wehub-resource-sync
89 lines
2.8 KiB
Python
89 lines
2.8 KiB
Python
# From https://github.com/tensorflow/models/blob/master/samples/core/get_started/iris_data.py
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# This file is the example used by TensorFlow to get users started. This code is used for testing.
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import pandas as pd
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import tensorflow as tf
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TRAIN_URL = "http://download.tensorflow.org/data/iris_training.csv"
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TEST_URL = "http://download.tensorflow.org/data/iris_test.csv"
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CSV_COLUMN_NAMES = ["SepalLength", "SepalWidth", "PetalLength", "PetalWidth", "Species"]
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SPECIES = ["Setosa", "Versicolor", "Virginica"]
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def maybe_download():
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train_path = tf.keras.utils.get_file(TRAIN_URL.split("/")[-1], TRAIN_URL)
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test_path = tf.keras.utils.get_file(TEST_URL.split("/")[-1], TEST_URL)
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return train_path, test_path
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def load_data(y_name="Species"):
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"""Returns the iris dataset as (train_x, train_y), (test_x, test_y)."""
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train_path, test_path = maybe_download()
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train = pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=0)
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train_y = train.pop(y_name)
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train_x = train
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test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0)
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test_y = test.pop(y_name)
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test_x = test
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return (train_x, train_y), (test_x, test_y)
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def train_input_fn(features, labels, batch_size):
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"""An input function for training"""
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# Convert the inputs to a Dataset.
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dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
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# Shuffle, repeat, and batch the examples.
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return dataset.shuffle(1000).repeat().batch(batch_size)
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def eval_input_fn(features, labels, batch_size):
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"""An input function for evaluation or prediction"""
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features = dict(features)
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# Use only features when labels are null.
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inputs = features if labels is None else (features, labels)
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# Convert the inputs to a Dataset.
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dataset = tf.data.Dataset.from_tensor_slices(inputs)
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# Batch the examples
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assert batch_size is not None, "batch_size must not be None"
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return dataset.batch(batch_size)
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# The remainder of this file contains a simple example of a csv parser,
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# implemented using the `Dataset` class.
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# `tf.parse_csv` sets the types of the outputs to match the examples given in
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# the `record_defaults` argument.
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CSV_TYPES = [[0.0], [0.0], [0.0], [0.0], [0]]
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def _parse_line(line):
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# Decode the line into its fields
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fields = tf.decode_csv(line, record_defaults=CSV_TYPES)
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# Pack the result into a dictionary
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features = dict(zip(CSV_COLUMN_NAMES, fields))
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# Separate the label from the features
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label = features.pop("Species")
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return features, label
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def csv_input_fn(csv_path, batch_size):
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# Create a dataset containing the text lines.
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dataset = tf.data.TextLineDataset(csv_path).skip(1)
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# Parse each line.
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dataset = dataset.map(_parse_line)
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# Shuffle, repeat, and batch the examples.
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return dataset.shuffle(1000).repeat().batch(batch_size)
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