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Unverified Commit 11e9c7ad authored by Mark Daoust's avatar Mark Daoust Committed by GitHub
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Cleanup get_started (#2824) 

* Add simplifications requested by get_started writers.

* More cleanup

* Clearer names and comments

* Use the keys from the train dict

* Use regular input_fn pattern

* Add comments to input functions
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samples/core/get_started/custom_estimator.py
View file @ 11e9c7ad
......@@ -11,7 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Example of DNNClassifier for Iris plant dataset."""
"""An Example of a custom Estimator for the Iris dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
......@@ -22,35 +22,60 @@ import tensorflow as tf
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', default=100, type=int, help='batch size')
parser.add_argument('--train_steps', default=200, type=int,
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"
COLUMNS = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Species']
CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth',
'PetalLength', 'PetalWidth', 'Species']
SPECIES = ['Sentosa', 'Versicolor', 'Virginica']
def load_data(train_fraction=0.8, seed=0, y_name='Species'):
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=COLUMNS, header=0)
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=COLUMNS, header=0)
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 make_dataset(*inputs):
return tf.data.Dataset.from_tensor_slices(inputs)
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))
def from_dataset(ds):
return lambda: ds.make_one_shot_iterator().get_next()
# 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):
......@@ -58,10 +83,8 @@ def my_model(features, labels, mode, params):
# Create three fully connected layers each layer having a dropout
# probability of 0.1.
net = tf.feature_column.input_layer(features, params['feature_columns'])
for units in params.get('hidden_units', [10, 20, 10]):
for units in params['hidden_units']:
net = tf.layers.dense(net, units=units, activation=tf.nn.relu)
net = tf.layers.dropout(net, rate=0.1,
training=mode == tf.estimator.ModeKeys.TRAIN)
# Compute logits (1 per class).
logits = tf.layers.dense(net, params['n_classes'], activation=None)
......@@ -110,46 +133,51 @@ def main(argv):
train_x = dict(train_x)
test_x = dict(test_x)
# Feature columns describe the input: all columns are numeric.
feature_columns = [tf.feature_column.numeric_column(col_name)
for col_name in COLUMNS[:-1]]
# Feature columns describe how to use the input.
my_feature_columns = []
for key in train_x.keys():
my_feature_columns.append(tf.feature_column.numeric_column(key=key))
# Build 3 layer DNN with 10, 20, 10 units respectively.
# Build 2 hidden layer DNN with 10, 10 units respectively.
classifier = tf.estimator.Estimator(
model_fn=my_model,
params={
'feature_columns': feature_columns,
'hidden_units': [10, 20, 10],
'feature_columns': my_feature_columns,
# Two hidden layers of 10 nodes each.
'hidden_units': [10, 10],
# The model must choose between 3 classes.
'n_classes': 3,
})
# Train the Model.
train = (
make_dataset(train_x, train_y)
.repeat()
.shuffle(1000)
.batch(args.batch_size))
classifier.train(input_fn=from_dataset(train), steps=args.train_steps)
classifier.train(
input_fn=lambda:train_input_fn(train_x, train_y, args.batch_size),
steps=args.train_steps)
# Evaluate the model.
test = make_dataset(test_x, test_y).batch(args.batch_size)
eval_result = classifier.evaluate(input_fn=from_dataset(test))
eval_result = classifier.evaluate(
input_fn=lambda:eval_input_fn(test_x, test_y, args.batch_size))
print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))
# Generate predictions from the model
predict_input = make_dataset({
'SepalLength': [6.4, 5.8],
'SepalWidth': [3.2, 3.1],
'PetalLength': [4.5, 5.0],
'PetalWidth': [1.5, 1.7],
}).batch(args.batch_size)
for p in classifier.predict(input_fn=from_dataset(predict_input)):
template = ('Prediction is "{}" ({:.1f}%)')
class_id = p['class_ids'][0]
probability = p['probabilities'][class_id]
print(template.format(SPECIES[class_id], 100 * probability))
expected = ['Setosa', 'Versicolor', 'Virginica']
predict_x = {
'SepalLength': [5.1, 5.9, 6.9],
'SepalWidth': [3.3, 3.0, 3.1],
'PetalLength': [1.7, 4.2, 5.4],
'PetalWidth': [0.5, 1.5, 2.1],
}
predictions = classifier.predict(
input_fn=lambda:eval_input_fn(predict_x, 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))
if __name__ == '__main__':
......
samples/core/get_started/estimator_test.py
View file @ 11e9c7ad
......@@ -35,7 +35,7 @@ FOUR_LINES = "\n".join([
def four_lines_data():
text = StringIO(FOUR_LINES)
df = pd.read_csv(text, names=premade_estimator.COLUMNS)
df = pd.read_csv(text, names=premade_estimator.CSV_COLUMN_NAMES)
xy = (df, df.pop("Species"))
return xy, xy
......
samples/core/get_started/premade_estimator.py
View file @ 11e9c7ad
......@@ -11,7 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Example of DNNClassifier for Iris plant dataset."""
"""An Example of a DNNClassifier for the Iris dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
......@@ -22,35 +22,59 @@ import tensorflow as tf
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', default=100, type=int, help='batch size')
parser.add_argument('--train_steps', default=200, type=int,
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"
COLUMNS = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Species']
CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth',
'PetalLength', 'PetalWidth', 'Species']
SPECIES = ['Sentosa', 'Versicolor', 'Virginica']
def load_data(train_fraction=0.8, seed=0, y_name='Species'):
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=COLUMNS, header=0)
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=COLUMNS, header=0)
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 make_dataset(*inputs):
return tf.data.Dataset.from_tensor_slices(inputs)
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)
def from_dataset(ds):
return lambda: ds.make_one_shot_iterator().get_next()
# 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):
......@@ -61,43 +85,48 @@ def main(argv):
train_x = dict(train_x)
test_x = dict(test_x)
# Feature columns describe the input: all columns are numeric.
feature_columns = [tf.feature_column.numeric_column(col_name)
for col_name in COLUMNS[:-1]]
# Feature columns describe how to use the input.
my_feature_columns = []
for key in train_x.keys():
my_feature_columns.append(tf.feature_column.numeric_column(key=key))
# Build 3 layer DNN with 10, 20, 10 units respectively.
# Build 2 hidden layer DNN with 10, 10 units respectively.
classifier = tf.estimator.DNNClassifier(
feature_columns=feature_columns,
hidden_units=[10, 20, 10],
feature_columns=my_feature_columns,
# Two hidden layers of 10 nodes each.
hidden_units=[10, 10],
# The model must choose between 3 classes.
n_classes=3)
# Train the Model.
train = (
make_dataset(train_x, train_y)
.repeat()
.shuffle(1000)
.batch(args.batch_size))
classifier.train(input_fn=from_dataset(train), steps=args.train_steps)
classifier.train(
input_fn=lambda:train_input_fn(train_x, train_y, args.batch_size),
steps=args.train_steps)
# Evaluate the model.
test = make_dataset(test_x, test_y).batch(args.batch_size)
eval_result = classifier.evaluate(input_fn=from_dataset(test))
eval_result = classifier.evaluate(
input_fn=lambda:eval_input_fn(test_x, test_y, args.batch_size))
print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))
# Generate predictions from the model
predict_input = make_dataset({
'SepalLength': [6.4, 5.8],
'SepalWidth': [3.2, 3.1],
'PetalLength': [4.5, 5.0],
'PetalWidth': [1.5, 1.7],
}).batch(args.batch_size)
for p in classifier.predict(input_fn=from_dataset(predict_input)):
template = ('Prediction is "{}" ({:.1f}%)')
class_id = p['class_ids'][0]
probability = p['probabilities'][class_id]
print(template.format(SPECIES[class_id], 100 * probability))
expected = ['Setosa', 'Versicolor', 'Virginica']
predict_x = {
'SepalLength': [5.1, 5.9, 6.9],
'SepalWidth': [3.3, 3.0, 3.1],
'PetalLength': [1.7, 4.2, 5.4],
'PetalWidth': [0.5, 1.5, 2.1],
}
predictions = classifier.predict(
input_fn=lambda:eval_input_fn(predict_x, 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))
if __name__ == '__main__':
......
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