1. tensorflow-iris-example.git
  2. Commit 8d83cd4a008f228cd8ede38c98fd3aec393cb5ca
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실행 파일 추가

Nepirity Corp authored on17/04/2018 15:34:56
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.gitignore
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 *.swp
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+model
iris_data.py
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+import pandas as pd
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+import tensorflow as tf
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+
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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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+
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+CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth',
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+                    'PetalLength', 'PetalWidth', 'Species']
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+SPECIES = ['Setosa', 'Versicolor', 'Virginica']
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+
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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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+
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+    return train_path, test_path
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+
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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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+
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+    train = pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=0)
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+    train_x, train_y = train, train.pop(y_name)
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+
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+    test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0)
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+    test_x, test_y = test, test.pop(y_name)
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+
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+    return (train_x, train_y), (test_x, test_y)
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+
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+
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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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+
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+    # Shuffle, repeat, and batch the examples.
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+    dataset = dataset.shuffle(1000).repeat().batch(batch_size)
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+
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+    # Return the dataset.
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+    return dataset
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+
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+
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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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+    if labels is None:
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+        # No labels, use only features.
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+        inputs = features
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+    else:
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+        inputs = (features, labels)
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+
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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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+
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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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+    dataset = dataset.batch(batch_size)
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+
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+    # Return the dataset.
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+    return dataset
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+
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+
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+# The remainder of this file contains a simple example of a csv parser,
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+#     implemented using a the `Dataset` class.
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+
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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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+
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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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+
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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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+
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+    # Separate the label from the features
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+    label = features.pop('Species')
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+
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+    return features, label
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+
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+
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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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+
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+    # Parse each line.
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+    dataset = dataset.map(_parse_line)
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+
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+    # Shuffle, repeat, and batch the examples.
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+    dataset = dataset.shuffle(1000).repeat().batch(batch_size)
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+
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+    # Return the dataset.
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+    return dataset
tutorial.py
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+import tensorflow as tf
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+import numpy as np
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+import iris_data
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+
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+(train_x, train_y), (test_x, test_y) = iris_data.load_data()
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+
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+print np.shape(train_x)
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+print np.shape(train_y)
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+
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+feature_columns = []
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+for key in train_x.keys():
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+    feature_columns.append(tf.feature_column.numeric_column(key))
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+
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+classifier = tf.estimator.DNNClassifier(
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+    feature_columns = feature_columns,
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+    hidden_units=[10, 10],
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+    n_classes = 3,
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+)
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+
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+batch_size = 100
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+steps = 1000
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+
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+def train_input_fn(x, y, batch_size):
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+    dataset = tf.data.Dataset.from_tensor_slices((dict(x), y))
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+    dataset = dataset.shuffle(1000).repeat().batch(batch_size)
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+
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+    return dataset
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+
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+classifier.train(input_fn = lambda:train_input_fn(train_x, train_y, batch_size), steps = steps)
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+
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+def test_input_fn(x, y, batch_size):
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+    x=dict(x)
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+    if y is None:
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+        inputs = x
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+    else:
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+        inputs = (x, y)
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+
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+    dataset = tf.data.Dataset.from_tensor_slices(inputs)
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+    dataset = dataset.batch(batch_size)
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+
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+    return dataset
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+
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+result = classifier.evaluate(input_fn = lambda:iris_data.eval_input_fn(test_x, test_y, batch_size))
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+print result
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+
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+predict_x = {
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+    'SepalLength': [5.1, 5.9, 6.9],
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+    'SepalWidth': [3.3, 3.0, 3.1],
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+    'PetalLength': [1.7, 4.2, 5.4],
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+    'PetalWidth': [0.5, 1.5, 2.1],
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+}
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+
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+predictions = classifier.predict(input_fn=lambda:test_input_fn(predict_x, y=None, batch_size=batch_size))
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+
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+for p in predictions:
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+    print p