机器学习tensorflow-keras之保存和恢复模型h5py

前言

模型进度可在训练期间和之后保存。这意味着,您可以从上次暂停的地方继续训练模型,避免训练时间过长。此外,可以保存意味着您可以分享模型,而他人可以对您的工作成果进行再创作。发布研究模型和相关技术时,大部分机器学习从业者会分享以下内容:

  • 用于创建模型的代码
  • 模型的训练权重或参数

分享此类数据有助于他人了解模型的工作原理并尝试使用新数据自行尝试模型。

数据和依赖

依赖

安装并导入 TensorFlow 和依赖项:

$ pip install -q h5py pyyaml

获取示例数据集

我们将使用 MNIST 数据集训练模型,以演示如何保存权重。要加快演示运行速度,请仅使用前 1000 个样本:

import os

import tensorflow as tf
from tensorflow import keras

tf.__version__
'1.12.0'

准备数据:

(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
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 0s 0us/step

定义模型

我们来构建一个简单的模型,以演示如何保存和加载权重。

# Returns a short sequential model
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

# Create a basic model instance
model = create_model()
model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
dense (Dense)                (None, 512)               401920
_________________________________________________________________
dropout (Dropout)            (None, 512)               0
_________________________________________________________________
dense_1 (Dense)              (None, 10)                5130
=================================================================
Total params: 407,050
Trainable params: 407,050
Non-trainable params: 0
_________________________________________________________________

在训练期间保存检查点

tf.keras.callbacks.ModelCheckpoint是执行此任务的回调。该回调需要几个参数来配置检查点。

用法

训练模型,并将 ModelCheckpoint 回调传递给该模型:

checkpoint_path = "training_1/cp.ckpt"
checkpoint_dir = os.path.dirname(checkpoint_path)

# Create checkpoint callback
cp_callback = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,
                                                 save_weights_only=True,
                                                 verbose=1)

model = create_model()

model.fit(train_images, train_labels,  epochs = 10,
          validation_data = (test_images,test_labels),
          callbacks = [cp_callback])  # pass callback to training
Train on 1000 samples, validate on 1000 samples
Epoch 1/10
 960/1000 [===========================>..] - ETA: 0s - loss: 1.2667 - acc: 0.6531
Epoch 00001: saving model to training_1/cp.ckpt
WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.

Consider using a TensorFlow optimizer from tf.train.
1000/1000 [==============================] - 0s 421us/step - loss: 1.2358 - acc: 0.6620 - val_loss: 0.7326 - val_acc: 0.7760
Epoch 2/10
 928/1000 [==========================>...] - ETA: 0s - loss: 0.4499 - acc: 0.8696
Epoch 00002: saving model to training_1/cp.ckpt
WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.

Consider using a TensorFlow optimizer from tf.train.
1000/1000 [==============================] - 0s 165us/step - loss: 0.4324 - acc: 0.8760 - val_loss: 0.5149 - val_acc: 0.8430
Epoch 3/10
 832/1000 [=======================>......] - ETA: 0s - loss: 0.2870 - acc: 0.9255
Epoch 00003: saving model to training_1/cp.ckpt
WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.

Consider using a TensorFlow optimizer from tf.train.
1000/1000 [==============================] - 0s 177us/step - loss: 0.2847 - acc: 0.9240 - val_loss: 0.4527 - val_acc: 0.8570
Epoch 4/10
 864/1000 [========================>.....] - ETA: 0s - loss: 0.2065 - acc: 0.9572
Epoch 00004: saving model to training_1/cp.ckpt
WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.

Consider using a TensorFlow optimizer from tf.train.
1000/1000 [==============================] - 0s 174us/step - loss: 0.2011 - acc: 0.9560 - val_loss: 0.4250 - val_acc: 0.8680
Epoch 5/10
 864/1000 [========================>.....] - ETA: 0s - loss: 0.1503 - acc: 0.9676
Epoch 00005: saving model to training_1/cp.ckpt
WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.

Consider using a TensorFlow optimizer from tf.train.
1000/1000 [==============================] - 0s 178us/step - loss: 0.1601 - acc: 0.9620 - val_loss: 0.4080 - val_acc: 0.8660
Epoch 6/10
 864/1000 [========================>.....] - ETA: 0s - loss: 0.1068 - acc: 0.9861
Epoch 00006: saving model to training_1/cp.ckpt
WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.

Consider using a TensorFlow optimizer from tf.train.
1000/1000 [==============================] - 0s 175us/step - loss: 0.1076 - acc: 0.9850 - val_loss: 0.4124 - val_acc: 0.8650
Epoch 7/10
 864/1000 [========================>.....] - ETA: 0s - loss: 0.0856 - acc: 0.9861
Epoch 00007: saving model to training_1/cp.ckpt
WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.

Consider using a TensorFlow optimizer from tf.train.
1000/1000 [==============================] - 0s 174us/step - loss: 0.0837 - acc: 0.9880 - val_loss: 0.4060 - val_acc: 0.8680
Epoch 8/10
 864/1000 [========================>.....] - ETA: 0s - loss: 0.0626 - acc: 0.9965
Epoch 00008: saving model to training_1/cp.ckpt
WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.

Consider using a TensorFlow optimizer from tf.train.
1000/1000 [==============================] - 0s 172us/step - loss: 0.0616 - acc: 0.9970 - val_loss: 0.4055 - val_acc: 0.8720
Epoch 9/10
 832/1000 [=======================>......] - ETA: 0s - loss: 0.0475 - acc: 1.0000
Epoch 00009: saving model to training_1/cp.ckpt
WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.

Consider using a TensorFlow optimizer from tf.train.
1000/1000 [==============================] - 0s 176us/step - loss: 0.0455 - acc: 1.0000 - val_loss: 0.3973 - val_acc: 0.8730
Epoch 10/10
 864/1000 [========================>.....] - ETA: 0s - loss: 0.0394 - acc: 0.9965
Epoch 00010: saving model to training_1/cp.ckpt
WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with `save_weights`. The model's weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer's state will not be saved.

Consider using a TensorFlow optimizer from tf.train.
1000/1000 [==============================] - 0s 176us/step - loss: 0.0389 - acc: 0.9960 - val_loss: 0.4114 - val_acc: 0.8720

上述代码将创建一个 TensorFlow 检查点文件集合,这些文件在每个周期结束时更新:

!ls {checkpoint_dir}
checkpoint  cp.ckpt.data-00000-of-00001  cp.ckpt.index

创建一个未经训练的全新模型。仅通过权重恢复模型时,您必须有一个与原始模型架构相同的模型。

现在,重新构建一个未经训练的全新模型,并用测试集对其进行评估。未训练模型的表现有很大的偶然性(准确率约为 10%):

model = create_model()

loss, acc = model.evaluate(test_images, test_labels)
print("Untrained model, accuracy: {:5.2f}%".format(100*acc))
1000/1000 [==============================] - 0s 118us/step
Untrained model, accuracy: 11.10%

然后从检查点加载权重,并重新评估:

model.load_weights(checkpoint_path)
loss,acc = model.evaluate(test_images, test_labels)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))
1000/1000 [==============================] - 0s 35us/step
Restored model, accuracy: 87.20%

回调选项

该回调提供了多个选项,用于为生成的检查点提供独一无二的名称,以及调整检查点创建频率。

训练一个新模型,每隔 5 个周期保存一次检查点并设置唯一名称:

# include the epoch in the file name. (uses `str.format`)
checkpoint_path = "training_2/cp-{epoch:04d}.ckpt"
checkpoint_dir = os.path.dirname(checkpoint_path)

cp_callback = tf.keras.callbacks.ModelCheckpoint(
    checkpoint_path, verbose=1, save_weights_only=True,
    # Save weights, every 5-epochs.
    period=5)

model = create_model()
model.fit(train_images, train_labels,
          epochs = 50, callbacks = [cp_callback],
          validation_data = (test_images,test_labels),
          verbose=0)
Epoch 00005: saving model to training_2/cp-0005.ckpt
Epoch 00010: saving model to training_2/cp-0010.ckpt
Epoch 00015: saving model to training_2/cp-0015.ckpt
Epoch 00020: saving model to training_2/cp-0020.ckpt
Epoch 00025: saving model to training_2/cp-0025.ckpt
Epoch 00030: saving model to training_2/cp-0030.ckpt
Epoch 00035: saving model to training_2/cp-0035.ckpt
Epoch 00040: saving model to training_2/cp-0040.ckpt
Epoch 00050: saving model to training_2/cp-0050.ckpt
latest = tf.train.latest_checkpoint(checkpoint_dir)
latest
'training_2/cp-0050.ckpt'

注意:默认的 TensorFlow 格式仅保存最近的 5 个检查点。

要进行测试,请重置模型并加载最新的检查点:

model = create_model()
model.load_weights(latest)
loss, acc = model.evaluate(test_images, test_labels)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))
1000/1000 [==============================] - 0s 89us/step
Restored model, accuracy: 87.60%

这些是什么文件?

上述代码将权重存储在检查点格式的文件集合中,这些文件仅包含经过训练的权重(采用二进制格式)。

检查点包括: 包含模型权重的一个或多个分片。 指示哪些权重存储在哪些分片中的索引文件。

如果您仅在一台机器上训练模型,则您将有 1 个后缀为 .data-00000-of-00001 的分片

手动保存权重

在上文中,您了解了如何将权重加载到模型中。

手动保存权重的方法同样也很简单,只需使用 Model.save_weights 方法即可。

# Save the weights
model.save_weights('./checkpoints/my_checkpoint')

# Restore the weights
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))
1000/1000 [==============================] - 0s 90us/step
Restored model, accuracy: 87.60%

保存整个模型

整个模型可以保存到一个文件中,其中包含权重值、模型配置乃至优化器配置。这样,您就可以为模型设置检查点,并稍后从完全相同的状态继续训练,而无需访问原始代码。

在 Keras 中保存完全可正常使用的模型非常有用,您可以在 TensorFlow.js 中加载它们,然后在网络浏览器中训练和运行它们。

Keras 使用 HDF5 标准提供基本的保存格式。对于我们来说,可将保存的模型视为一个二进制 blob。

model = create_model()

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

# Save entire model to a HDF5 file
model.save('my_model.h5')
Epoch 1/5
1000/1000 [==============================] - 0s 342us/step - loss: 1.1782 - acc: 0.6630
Epoch 2/5
1000/1000 [==============================] - 0s 131us/step - loss: 0.4267 - acc: 0.8730
Epoch 3/5
1000/1000 [==============================] - 0s 130us/step - loss: 0.2926 - acc: 0.9250
Epoch 4/5
1000/1000 [==============================] - 0s 132us/step - loss: 0.2049 - acc: 0.9480
Epoch 5/5
1000/1000 [==============================] - 0s 131us/step - loss: 0.1533 - acc: 0.9640

现在,从该文件重新创建模型:

# Recreate the exact same model, including weights and optimizer.
new_model = keras.models.load_model('my_model.h5')
new_model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
dense_12 (Dense)             (None, 512)               401920
_________________________________________________________________
dropout_6 (Dropout)          (None, 512)               0
_________________________________________________________________
dense_13 (Dense)             (None, 10)                5130
=================================================================
Total params: 407,050
Trainable params: 407,050
Non-trainable params: 0
_________________________________________________________________

检查其准确率:

loss, acc = new_model.evaluate(test_images, test_labels)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))
1000/1000 [==============================] - 0s 109us/step
Restored model, accuracy: 86.70%

此技巧可保存以下所有内容:

  • 权重值
  • 模型配置(架构)
  • 优化器配置

Keras 通过检查架构来保存模型。目前,它无法保存 TensorFlow 优化器(来自 tf.train)。使用此类优化器时,您需要在加载模型后对其进行重新编译,使优化器的状态变松散。

参考资料


  1. 1.保存和恢复模型 https://www.tensorflow.org/tutorials/keras/save_and_restore_models
  2. 2.jupyter notebook 代码 https://github.com/tensorflow/docs/blob/master/site/en/tutorials/keras/save_and_restore_models.ipynb

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