![]() ![]() To train a model, you need (inputs, labels) pairs, so pass (features, labels) and om_tensor_slices will return the needed pairs of slices: numeric_dataset = tf._tensor_slices((numeric_features, target)) Each row is initially a vector of values. If you want to apply tf.data transformations to a DataFrame of a uniform dtype, the om_tensor_slices method will create a dataset that iterates over the rows of the DataFrame. When you pass the DataFrame as the x argument to Model.fit, Keras treats the DataFrame as it would a NumPy array: model = get_basic_model() Use the normalization layer as the first layer of a simple model: def get_basic_model(): To set the layer's mean and standard-deviation before running it be sure to call the Normalization.adapt method: normalizer = tf.(axis=-1)Ĭall the layer on the first three rows of the DataFrame to visualize an example of the output from this layer: normalizer(numeric_features.iloc) The first step is to normalize the input ranges. With Model.fitĪ DataFrame, interpreted as a single tensor, can be used directly as an argument to the Model.fit method.īelow is an example of training a model on the numeric features of the dataset. In general, if an object can be converted to a tensor with tf.convert_to_tensor it can be passed anywhere you can pass a tf.Tensor. To convert it to a tensor, use tf.convert_to_tensor: tf.convert_to_tensor(numeric_features) The DataFrame can be converted to a NumPy array using the DataFrame.values property or numpy.array(df). Take the numeric features from the dataset (skip the categorical features for now): numeric_feature_names = This works because the pandas.DataFrame class supports the _array_ protocol, and TensorFlow's tf.convert_to_tensor function accepts objects that support the protocol. If your data has a uniform datatype, or dtype, it's possible to use a pandas DataFrame anywhere you could use a NumPy array. You will build models to predict the label contained in the target column. This is what the data looks like: df.head() Read the CSV file using pandas: df = pd.read_csv(csv_file) ![]() Read data using pandas import pandas as pdĭownload the CSV file containing the heart disease dataset: csv_file = tf._file('heart.csv', '')ġ6384/13273 - 0s 0us/stepĢ4576/13273 - 0s 0us/step You will use this information to predict whether a patient has heart disease, which is a binary classification task. ![]() Each row describes a patient, and each column describes an attribute. There are several hundred rows in the CSV. You will use a small heart disease dataset provided by the UCI Machine Learning Repository. This tutorial provides examples of how to load pandas DataFrames into TensorFlow. ![]()
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