![]() Let’s start by preparing two empty lists: We also add all of our features to a list called all_inputs. return lambda feature: encoder(index(feature))Īpply the preprocessing utility functions defined earlier on our numerical and categorical features and store it into a list called encoded_features The lambda function captures the # layer, so you can use them, or include them in the Keras Functional model later. # Apply multi-hot encoding to the indices. # Learn the set of possible values and assign them a fixed integer index. # Prepare a `tf.data.Dataset` that only yields the feature. # Otherwise, create a layer that turns integer values into integer indices. # Create a layer that turns strings into integer indices. ![]() Define a new utility function that returns a layer which applies feature-wise normalization to numerical features using that Keras preprocessing layer:ĭef get_category_encoding_layer(name, dataset, dtype, max_tokens = None):.Tf.: Turns integer categorical values into integer indices. Tf.: Turns string categorical values into integer indices. Tf.: Turns integer categorical features into one-hot, multi-hot, or tf-idf dense representations. Tf.: Performs feature-wise normalization of input features. In this tutorial, you will use the following preprocessing layers to demonstrate how to perform preprocessing, structured data encoding, and feature engineering: Next, we define utility functions to do the feature preprocessing operations. Let’s download the data and load it into a Pandas dataframe:īatch_size = 32 ds_train = dataframe_to_dataset(df_train, shuffle = True, batch_size =batch_size)ĭs_val = dataframe_to_dataset(df_val, shuffle = True, batch_size =batch_size).Number of major vessels (0-3) featureored by fluoroscopyĭiagnosis of heart disease (1 = true 0 = false) ST depression induced by exercise relative to rest Resting electrocardiogram results (0, 1, 2)Įxercise induced angina (1 = yes 0 = no) Resting blood pressure (in mm Hg on admission)įasting blood sugar in 120 mg/dl (1 = true 0 = false) We use the features below to predict whether a patient has a heart disease ( Target).
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