That actually exists, if you set the environment variable TF2_BEHAVIOR to 1 it will enable 2.x behavior. I have not found documentation about it, but you can see it in a comment in the source code:
# TF2 behavior is enabled if either 1) enable_v2_behavior() is called or # 2) the TF2_BEHAVIOR = 1 environment variable is set.In the latter case, # the modules below independently check if tf2.enabled().
I tested it in 1.15.0 and it seems to work:
import os os.environ['TF2_BEHAVIOR'] = '1' import tensorflow as tf print(tf.constant([1, 2, 3])) # Eager mode is enabled # tf.Tensor([1 2 3], shape = (3, ), dtype = int32) print(tf.TensorShape([1, 2])[1]) # TensorShape dimensions are int # 2
Last updated 2022-05-24 UTC.
Disables TensorFlow 2.x behaviors.
tf.compat.v1.disable_v2_behavior()
As part of our migration to TensorFlow 2 I'd anycodings_tensorflow like to enable TensorFlow v2 behaviour in anycodings_tensorflow TensorFlow v1 (specifically TensorFlow anycodings_tensorflow 1.14). I'm aware that I can call anycodings_tensorflow tf.enable_v2_behavior() to do this, but I'd anycodings_tensorflow like to do this implicitly (via an anycodings_tensorflow environment variable or similar) without anycodings_tensorflow having to explicitly call this function.,The docs for enable_v2_behavior state "This anycodings_tensorflow function is called in the main TensorFlow anycodings_tensorflow __init__.py file, user should not need to anycodings_tensorflow call it, except during complex migrations.", anycodings_tensorflow but this doesn't appear to be true (at least anycodings_tensorflow in TensorFlow 1.14; this is true for anycodings_tensorflow TensorFlow 2.0 I guess).,That actually exists, if you set the anycodings_tensorflow environment variable TF2_BEHAVIOR to 1 anycodings_tensorflow it will enable 2.x behavior. I have not anycodings_tensorflow found documentation about it, but you anycodings_tensorflow can see it in a comment in the source anycodings_tensorflow code:,How to duplicate and store a final copy of an array that would not change value enven after passing it using methods
That actually exists, if you set the anycodings_tensorflow environment variable TF2_BEHAVIOR to 1 anycodings_tensorflow it will enable 2.x behavior. I have not anycodings_tensorflow found documentation about it, but you anycodings_tensorflow can see it in a comment in the source anycodings_tensorflow code:
# TF2 behavior is enabled if either 1) enable_v2_behavior() is called or # 2) the TF2_BEHAVIOR = 1 environment variable is set.In the latter case, # the modules below independently check if tf2.enabled().
I tested it in 1.15.0 and it seems to anycodings_tensorflow work:
import os os.environ['TF2_BEHAVIOR'] = '1' import tensorflow as tf print(tf.constant([1, 2, 3])) # Eager mode is enabled # tf.Tensor([1 2 3], shape = (3, ), dtype = int32) print(tf.TensorShape([1, 2])[1]) # TensorShape dimensions are int # 2
Resource variables, v1.enable_resource_variables(): Some code may depends on non-deterministic behaviors enabled by TF reference variables. Resource variables are locked while being written to, and so provide more intuitive consistency guarantees.,Resource variables, v1.enable_resource_variables(): Some code may depends on non-deterministic behaviors enabled by TF reference variables. Resource variables are locked while being written to, and so provide more intuitive consistency guarantees. This may change behavior in edge cases. This may create extra copies and can have higher memory usage. This can be disabled by passing use_resource=False to the tf.Variable constructor. ,Eager execution, v1.enable_eager_execution() : Any code that implicitly uses a tf.Graph will fail. Be sure to wrap this code in a with tf.Graph().as_default() context.,Eager execution, v1.enable_eager_execution() : Any code that implicitly uses a tf.Graph will fail. Be sure to wrap this code in a with tf.Graph().as_default() context.
In[0]:
# @title Licensed under the Apache License, Version 2.0(the "License");
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# limitations under the License.It is still possible to run 1.X code, unmodified (except for contrib), in TensorFlow 2.0:
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()They can be passed directly to the tf.keras.Model.fit method.
model.fit(dataset, epochs = 5)
They can be iterated over directly standard Python:
for example_batch, label_batch in dataset:
breakIn[0]:
import tensorflow as tf
import tensorflow_datasets as tfdsimport tensorflow as tf
import tensorflow_datasets as tfdsimport tensorflow as tf
import tensorflow_datasets as tfds