Split iris_data.py from estimator examples. (#2954)

* Split iris data

* move shared code to iris_data.py

* add minimal csv example

* remove unused pandas import

* Use sparse softmax loss to avoid warning

samples/core/get_started/custom_estimator.py

@@ -17,67 +17,15 @@ from __future__ import division
 from __future__ import print_function
 
 import argparse
-import pandas as pd
 import tensorflow as tf
 
+import iris_data
+
 parser = argparse.ArgumentParser()
 parser.add_argument('--batch_size', default=100, type=int, help='batch size')
 parser.add_argument('--train_steps', default=1000, type=int,
                     help='number of training steps')
 
-TRAIN_URL = "http://download.tensorflow.org/data/iris_training.csv"
-TEST_URL = "http://download.tensorflow.org/data/iris_test.csv"
-
-CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth',
-                    'PetalLength', 'PetalWidth', 'Species']
-SPECIES = ['Sentosa', 'Versicolor', 'Virginica']
-
-
-def load_data(y_name='Species'):
-    """Returns the iris dataset as (train_x, train_y), (test_x, test_y)."""
-    train_path = tf.keras.utils.get_file(TRAIN_URL.split('/')[-1], TRAIN_URL)
-    train = pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=0)
-    train_x, train_y = train, train.pop(y_name)
-
-    test_path = tf.keras.utils.get_file(TEST_URL.split('/')[-1], TEST_URL)
-    test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0)
-    test_x, test_y = test, test.pop(y_name)
-
-    return (train_x, train_y), (test_x, test_y)
-
-
-
-def train_input_fn(features, labels, batch_size):
-    """An input function for training"""
-    # Convert the inputs to a Dataset.
-    dataset = tf.data.Dataset.from_tensor_slices((features, labels))
-
-    # Shuffle, repeat, and batch the examples.
-    dataset = dataset.shuffle(1000).repeat().batch(batch_size)
-
-    # Return the read end of the pipeline.
-    return dataset.make_one_shot_iterator().get_next()
-
-
-def eval_input_fn(features, labels=None, batch_size=None):
-    """An input function for evaluation or prediction"""
-    if labels is None:
-        # No labels, use only features.
-        inputs = features
-    else:
-        inputs = (features, labels)
-
-    # Convert the inputs to a Dataset.
-    dataset = tf.data.Dataset.from_tensor_slices(inputs)
-
-    # Batch the examples
-    assert batch_size is not None, "batch_size must not be None"
-    dataset = dataset.batch(batch_size)
-
-    # Return the read end of the pipeline.
-    return dataset.make_one_shot_iterator().get_next()
-
-
 def my_model(features, labels, mode, params):
     """DNN with three hidden layers, and dropout of 0.1 probability."""
     # Create three fully connected layers each layer having a dropout
@@ -99,12 +47,8 @@ def my_model(features, labels, mode, params):
         }
         return tf.estimator.EstimatorSpec(mode, predictions=predictions)
 
-    # Convert the labels to a one-hot tensor of shape (length of features, 3)
-    # and with a on-value of 1 for each one-hot vector of length 3.
-    onehot_labels = tf.one_hot(labels, 3, 1, 0)
     # Compute loss.
-    loss = tf.losses.softmax_cross_entropy(
-        onehot_labels=onehot_labels, logits=logits)
+    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
 
     # Compute evaluation metrics.
     accuracy = tf.metrics.accuracy(labels=labels,
@@ -129,9 +73,7 @@ def main(argv):
     args = parser.parse_args(argv[1:])
 
     # Fetch the data
-    (train_x, train_y), (test_x, test_y) = load_data()
-    train_x = dict(train_x)
-    test_x = dict(test_x)
+    (train_x, train_y), (test_x, test_y) = iris_data.load_data()
 
     # Feature columns describe how to use the input.
     my_feature_columns = []
@@ -151,12 +93,12 @@ def main(argv):
 
     # Train the Model.
     classifier.train(
-        input_fn=lambda:train_input_fn(train_x, train_y, args.batch_size),
+        input_fn=lambda:iris_data.train_input_fn(train_x, train_y, args.batch_size),
         steps=args.train_steps)
 
     # Evaluate the model.
     eval_result = classifier.evaluate(
-        input_fn=lambda:eval_input_fn(test_x, test_y, args.batch_size))
+        input_fn=lambda:iris_data.eval_input_fn(test_x, test_y, args.batch_size))
 
     print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))
 
@@ -170,14 +112,18 @@ def main(argv):
     }
 
     predictions = classifier.predict(
-        input_fn=lambda:eval_input_fn(predict_x, batch_size=args.batch_size))
+        input_fn=lambda:iris_data.eval_input_fn(predict_x,
+                                                labels=None,
+                                                batch_size=args.batch_size))
 
     for pred_dict, expec in zip(predictions, expected):
         template = ('\nPrediction is "{}" ({:.1f}%), expected "{}"')
 
         class_id = pred_dict['class_ids'][0]
         probability = pred_dict['probabilities'][class_id]
-        print(template.format(SPECIES[class_id], 100 * probability, expec))
+
+        print(template.format(iris_data.SPECIES[class_id],
+                              100 * probability, expec))
 
 
 if __name__ == '__main__':

samples/core/get_started/estimator_test.py

@@ -23,6 +23,7 @@ import pandas as pd
 
 from six.moves import StringIO
 
+import iris_data
 import custom_estimator
 import premade_estimator
 
@@ -35,7 +36,7 @@ FOUR_LINES = "\n".join([
 def four_lines_data():
   text = StringIO(FOUR_LINES)
 
-  df = pd.read_csv(text, names=premade_estimator.CSV_COLUMN_NAMES)
+  df = pd.read_csv(text, names=iris_data.CSV_COLUMN_NAMES)
 
   xy = (df, df.pop("Species"))
   return xy, xy

samples/core/get_started/iris_data.py

+import pandas as pd
+import tensorflow as tf
+
+TRAIN_URL = "http://download.tensorflow.org/data/iris_training.csv"
+TEST_URL = "http://download.tensorflow.org/data/iris_test.csv"
+
+CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth',
+                    'PetalLength', 'PetalWidth', 'Species']
+SPECIES = ['Sentosa', 'Versicolor', 'Virginica']
+
+def maybe_download():
+    train_path = tf.keras.utils.get_file(TRAIN_URL.split('/')[-1], TRAIN_URL)
+    test_path = tf.keras.utils.get_file(TEST_URL.split('/')[-1], TEST_URL)
+
+    return train_path, test_path
+
+def load_data(y_name='Species'):
+    """Returns the iris dataset as (train_x, train_y), (test_x, test_y)."""
+    train_path, test_path = maybe_download()
+
+    train = pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=0)
+    train_x, train_y = train, train.pop(y_name)
+
+    test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0)
+    test_x, test_y = test, test.pop(y_name)
+
+    return (train_x, train_y), (test_x, test_y)
+
+
+def train_input_fn(features, labels, batch_size):
+    """An input function for training"""
+    # Convert the inputs to a Dataset.
+    dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
+
+    # Shuffle, repeat, and batch the examples.
+    dataset = dataset.shuffle(1000).repeat().batch(batch_size)
+
+    # Return the read end of the pipeline.
+    return dataset.make_one_shot_iterator().get_next()
+
+
+def eval_input_fn(features, labels, batch_size):
+    """An input function for evaluation or prediction"""
+    features=dict(features)
+    if labels is None:
+        # No labels, use only features.
+        inputs = features
+    else:
+        inputs = (features, labels)
+
+    # Convert the inputs to a Dataset.
+    dataset = tf.data.Dataset.from_tensor_slices(inputs)
+
+    # Batch the examples
+    assert batch_size is not None, "batch_size must not be None"
+    dataset = dataset.batch(batch_size)
+
+    # Return the read end of the pipeline.
+    return dataset.make_one_shot_iterator().get_next()
+
+
+# The remainder of this file contains a simple example of a csv parser,
+#     implemented using a the `Dataset` class.
+
+# `tf.parse_csv` sets the types of the outputs to match the examples given in
+#     the `record_defaults` argument.
+CSV_TYPES = [[0.0], [0.0], [0.0], [0.0], [0]]
+
+def _parse_line(line):
+    # Decode the line into its fields
+    fields = tf.decode_csv(line, record_defaults=CSV_TYPES)
+
+    # Pack the result into a dictionary
+    features = dict(zip(CSV_COLUMN_NAMES, fields))
+
+    # Separate the label from the features
+    label = features.pop('Species')
+
+    return features, label
+
+
+def csv_input_fn(csv_path, batch_size):
+    # Create a dataset containing the text lines.
+    dataset = tf.data.TextLineDataset(csv_path).skip(1)
+
+    # Parse each line.
+    dataset = dataset.map(_parse_line)
+
+    # Shuffle, repeat, and batch the examples.
+    dataset = dataset.shuffle(1000).repeat().batch(batch_size)
+
+    # Return the read end of the pipeline.
+    return dataset.make_one_shot_iterator().get_next()
\ No newline at end of file

samples/core/get_started/premade_estimator.py

@@ -17,73 +17,21 @@ from __future__ import division
 from __future__ import print_function
 
 import argparse
-import pandas as pd
 import tensorflow as tf
 
+import iris_data
+
+
 parser = argparse.ArgumentParser()
 parser.add_argument('--batch_size', default=100, type=int, help='batch size')
 parser.add_argument('--train_steps', default=1000, type=int,
                     help='number of training steps')
 
-TRAIN_URL = "http://download.tensorflow.org/data/iris_training.csv"
-TEST_URL = "http://download.tensorflow.org/data/iris_test.csv"
-
-CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth',
-                    'PetalLength', 'PetalWidth', 'Species']
-SPECIES = ['Sentosa', 'Versicolor', 'Virginica']
-
-
-def load_data(y_name='Species'):
-    """Returns the iris dataset as (train_x, train_y), (test_x, test_y)."""
-    train_path = tf.keras.utils.get_file(TRAIN_URL.split('/')[-1], TRAIN_URL)
-    train = pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=0)
-    train_x, train_y = train, train.pop(y_name)
-
-    test_path = tf.keras.utils.get_file(TEST_URL.split('/')[-1], TEST_URL)
-    test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0)
-    test_x, test_y = test, test.pop(y_name)
-
-    return (train_x, train_y), (test_x, test_y)
-
-
-def train_input_fn(features, labels, batch_size):
-    """An input function for training"""
-    # Convert the inputs to a Dataset.
-    dataset = tf.data.Dataset.from_tensor_slices((features, labels))
-
-    # Shuffle, repeat, and batch the examples.
-    dataset = dataset.shuffle(1000).repeat().batch(batch_size)
-
-    # Return the read end of the pipeline.
-    return dataset.make_one_shot_iterator().get_next()
-
-
-def eval_input_fn(features, labels=None, batch_size=None):
-    """An input function for evaluation or prediction"""
-    if labels is None:
-        # No labels, use only features.
-        inputs = features
-    else:
-        inputs = (features, labels)
-
-    # Convert the inputs to a Dataset.
-    dataset = tf.data.Dataset.from_tensor_slices(inputs)
-
-    # Batch the examples
-    assert batch_size is not None, "batch_size must not be None"
-    dataset = dataset.batch(batch_size)
-
-    # Return the read end of the pipeline.
-    return dataset.make_one_shot_iterator().get_next()
-
-
 def main(argv):
     args = parser.parse_args(argv[1:])
 
     # Fetch the data
-    (train_x, train_y), (test_x, test_y) = load_data()
-    train_x = dict(train_x)
-    test_x = dict(test_x)
+    (train_x, train_y), (test_x, test_y) = iris_data.load_data()
 
     # Feature columns describe how to use the input.
     my_feature_columns = []
@@ -100,12 +48,14 @@ def main(argv):
 
     # Train the Model.
     classifier.train(
-        input_fn=lambda:train_input_fn(train_x, train_y, args.batch_size),
+        input_fn=lambda:iris_data.train_input_fn(train_x, train_y,
+                                                 args.batch_size),
         steps=args.train_steps)
 
     # Evaluate the model.
     eval_result = classifier.evaluate(
-        input_fn=lambda:eval_input_fn(test_x, test_y, args.batch_size))
+        input_fn=lambda:iris_data.eval_input_fn(test_x, test_y,
+                                                args.batch_size))
 
     print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))
 
@@ -119,14 +69,18 @@ def main(argv):
     }
 
     predictions = classifier.predict(
-        input_fn=lambda:eval_input_fn(predict_x, batch_size=args.batch_size))
+        input_fn=lambda:iris_data.eval_input_fn(predict_x,
+                                                labels=None,
+                                                batch_size=args.batch_size))
 
     for pred_dict, expec in zip(predictions, expected):
         template = ('\nPrediction is "{}" ({:.1f}%), expected "{}"')
 
         class_id = pred_dict['class_ids'][0]
         probability = pred_dict['probabilities'][class_id]
-        print(template.format(SPECIES[class_id], 100 * probability, expec))
+
+        print(template.format(iris_data.SPECIES[class_id],
+                              100 * probability, expec))
 
 
 if __name__ == '__main__':