![]() This modification allows the correct import of the Keras module within TensorFlow. Now, when you import Keras from TensorFlow, the naming conflict should be resolved. The process of selecting the right set of hyperparameters for your machine learning (ML) application is called hyperparameter tuning or hypertuning. With the following modified code: import typing as _typing The Keras Tuner is a library that helps you pick the optimal set of hyperparameters for your TensorFlow program. _current_module._path_ = + _current_module._path_ You will train your own word embeddings using a simple Keras model for a sentiment classification task, and then visualize them in the Embedding Projector (shown in the image below). _module_dir = _module_util.get_parent_dir_for_name(_keras_module) Setup Run in Google Colab View source on GitHub Download notebook This tutorial contains an introduction to word embeddings. Keras = _LazyLoader("keras", globals(), _keras_module) Replace this existing code block _keras_module = "keras.api._v2.keras" Look for the section of code around line 387 that mentions the keras module. Open the _init_.py file in a text editor. This should navigate you directly to the _init_.py file. The file path is typically site-packages/tensorflow/_init_.py.Īlternatively, you can quickly open the file by clicking on the word "tensorflow" in your import statement and pressing CTRL (or CMD on Mac) simultaneously. Locate the _init_.py file within your TensorFlow installation. Follow the steps below to resolve the issue: It can be resolved by modifying the _init_.py file in the TensorFlow package. This error typically occurs when there is a naming conflict in TensorFlow while trying to import Keras. The most common type of model is the Sequential model, which is a linear stack of layers. The next two sections look at each type more closely. In your above code snippet, it doesn't make sense to compute twice the probabilities of the same class. There are two ways to create a model using the Layers API: A sequential model, and a functional model. Theoretically, the second one should only work for 2.2.0 = 2.5.0 because of keras lazy loading introduced in TF 2.5, and haven't been fixed yet (Release 2.9.1) though related commits have been merged into master branch. predictprobmodel.predict ( testa,testb) predictclassesnp.argmax (predictprob,axis1) Note, It's NOT the solution. If tensorflow has keras attribute, then it uses the attribute, otherwise it import keras as a submodule. The first one need tensorflow has keras attribute with correct type statically during type checking.īut the second one need tensorflow._path_ contains keras module statically during type checking. # if foo is a submodule but not an attribute, this is (roughly) equivalent to # if foo is an attribute, this is (roughly) equivalent to # if foo is a submodule but not an attribute, this will fail
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