tensorflow2.0保存和恢复模型3种方法

save_weights(
 filepath,
 overwrite=True,
 save_format=None
)

load_weights(
 filepath,
 by_name=False
)

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets, layers, optimizers
 
# step1 加载训练集和测试集合
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
 
 
# step2 创建模型
def create_model():
 return tf.keras.models.Sequential([
 tf.keras.layers.Flatten(input_shape=(28, 28)),
 tf.keras.layers.Dense(512, activation='relu'),
 tf.keras.layers.Dropout(0.2),
 tf.keras.layers.Dense(10, activation='softmax')
 ])
model = create_model()
 
# step3 编译模型 主要是确定优化方法，损失函数等
model.compile(optimizer='adam',
  loss='sparse_categorical_crossentropy',
  metrics=['accuracy'])
 
# step4 模型训练 训练一个epochs
model.fit(x=x_train,
  y=y_train,
  epochs=1,
  )
 
# step5 模型测试
loss, acc = model.evaluate(x_test, y_test)
print("train model, accuracy:{:5.2f}%".format(100 * acc))
 
# step6 保存模型的权重和偏置
model.save_weights('./save_weights/my_save_weights')
 
# step7 删除模型
del model
 
# step8 重新创建模型
model = create_model()
model.compile(optimizer='adam',
  loss='sparse_categorical_crossentropy',
  metrics=['accuracy'])
 
# step9 恢复权重
model.load_weights('./save_weights/my_save_weights')
 
# step10 测试模型
loss, acc = model.evaluate(x_test, y_test)
print("Restored model, accuracy:{:5.2f}%".format(100 * acc))

save(
 filepath,
 overwrite=True,
 include_optimizer=True,
 save_format=None
)

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets, layers, optimizers
 
 
# step1 加载训练集和测试集合
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
 
 
# step2 创建模型
def create_model():
 return tf.keras.models.Sequential([
 tf.keras.layers.Flatten(input_shape=(28, 28)),
 tf.keras.layers.Dense(512, activation='relu'),
 tf.keras.layers.Dropout(0.2),
 tf.keras.layers.Dense(10, activation='softmax')
 ])
model = create_model()
 
# step3 编译模型 主要是确定优化方法，损失函数等
model.compile(optimizer='adam',
  loss='sparse_categorical_crossentropy',
  metrics=['accuracy'])
 
# step4 模型训练 训练一个epochs
model.fit(x=x_train,
  y=y_train,
  epochs=1,
  )
 
# step5 模型测试
loss, acc = model.evaluate(x_test, y_test)
print("train model, accuracy:{:5.2f}%".format(100 * acc))
 
# step6 保存模型的权重和偏置
model.save('my_model.h5') # creates a HDF5 file 'my_model.h5'
 
# step7 删除模型
del model # deletes the existing model
 
 
# step8 恢复模型
# returns a compiled model
# identical to the previous one
restored_model = tf.keras.models.load_model('my_model.h5')
 
# step9 测试模型
loss, acc = restored_model.evaluate(x_test, y_test)
print("Restored model, accuracy:{:5.2f}%".format(100 * acc))