方法1：只儲存模型的權重和偏置

這種方法不會儲存整個網路的結構，只是儲存模型的權重和偏置，所以在後期恢復模型之前，必須手動建立和之前模型一模一樣的模型，以保證權重和偏置的維度和儲存之前的相同。

tf.keras.model類中的save_weights方法和load_weights方法，引數解釋我就直接搬運官網的內容了。

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

Arguments:

filepath: String,path to the file to save the weights to. When saving in TensorFlow format,this is the prefix used for checkpoint files (multiple files are generated). Note that the '.h5' suffix causes weights to be saved in HDF5 format.

overwrite: Whether to silently overwrite any existing file at the target location,or provide the user with a manual prompt.

save_format: Either 'tf' or 'h5'. A filepath ending in '.h5' or '.keras' will default to HDF5 if save_format is None. Otherwise None defaults to 'tf'.

load_weights(
 filepath,by_name=False
)

例項1：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets,layers,optimizers
 
# step1 載入訓練集和測試集合
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 建立模型
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 編譯模型 主要是確定優化方法，損失函式等
model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])
 
# step4 模型訓練 訓練一個epochs
model.fit(x=x_train,y=y_train,epochs=1,)
 
# step5 模型測試
loss,acc = model.evaluate(x_test,y_test)
print("train model,accuracy:{:5.2f}%".format(100 * acc))
 
# step6 儲存模型的權重和偏置
model.save_weights('./save_weights/my_save_weights')
 
# step7 刪除模型
del model
 
# step8 重新建立模型
model = create_model()
model.compile(optimizer='adam',metrics=['accuracy'])
 
# step9 恢復權重
model.load_weights('./save_weights/my_save_weights')
 
# step10 測試模型
loss,y_test)
print("Restored model,accuracy:{:5.2f}%".format(100 * acc))

train model,accuracy:96.55%

Restored model,accuracy:96.55%

可以看到在模型的權重和偏置恢復之後，在測試集合上同樣達到了訓練之前相同的準確率。

方法2：直接儲存整個模型

這種方法會將網路的結構，權重和優化器的狀態等引數全部儲存下來，後期恢復的時候就沒必要建立新的網路了。

tf.keras.model類中的save方法和load_model方法

save(
 filepath,include_optimizer=True,path to SavedModel or H5 file to save the model.

overwrite: Whether to silently overwrite any existing file at the target location,or provide the user with a manual prompt.
include_optimizer: If True,save optimizer's state together.
save_format: Either 'tf' or 'h5',indicating whether to save the model to Tensorflow SavedModel or HDF5. The default is currently 'h5',but will switch to 'tf' in TensorFlow 2.0. The 'tf' option is currently disabled (use tf.keras.experimental.export_saved_model instead).
例項2：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets,optimizers
 
 
# step1 載入訓練集和測試集合
mnist = tf.keras.datasets.mnist
(x_train,accuracy:{:5.2f}%".format(100 * acc))
 
# step6 儲存模型的權重和偏置
model.save('my_model.h5') # creates a HDF5 file 'my_model.h5'
 
# step7 刪除模型
del model # deletes the existing model
 
 
# step8 恢復模型
# returns a compiled model
# identical to the previous one
restored_model = tf.keras.models.load_model('my_model.h5')
 
# step9 測試模型
loss,acc = restored_model.evaluate(x_test,accuracy:96.94%

Restored model,accuracy:96.94%
方法3：使用tf.keras.callbacks.ModelCheckpoint方法在訓練過程中儲存模型
該方法繼承自tf.keras.callbacks類，一般配合mode.fit函式使用
以上這篇tensorflow2.0儲存和恢復模型3種方法就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。