 model.save_weights('my_model_weights.h5') # 打印消息 print('训练和保存模型结构完成！！！')
加载结构：
from keras.models import model_from_json
from keras.models import model_from_yaml
# 加载模型结构
model = model_from_json(open('model_architecture_1.json', 'r').read())
或：
model = model_from_yaml(open('model_arthitecture_2.yaml', 'r').read())
# 加载模型权重
model.load_weights('my_model_weights.h5')
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