[深度学习] tf.keras入门5-模型保存和载入

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%