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tensorflow2.0保存和恢復(fù)模型3種方法

更新時(shí)間：2020年02月03日 16:31:55 作者：李宜君

今天小編就為大家分享一篇tensorflow2.0保存和恢復(fù)模型3種方法，具有很好的參考價(jià)值，希望對(duì)大家有所幫助。一起跟隨小編過(guò)來(lái)看看吧

方法1：只保存模型的權(quán)重和偏置

這種方法不會(huì)保存整個(gè)網(wǎng)絡(luò)的結(jié)構(gòu)，只是保存模型的權(quán)重和偏置，所以在后期恢復(fù)模型之前，必須手動(dòng)創(chuàng)建和之前模型一模一樣的模型，以保證權(quán)重和偏置的維度和保存之前的相同。

tf.keras.model類中的save_weights方法和load_weights方法，參數(shù)解釋我就直接搬運(yùn)官網(wǎng)的內(nèi)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
)

實(shí)例1：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets, layers, optimizers
 
# step1 加載訓(xùn)練集和測(cè)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 創(chuàng)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 編譯模型 主要是確定優(yōu)化方法，損失函數(shù)等
model.compile(optimizer='adam',
  loss='sparse_categorical_crossentropy',
  metrics=['accuracy'])
 
# step4 模型訓(xùn)練 訓(xùn)練一個(gè)epochs
model.fit(x=x_train,
  y=y_train,
  epochs=1,
  )
 
# step5 模型測(cè)試
loss, acc = model.evaluate(x_test, y_test)
print("train model, accuracy:{:5.2f}%".format(100 * acc))
 
# step6 保存模型的權(quán)重和偏置
model.save_weights('./save_weights/my_save_weights')
 
# step7 刪除模型
del model
 
# step8 重新創(chuàng)建模型
model = create_model()
model.compile(optimizer='adam',
  loss='sparse_categorical_crossentropy',
  metrics=['accuracy'])
 
# step9 恢復(fù)權(quán)重
model.load_weights('./save_weights/my_save_weights')
 
# step10 測(cè)試模型
loss, acc = model.evaluate(x_test, 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(quán)重和偏置恢復(fù)之后，在測(cè)試集合上同樣達(dá)到了訓(xùn)練之前相同的準(zhǔn)確率。

方法2：直接保存整個(gè)模型

這種方法會(huì)將網(wǎng)絡(luò)的結(jié)構(gòu)，權(quán)重和優(yōu)化器的狀態(tài)等參數(shù)全部保存下來(lái)，后期恢復(fù)的時(shí)候就沒(méi)必要?jiǎng)?chuàng)建新的網(wǎng)絡(luò)了。

tf.keras.model類中的save方法和load_model方法

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

Arguments:

filepath: String, 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).

實(shí)例2：

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets, layers, optimizers
 
 
# step1 加載訓(xùn)練集和測(cè)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 創(chuàng)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 編譯模型 主要是確定優(yōu)化方法，損失函數(shù)等
model.compile(optimizer='adam',
  loss='sparse_categorical_crossentropy',
  metrics=['accuracy'])
 
# step4 模型訓(xùn)練 訓(xùn)練一個(gè)epochs
model.fit(x=x_train,
  y=y_train,
  epochs=1,
  )
 
# step5 模型測(cè)試
loss, acc = model.evaluate(x_test, y_test)
print("train model, accuracy:{:5.2f}%".format(100 * acc))
 
# step6 保存模型的權(quán)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(fù)模型
# returns a compiled model
# identical to the previous one
restored_model = tf.keras.models.load_model('my_model.h5')
 
# step9 測(cè)試模型
loss, acc = restored_model.evaluate(x_test, y_test)
print("Restored model, accuracy:{:5.2f}%".format(100 * acc))

train model, accuracy:96.94%

Restored model, accuracy:96.94%

方法3：使用tf.keras.callbacks.ModelCheckpoint方法在訓(xùn)練過(guò)程中保存模型

該方法繼承自tf.keras.callbacks類，一般配合mode.fit函數(shù)使用

以上這篇tensorflow2.0保存和恢復(fù)模型3種方法就是小編分享給大家的全部?jī)?nèi)容了，希望能給大家一個(gè)參考，也希望大家多多支持腳本之家。

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