目录

设置

基于checkpoints的模型保存

通过ModelCheckpoint模块来自动保存数据

手动保存权重

整个模型保存

总体代码

---

模型可以在训练中或者训练完成后保存。具体文档参考：https://tensorflow.google.cn/tutorials/keras/save_and_restore_models

设置

依赖项设置：

!pip install -q h5py pyyaml 

模型建立：

from __future__ import absolute_import, division, print_function

import os

import tensorflow as tf

from tensorflow import keras

tf.__version__

(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()

train_labels = train_labels[:1000]

test_labels = test_labels[:1000]

train_images = train_images[:1000].reshape(-1, 28 * 28) / 255.0

test_images = test_images[:1000].reshape(-1, 28 * 28) / 255.0

# 模型创建模型

def create_model():

  model = tf.keras.models.Sequential([

    keras.layers.Dense(512, activation=tf.nn.relu, input_shape=(784,)),

    keras.layers.Dropout(0.2),

    keras.layers.Dense(10, activation=tf.nn.softmax)

  ])

  model.compile(optimizer=tf.keras.optimizers.Adam(),

                loss=tf.keras.losses.sparse_categorical_crossentropy,

                metrics=['accuracy'])

  return model

#创建模型

model = create_model()

model.summary()

基于checkpoints的模型保存

通过ModelCheckpoint模块来自动保存数据

#创建回调函数

cp_callback = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,

                                                 save_weights_only=True, #只保存权重

                                                 verbose=1)

model = create_model()

model.fit(train_images, train_labels,  epochs = 10,

          validation_data = (test_images,test_labels),

          callbacks = [cp_callback])  #保存模型

通过load_weight读取权重

#对全新没有训练的模型进行预测

model = create_model()

loss, acc = model.evaluate(test_images, test_labels)

print("Untrained model, accuracy: {:5.2f}%".format(100*acc)) #11.4%

#载入权重参数后的模型

model.load_weights(checkpoint_path)

loss,acc = model.evaluate(test_images, test_labels)

print("Restored model, accuracy: {:5.2f}%".format(100*acc)) #86.2

手动保存权重

# 保存权重

model.save_weights('./checkpoints/my_checkpoint')

#恢复模型

model = create_model()

model.load_weights('./checkpoints/my_checkpoint')

loss,acc = model.evaluate(test_images, test_labels)

print("Restored model, accuracy: {:5.2f}%".format(100*acc)) #87.00%

整个模型保存

基于keras的HD5文件保存整个模型所有参数，优化器参数等。

#将整个模型保存为HDF5文件

model = create_model()

model.fit(train_images, train_labels, epochs=5)

model.save('my_model.h5')

#载入一个相同的模型

new_model = keras.models.load_model('my_model.h5')

new_model.summary()

loss, acc = new_model.evaluate(test_images, test_labels)

print("Restored model, accuracy: {:5.2f}%".format(100*acc)) #86.30%

总体代码

from __future__ import absolute_import, division, print_function

import os

import tensorflow as tf

from tensorflow import keras

tf.__version__

(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()

train_labels = train_labels[:1000]

test_labels = test_labels[:1000]

train_images = train_images[:1000].reshape(-1, 28 * 28) / 255.0

test_images = test_images[:1000].reshape(-1, 28 * 28) / 255.0

# 模型创建模型

def create_model():

  model = tf.keras.models.Sequential([

    keras.layers.Dense(512, activation=tf.nn.relu, input_shape=(784,)),

    keras.layers.Dropout(0.2),

    keras.layers.Dense(10, activation=tf.nn.softmax)

  ])

  model.compile(optimizer=tf.keras.optimizers.Adam(),

                loss=tf.keras.losses.sparse_categorical_crossentropy,

                metrics=['accuracy'])

  return model

#创建模型

model = create_model()

model.summary()

checkpoint_path = "training_1/cp.ckpt"

checkpoint_dir = os.path.dirname(checkpoint_path)

'''

#创建回调函数

cp_callback = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,

                                                 save_weights_only=True, #只保存权重

                                                 verbose=1)

model = create_model()

model.fit(train_images, train_labels,  epochs = 10,

          validation_data = (test_images,test_labels),

          callbacks = [cp_callback])  #保存模型

#对全新没有训练的模型进行预测

model = create_model()

loss, acc = model.evaluate(test_images, test_labels)

print("Untrained model, accuracy: {:5.2f}%".format(100*acc)) #11.4%

#载入权重参数后的模型

model.load_weights(checkpoint_path)

loss,acc = model.evaluate(test_images, test_labels)

print("Restored model, accuracy: {:5.2f}%".format(100*acc)) #86.2

# 保存权重

model.save_weights('./checkpoints/my_checkpoint')

#恢复模型

model = create_model()

model.load_weights('./checkpoints/my_checkpoint')

loss,acc = model.evaluate(test_images, test_labels)

print("Restored model, accuracy: {:5.2f}%".format(100*acc)) #87.00%

'''

#将整个模型保存为HDF5文件

model = create_model()

model.fit(train_images, train_labels, epochs=5)

model.save('my_model.h5')

#载入一个相同的模型

new_model = keras.models.load_model('my_model.h5')

new_model.summary()

loss, acc = new_model.evaluate(test_images, test_labels)

print("Restored model, accuracy: {:5.2f}%".format(100*acc)) #86.30%

[深度学习] tf.keras入门5-模型保存和载入的相关教程结束。