特遣部队。keras模型保存TF2.0

我看到了使用TF1.x保存模型的很好的答案。我想提供更多关于保存张量流的提示。Keras模型有点复杂，因为有很多方法来保存一个模型。

这里我提供了一个保存张量流的例子。Keras模型到当前目录下的model_path文件夹。这可以很好地与最新的tensorflow (TF2.0)一起工作。如果在不久的将来有任何变化，我会更新这个描述。

保存和加载整个模型

import tensorflow as tf
from tensorflow import keras
mnist = tf.keras.datasets.mnist

#import data
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# create a model
def create_model():
  model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(512, activation=tf.nn.relu),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10, activation=tf.nn.softmax)
    ])
# compile the model
  model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
  return model

# Create a basic model instance
model=create_model()

model.fit(x_train, y_train, epochs=1)
loss, acc = model.evaluate(x_test, y_test,verbose=1)
print("Original model, accuracy: {:5.2f}%".format(100*acc))

# Save entire model to a HDF5 file
model.save('./model_path/my_model.h5')

# Recreate the exact same model, including weights and optimizer.
new_model = keras.models.load_model('./model_path/my_model.h5')
loss, acc = new_model.evaluate(x_test, y_test)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))

仅保存和加载模型重量

如果您只对保存模型权重感兴趣，然后加载权重以恢复模型，那么

model.fit(x_train, y_train, epochs=5)
loss, acc = model.evaluate(x_test, y_test,verbose=1)
print("Original model, accuracy: {:5.2f}%".format(100*acc))

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

使用keras检查点回调保存和恢复

# 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.save_weights(checkpoint_path.format(epoch=0))
model.fit(train_images, train_labels,
          epochs = 50, callbacks = [cp_callback],
          validation_data = (test_images,test_labels),
          verbose=0)

latest = tf.train.latest_checkpoint(checkpoint_dir)

new_model = create_model()
new_model.load_weights(latest)
loss, acc = new_model.evaluate(test_images, test_labels)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))

保存自定义度量的模型

import tensorflow as tf
from tensorflow import keras
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

# Custom Loss1 (for example) 
@tf.function() 
def customLoss1(yTrue,yPred):
  return tf.reduce_mean(yTrue-yPred) 

# Custom Loss2 (for example) 
@tf.function() 
def customLoss2(yTrue, yPred):
  return tf.reduce_mean(tf.square(tf.subtract(yTrue,yPred))) 

def create_model():
  model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(512, activation=tf.nn.relu),  
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10, activation=tf.nn.softmax)
    ])
  model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy', customLoss1, customLoss2])
  return model

# Create a basic model instance
model=create_model()

# Fit and evaluate model 
model.fit(x_train, y_train, epochs=1)
loss, acc,loss1, loss2 = model.evaluate(x_test, y_test,verbose=1)
print("Original model, accuracy: {:5.2f}%".format(100*acc))

model.save("./model.h5")

new_model=tf.keras.models.load_model("./model.h5",custom_objects={'customLoss1':customLoss1,'customLoss2':customLoss2})

使用自定义操作保存keras模型

当我们像下面的例子(tf.tile)一样进行自定义操作时，我们需要创建一个函数并使用Lambda层进行包装。否则，无法保存模型。

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, Lambda
from tensorflow.keras import Model

def my_fun(a):
  out = tf.tile(a, (1, tf.shape(a)[0]))
  return out

a = Input(shape=(10,))
#out = tf.tile(a, (1, tf.shape(a)[0]))
out = Lambda(lambda x : my_fun(x))(a)
model = Model(a, out)

x = np.zeros((50,10), dtype=np.float32)
print(model(x).numpy())

model.save('my_model.h5')

#load the model
new_model=tf.keras.models.load_model("my_model.h5")

我想我已经介绍了许多保存tf的方法中的一些。keras模型。然而，还有许多其他的方法。如果你发现你的用例没有在上面提到，请在下面评论。谢谢!

对于TensorFlow版本< 0.11.0RC1:

保存的检查点包含模型中的变量值，而不是模型/图本身，这意味着当您恢复检查点时，图应该是相同的。

这里有一个线性回归的例子，其中有一个训练循环，保存变量检查点，还有一个评估部分，将恢复之前运行中保存的变量并计算预测。当然，如果你愿意，你也可以恢复变量并继续训练。

x = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)

w = tf.Variable(tf.zeros([1, 1], dtype=tf.float32))
b = tf.Variable(tf.ones([1, 1], dtype=tf.float32))
y_hat = tf.add(b, tf.matmul(x, w))

...more setup for optimization and what not...

saver = tf.train.Saver()  # defaults to saving all variables - in this case w and b

with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())
    if FLAGS.train:
        for i in xrange(FLAGS.training_steps):
            ...training loop...
            if (i + 1) % FLAGS.checkpoint_steps == 0:
                saver.save(sess, FLAGS.checkpoint_dir + 'model.ckpt',
                           global_step=i+1)
    else:
        # Here's where you're restoring the variables w and b.
        # Note that the graph is exactly as it was when the variables were
        # saved in a prior training run.
        ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
        if ckpt and ckpt.model_checkpoint_path:
            saver.restore(sess, ckpt.model_checkpoint_path)
        else:
            ...no checkpoint found...

        # Now you can run the model to get predictions
        batch_x = ...load some data...
        predictions = sess.run(y_hat, feed_dict={x: batch_x})

下面是变量文档，涵盖了保存和恢复。这是保存程序的文档。

我的环境:Python 3.6, Tensorflow 1.3.0

虽然有很多解决方案，但大多数都是基于tf.train.Saver。当我们加载由Saver保存的.ckpt文件时，我们必须要么重新定义张量流网络，要么使用一些奇怪且难以记住的名称，例如:“placehold_0:0”,“密集/亚当/重量:0”。这里我推荐使用tf。saved_model，下面给出的一个最简单的例子，你可以从为TensorFlow模型服务中学到更多:

保存模型:

import tensorflow as tf

# define the tensorflow network and do some trains
x = tf.placeholder("float", name="x")
w = tf.Variable(2.0, name="w")
b = tf.Variable(0.0, name="bias")

h = tf.multiply(x, w)
y = tf.add(h, b, name="y")
sess = tf.Session()
sess.run(tf.global_variables_initializer())

# save the model
export_path =  './savedmodel'
builder = tf.saved_model.builder.SavedModelBuilder(export_path)

tensor_info_x = tf.saved_model.utils.build_tensor_info(x)
tensor_info_y = tf.saved_model.utils.build_tensor_info(y)

prediction_signature = (
  tf.saved_model.signature_def_utils.build_signature_def(
      inputs={'x_input': tensor_info_x},
      outputs={'y_output': tensor_info_y},
      method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))

builder.add_meta_graph_and_variables(
  sess, [tf.saved_model.tag_constants.SERVING],
  signature_def_map={
      tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
          prediction_signature 
  },
  )
builder.save()

加载模型:

import tensorflow as tf
sess=tf.Session() 
signature_key = tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
input_key = 'x_input'
output_key = 'y_output'

export_path =  './savedmodel'
meta_graph_def = tf.saved_model.loader.load(
           sess,
          [tf.saved_model.tag_constants.SERVING],
          export_path)
signature = meta_graph_def.signature_def

x_tensor_name = signature[signature_key].inputs[input_key].name
y_tensor_name = signature[signature_key].outputs[output_key].name

x = sess.graph.get_tensor_by_name(x_tensor_name)
y = sess.graph.get_tensor_by_name(y_tensor_name)

y_out = sess.run(y, {x: 3.0})

模型有两个部分，模型定义，由Supervisor保存为图。模型目录中的PBTXT和张量的数值，保存到检查点文件，如model.ckpt-1003418。

可以使用tf恢复模型定义。import_graph_def，并且使用Saver恢复权重。

然而，Saver使用特殊的集合保存附加到模型Graph的变量列表，并且这个集合没有使用import_graph_def初始化，所以您目前不能同时使用这两者(这在我们的路线图中进行修复)。现在，您必须使用Ryan Sepassi的方法——手动构造具有相同节点名称的图，并使用Saver将权重加载到其中。

(或者，您可以通过使用import_graph_def，手动创建变量和使用tf.add_to_collection(tf.GraphKeys. collection)来破解它。变量，变量)为每个变量，然后使用Saver)

如果它是一个内部保存的模型，您只需为所有变量指定一个恢复器为

restorer = tf.train.Saver(tf.all_variables())

并使用它来恢复当前会话中的变量:

restorer.restore(self._sess, model_file)

对于外部模型，您需要指定从它的变量名到您的变量名的映射。您可以使用该命令查看模型变量名

python /path/to/tensorflow/tensorflow/python/tools/inspect_checkpoint.py --file_name=/path/to/pretrained_model/model.ckpt

inspect_checkpoint.py脚本可以在`。tensorflow源码的/tensorflow/python/tools文件夹。

为了指定映射，你可以使用我的Tensorflow-Worklab，它包含一组类和脚本来训练和再训练不同的模型。它包括一个再训练ResNet模型的例子，位于这里

下面是我对这两种基本情况的简单解决方案，这两种情况的不同之处在于您是想从文件加载图形还是在运行时构建它。

这个答案适用于Tensorflow 0.12+(包括1.0)。

在代码中重建图形

储蓄

graph = ... # build the graph
saver = tf.train.Saver()  # create the saver after the graph
with ... as sess:  # your session object
    saver.save(sess, 'my-model')

加载

graph = ... # build the graph
saver = tf.train.Saver()  # create the saver after the graph
with ... as sess:  # your session object
    saver.restore(sess, tf.train.latest_checkpoint('./'))
    # now you can use the graph, continue training or whatever

还从文件中加载图形

当使用这种技术时，确保所有的层/变量都显式地设置了唯一的名称。否则Tensorflow将使名称本身是唯一的，因此它们将不同于存储在文件中的名称。在前一种技术中，这不是问题，因为名称在加载和保存时都以相同的方式“损坏”。

储蓄

graph = ... # build the graph

for op in [ ... ]:  # operators you want to use after restoring the model
    tf.add_to_collection('ops_to_restore', op)

saver = tf.train.Saver()  # create the saver after the graph
with ... as sess:  # your session object
    saver.save(sess, 'my-model')

加载

with ... as sess:  # your session object
    saver = tf.train.import_meta_graph('my-model.meta')
    saver.restore(sess, tf.train.latest_checkpoint('./'))
    ops = tf.get_collection('ops_to_restore')  # here are your operators in the same order in which you saved them to the collection