Import “tensorflow.keras” could not be resolved after upgrading to TensorFlow 2.8.0
I had the same problem and resolved it by importing it as from tensorflow.python.keras.layers import Dense
tensorflow2.0
Should I use @tf.function for all functions?
TLDR: It depends on your function and whether you are in production or development. Don’t use tf.function if you want to be able to debug your function easily, or if it falls under the limitations of AutoGraph or tf.v1 code compatibility. I would highly recommend watching the Inside TensorFlow talks about AutoGraph and Functions, not … Read more
How to use windows created by the Dataset.window() method in TensorFlow 2.0?
The solution is to call flat_map() like this: dataset = dataset.flat_map(lambda window: window.batch(5)) Now each item in the dataset is a window, so you can split it like this: dataset = dataset.map(lambda window: (window[:-1], window[-1:])) So the full code is: import tensorflow as tf dataset = tf.data.Dataset.from_tensor_slices(tf.range(10)) dataset = dataset.window(5, shift=1, drop_remainder=True) dataset = dataset.flat_map(lambda … Read more
ModuleNotFoundError: No module named ‘tensorflow_core.estimator’ for tensorflow 2.1.0
TL;DR: Just solved this issue by making sure that both tensorflow and tensorflow-estimator were in the same version. (in my case, I needed to downgrade tensorflow-estimator, so conda install tensorflow-estimator=2.1.0 solved it for me) As you may have noticed, some tensorflow versions do not play well with certain GPUs, so I would first check some … Read more
Running the Tensorflow 2.0 code gives ‘ValueError: tf.function-decorated function tried to create variables on non-first call’. What am I doing wrong?
As you are trying to use function decorator in TF 2.0, please enable run function eagerly by using below line after importing TensorFlow: tf.config.experimental_run_functions_eagerly(True) Since the above is deprecated(no longer experimental?), please use the following instead: tf.config.run_functions_eagerly(True) If you want to know more do refer to this link.
Tensorboard not found as magic function in jupyter
UPDATE For newer TF versions (tensorflow>=1.14.0 & tensorflow != 2.0.0a0 – newer than TF2.0-alpha) load the extension like this %load_ext tensorboard OLD ANSWER The extension needs to be loaded first: %load_ext tensorboard.notebook %tensorboard –logdir {logs_base_dir}
module ‘tensorflow’ has no attribute ‘logging’
tf.logging was for Logging and Summary Operations and in TF 2.0 it has been removed in favor of the open-source absl-py, and to make the main tf.* namespace has functions that will be used more often. In TF.2 lesser used functions are gone or moved into sub-packages like tf.math So instead of tf.logging you could: … Read more
This model has not yet been built error on model.summary()
The error says what to do: This model has not yet been built. Build the model first by calling build() model.build(input_shape) # `input_shape` is the shape of the input data # e.g. input_shape = (None, 32, 32, 3) model.summary()
WARNING:tensorflow:sample_weight modes were coerced from … to [‘…’]
This seems like a bogus message. I get the same warning message after upgrading to TensorFlow 2.1, but I do not use any class weights or sample weights at all. I do use a generator that returns a tuple like this: return inputs, targets And now I just changed it to the following to make … Read more
RuntimeError: tf.placeholder() is not compatible with eager execution
I found an easy solution here: disable Tensorflow eager execution Basicaly it is: tf.compat.v1.disable_eager_execution() With this, you disable the default activate eager execution and you don’t need to touch the code much more.