Como classificar dados estruturados usando camadas de pré-processamento do Keras avaliações

There was an error in the code, because of which the notebook wasn't executable.

Abhirama G. · Revisado há almost 3 years

good

utpal d. · Revisado há almost 3 years

Jeff H. · Revisado há almost 3 years

Diogo F. · Revisado há almost 3 years

AttributeError: 'StringLookup' object has no attribute 'vocabulary_size' def get_category_encoding_layer(name, dataset, dtype, max_tokens=None): # Create a StringLookup layer which will turn strings into integer indices if dtype == 'string': index = preprocessing.StringLookup(max_tokens=max_tokens) else: index = preprocessing.IntegerLookup(max_tokens=max_tokens) # TODO # Prepare a Dataset that only yields our feature feature_ds = dataset.map(lambda x, y: x[name]) # Learn the set of possible values and assign them a fixed integer index. index.adapt(feature_ds) # Create a Discretization for our integer indices. encoder = preprocessing.CategoryEncoding(num_tokens=index.vocabulary_size()) # Apply one-hot encoding to our indices. The lambda function captures the # layer so we can use them, or include them in the functional model later. return lambda feature: encoder(index(feature))

Nikolas H. · Revisado há almost 3 years

very clear lab, thanks!

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