在Tensorflow中训练一个模型后:
如何拯救训练过的模型? 您以后如何恢复这个保存的模型?
在Tensorflow中训练一个模型后:
如何拯救训练过的模型? 您以后如何恢复这个保存的模型?
当前回答
在@Vishnuvardhan Janapati的回答之后,这里是另一种在TensorFlow 2.0.0下保存和重载自定义层/度量/损失模型的方法
import tensorflow as tf
from tensorflow.keras.layers import Layer
from tensorflow.keras.utils.generic_utils import get_custom_objects
# custom loss (for example)
def custom_loss(y_true,y_pred):
return tf.reduce_mean(y_true - y_pred)
get_custom_objects().update({'custom_loss': custom_loss})
# custom loss (for example)
class CustomLayer(Layer):
def __init__(self, ...):
...
# define custom layer and all necessary custom operations inside custom layer
get_custom_objects().update({'CustomLayer': CustomLayer})
通过这种方式,一旦您执行了这些代码,并使用tf.keras.models保存了您的模型。Save_model或model。save或ModelCheckpoint回调,您可以重新加载您的模型,而不需要精确的自定义对象,就像这样简单
new_model = tf.keras.models.load_model("./model.h5"})
其他回答
如果您使用tf.train.MonitoredTrainingSession作为默认会话,则不需要添加额外的代码来执行保存/恢复操作。只需将检查点目录名称传递给MonitoredTrainingSession的构造函数,它将使用会话挂钩来处理这些。
使用tf.train.Saver保存模型。记住,如果想要减小模型大小,就需要指定var_list。val_list可以是:
特遣部队。trainable_variables或 tf.global_variables。
如第6255期所述:
use '**./**model_name.ckpt'
saver.restore(sess,'./my_model_final.ckpt')
而不是
saver.restore('my_model_final.ckpt')
对于张量流2.0,它非常简单
#保存模型 model.save(“path_to_my_model.h5”)
恢复:
new_model = tensorflow.keras.models.load_model('path_to_my_model.h5')
根据新的Tensorflow版本,tf.train.Checkpoint是保存和恢复模型的最佳方式:
Checkpoint.save and Checkpoint.restore write and read object-based checkpoints, in contrast to tf.train.Saver which writes and reads variable.name based checkpoints. Object-based checkpointing saves a graph of dependencies between Python objects (Layers, Optimizers, Variables, etc.) with named edges, and this graph is used to match variables when restoring a checkpoint. It can be more robust to changes in the Python program, and helps to support restore-on-create for variables when executing eagerly. Prefer tf.train.Checkpoint over tf.train.Saver for new code.
这里有一个例子:
import tensorflow as tf
import os
tf.enable_eager_execution()
checkpoint_directory = "/tmp/training_checkpoints"
checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
checkpoint = tf.train.Checkpoint(optimizer=optimizer, model=model)
status = checkpoint.restore(tf.train.latest_checkpoint(checkpoint_directory))
for _ in range(num_training_steps):
optimizer.minimize( ... ) # Variables will be restored on creation.
status.assert_consumed() # Optional sanity checks.
checkpoint.save(file_prefix=checkpoint_prefix)
这里有更多信息和示例。