diff --git a/tensorflow/contrib/learn/python/learn/__init__.py b/tensorflow/contrib/learn/python/learn/__init__.py
index 8de7797e6b7..1d72243f992 100644
--- a/tensorflow/contrib/learn/python/learn/__init__.py
+++ b/tensorflow/contrib/learn/python/learn/__init__.py
@@ -1,5 +1,4 @@
-"""Main Scikit Flow module."""
-# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -13,6 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
+"""High level API for learning with TensorFlow."""
+
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
diff --git a/tensorflow/contrib/learn/python/learn/datasets/base.py b/tensorflow/contrib/learn/python/learn/datasets/base.py
index 7f78b2dced9..9c29b9eeb11 100644
--- a/tensorflow/contrib/learn/python/learn/datasets/base.py
+++ b/tensorflow/contrib/learn/python/learn/datasets/base.py
@@ -1,5 +1,4 @@
-"""Base utilities for loading datasets."""
-# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -13,6 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
+"""Base utilities for loading datasets."""
+
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
diff --git a/tensorflow/contrib/learn/python/learn/estimators/__init__.py b/tensorflow/contrib/learn/python/learn/estimators/__init__.py
index e714c15f2e0..1b0d0aef6f5 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/__init__.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/__init__.py
@@ -1,5 +1,4 @@
-"""Scikit Flow Estimators."""
-# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -12,12 +11,16 @@
# 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.
+
+"""Estimators."""
+
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.learn.python.learn.estimators.autoencoder import TensorFlowDNNAutoencoder
-from tensorflow.contrib.learn.python.learn.estimators.base import TensorFlowEstimator, TensorFlowBaseTransformer
+from tensorflow.contrib.learn.python.learn.estimators.base import TensorFlowBaseTransformer
+from tensorflow.contrib.learn.python.learn.estimators.base import TensorFlowEstimator
from tensorflow.contrib.learn.python.learn.estimators.dnn import DNNClassifier
from tensorflow.contrib.learn.python.learn.estimators.dnn import DNNRegressor
from tensorflow.contrib.learn.python.learn.estimators.dnn import TensorFlowDNNClassifier
diff --git a/tensorflow/contrib/learn/python/learn/estimators/_sklearn.py b/tensorflow/contrib/learn/python/learn/estimators/_sklearn.py
index dcd1d81056b..5032ea966d4 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/_sklearn.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/_sklearn.py
@@ -1,5 +1,4 @@
-"""sklearn cross-support."""
-# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -12,6 +11,9 @@
# 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.
+
+"""sklearn cross-support."""
+
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
@@ -20,6 +22,8 @@ import collections
import os
import numpy as np
+import six
+
def _pprint(d):
return ', '.join(['%s=%s' % (key, str(value)) for key, value in d.items()])
@@ -102,6 +106,7 @@ class _BaseEstimator(object):
_pprint(self.get_params(deep=False)),)
+# pylint: disable=old-style-class
class _ClassifierMixin():
"""Mixin class for all classifiers."""
pass
@@ -111,8 +116,10 @@ class _RegressorMixin():
"""Mixin class for all regression estimators."""
pass
+
class _TransformerMixin():
- """Mixin class for all transformer estimators."""
+ """Mixin class for all transformer estimators."""
+
class _NotFittedError(ValueError, AttributeError):
"""Exception class to raise if estimator is used before fitting.
@@ -134,6 +141,8 @@ class _NotFittedError(ValueError, AttributeError):
https://github.com/scikit-learn/scikit-learn/master/sklearn/exceptions.py
"""
+# pylint: enable=old-style-class
+
def _accuracy_score(y_true, y_pred):
score = y_true == y_pred
@@ -149,8 +158,7 @@ def _mean_squared_error(y_true, y_pred):
def _train_test_split(*args, **options):
- n_array = len(args)
-
+ # pylint: disable=missing-docstring
test_size = options.pop('test_size', None)
train_size = options.pop('train_size', None)
random_state = options.pop('random_state', None)
@@ -159,7 +167,7 @@ def _train_test_split(*args, **options):
train_size = 0.75
elif train_size is None:
train_size = 1 - test_size
- train_size = train_size * args[0].shape[0]
+ train_size *= args[0].shape[0]
np.random.seed(random_state)
indices = np.random.permutation(args[0].shape[0])
@@ -173,6 +181,7 @@ def _train_test_split(*args, **options):
# If "TENSORFLOW_SKLEARN" flag is defined then try to import from sklearn.
TRY_IMPORT_SKLEARN = os.environ.get('TENSORFLOW_SKLEARN', False)
if TRY_IMPORT_SKLEARN:
+ # pylint: disable=g-import-not-at-top,g-multiple-import,unused-import
from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin, TransformerMixin
from sklearn.metrics import accuracy_score, log_loss, mean_squared_error
from sklearn.cross_validation import train_test_split
diff --git a/tensorflow/contrib/learn/python/learn/estimators/autoencoder.py b/tensorflow/contrib/learn/python/learn/estimators/autoencoder.py
index 690bac8f196..a3f41697680 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/autoencoder.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/autoencoder.py
@@ -1,5 +1,4 @@
-"""Deep Autoencoder estimators."""
-# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -12,105 +11,115 @@
# 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.
+
+"""Deep Autoencoder estimators."""
+
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
-from tensorflow.python.ops import nn
-from tensorflow.contrib.learn.python.learn.estimators.base import TensorFlowBaseTransformer
+import numpy as np
+
from tensorflow.contrib.learn.python.learn import models
+from tensorflow.contrib.learn.python.learn.estimators.base import TensorFlowBaseTransformer
+from tensorflow.python.ops import nn
class TensorFlowDNNAutoencoder(TensorFlowBaseTransformer):
- """TensorFlow Autoencoder Regressor model.
+ """TensorFlow Autoencoder Regressor model.
- Parameters:
- hidden_units: List of hidden units per layer.
- batch_size: Mini batch size.
- activation: activation function used to map inner latent layer onto
- reconstruction layer.
- add_noise: a function that adds noise to tensor_in,
- e.g. def add_noise(x):
- return(x + np.random.normal(0, 0.1, (len(x), len(x[0]))))
- steps: Number of steps to run over data.
- optimizer: Optimizer name (or class), for example "SGD", "Adam",
- "Adagrad".
- learning_rate: If this is constant float value, no decay function is used.
- Instead, a customized decay function can be passed that accepts
- global_step as parameter and returns a Tensor.
- e.g. exponential decay function:
- def exp_decay(global_step):
- return tf.train.exponential_decay(
- learning_rate=0.1, global_step,
- decay_steps=2, decay_rate=0.001)
- continue_training: when continue_training is True, once initialized
- model will be continuely trained on every call of fit.
- config: RunConfig object that controls the configurations of the session,
- e.g. num_cores, gpu_memory_fraction, etc.
- verbose: Controls the verbosity, possible values:
- 0: the algorithm and debug information is muted.
- 1: trainer prints the progress.
- 2: log device placement is printed.
- dropout: When not None, the probability we will drop out a given
- coordinate.
- """
- def __init__(self, hidden_units, n_classes=0, batch_size=32,
- steps=200, optimizer="Adagrad", learning_rate=0.1,
- clip_gradients=5.0, activation=nn.relu, add_noise=None,
- continue_training=False, config=None,
- verbose=1, dropout=None):
- self.hidden_units = hidden_units
- self.dropout = dropout
- self.activation = activation
- self.add_noise = add_noise
- super(TensorFlowDNNAutoencoder, self).__init__(
- model_fn=self._model_fn,
- n_classes=n_classes,
- batch_size=batch_size, steps=steps, optimizer=optimizer,
- learning_rate=learning_rate, clip_gradients=clip_gradients,
- continue_training=continue_training,
- config=config, verbose=verbose)
+ Parameters:
+ hidden_units: List of hidden units per layer.
+ batch_size: Mini batch size.
+ activation: activation function used to map inner latent layer onto
+ reconstruction layer.
+ add_noise: a function that adds noise to tensor_in,
+ e.g. def add_noise(x):
+ return(x + np.random.normal(0, 0.1, (len(x), len(x[0]))))
+ steps: Number of steps to run over data.
+ optimizer: Optimizer name (or class), for example "SGD", "Adam",
+ "Adagrad".
+ learning_rate: If this is constant float value, no decay function is used.
+ Instead, a customized decay function can be passed that accepts
+ global_step as parameter and returns a Tensor.
+ e.g. exponential decay function:
+ def exp_decay(global_step):
+ return tf.train.exponential_decay(
+ learning_rate=0.1, global_step,
+ decay_steps=2, decay_rate=0.001)
+ continue_training: when continue_training is True, once initialized
+ model will be continuely trained on every call of fit.
+ config: RunConfig object that controls the configurations of the session,
+ e.g. num_cores, gpu_memory_fraction, etc.
+ verbose: Controls the verbosity, possible values:
+ 0: the algorithm and debug information is muted.
+ 1: trainer prints the progress.
+ 2: log device placement is printed.
+ dropout: When not None, the probability we will drop out a given
+ coordinate.
+ """
- def _model_fn(self, X, y):
- encoder, decoder, autoencoder_estimator = models.get_autoencoder_model(
- self.hidden_units,
- models.linear_regression,
- activation=self.activation,
- add_noise=self.add_noise,
- dropout=self.dropout)(X)
- self.encoder = encoder
- self.decoder = decoder
- return autoencoder_estimator
+ def __init__(self, hidden_units, n_classes=0, batch_size=32,
+ steps=200, optimizer="Adagrad", learning_rate=0.1,
+ clip_gradients=5.0, activation=nn.relu, add_noise=None,
+ continue_training=False, config=None,
+ verbose=1, dropout=None):
+ self.hidden_units = hidden_units
+ self.dropout = dropout
+ self.activation = activation
+ self.add_noise = add_noise
+ super(TensorFlowDNNAutoencoder, self).__init__(
+ model_fn=self._model_fn,
+ n_classes=n_classes,
+ batch_size=batch_size, steps=steps, optimizer=optimizer,
+ learning_rate=learning_rate, clip_gradients=clip_gradients,
+ continue_training=continue_training,
+ config=config, verbose=verbose)
- def generate(self, hidden=None):
- """Generate new data using trained construction layer"""
- if hidden is None:
- last_layer = len(self.hidden_units) - 1
- bias = self.get_tensor_value('encoder/dnn/layer%d/Linear/Bias:0' % last_layer)
- import numpy as np
- hidden = np.random.normal(size=bias.shape)
- hidden = np.reshape(hidden, (1, len(hidden)))
- return self._session.run(self.decoder, feed_dict={self.encoder: hidden})
+ def _model_fn(self, X, y):
+ encoder, decoder, autoencoder_estimator = models.get_autoencoder_model(
+ self.hidden_units,
+ models.linear_regression,
+ activation=self.activation,
+ add_noise=self.add_noise,
+ dropout=self.dropout)(X)
+ self.encoder = encoder
+ self.decoder = decoder
+ return autoencoder_estimator
- @property
- def weights_(self):
- """Returns weights of the autoencoder's weight layers."""
- weights = []
- for layer in range(len(self.hidden_units)):
- weights.append(self.get_tensor_value('encoder/dnn/layer%d/Linear/Matrix:0' % layer))
- for layer in range(len(self.hidden_units)):
- weights.append(self.get_tensor_value('decoder/dnn/layer%d/Linear/Matrix:0' % layer))
- weights.append(self.get_tensor_value('linear_regression/weights:0'))
- return weights
+ def generate(self, hidden=None):
+ """Generate new data using trained construction layer."""
+ if hidden is None:
+ last_layer = len(self.hidden_units) - 1
+ bias = self.get_tensor_value(
+ "encoder/dnn/layer%d/Linear/Bias:0" % last_layer)
+ hidden = np.random.normal(size=bias.shape)
+ hidden = np.reshape(hidden, (1, len(hidden)))
+ return self._session.run(self.decoder, feed_dict={self.encoder: hidden})
- @property
- def bias_(self):
- """Returns bias of the autoencoder's bias layers."""
- biases = []
- for layer in range(len(self.hidden_units)):
- biases.append(self.get_tensor_value('encoder/dnn/layer%d/Linear/Bias:0' % layer))
- for layer in range(len(self.hidden_units)):
- biases.append(self.get_tensor_value('decoder/dnn/layer%d/Linear/Bias:0' % layer))
- biases.append(self.get_tensor_value('linear_regression/bias:0'))
- return biases
+ @property
+ def weights_(self):
+ """Returns weights of the autoencoder's weight layers."""
+ weights = []
+ for layer in range(len(self.hidden_units)):
+ weights.append(self.get_tensor_value(
+ "encoder/dnn/layer%d/Linear/Matrix:0" % layer))
+ for layer in range(len(self.hidden_units)):
+ weights.append(self.get_tensor_value(
+ "decoder/dnn/layer%d/Linear/Matrix:0" % layer))
+ weights.append(self.get_tensor_value("linear_regression/weights:0"))
+ return weights
+
+ @property
+ def bias_(self):
+ """Returns bias of the autoencoder's bias layers."""
+ biases = []
+ for layer in range(len(self.hidden_units)):
+ biases.append(self.get_tensor_value(
+ "encoder/dnn/layer%d/Linear/Bias:0" % layer))
+ for layer in range(len(self.hidden_units)):
+ biases.append(self.get_tensor_value(
+ "decoder/dnn/layer%d/Linear/Bias:0" % layer))
+ biases.append(self.get_tensor_value("linear_regression/bias:0"))
+ return biases
diff --git a/tensorflow/contrib/learn/python/learn/estimators/base.py b/tensorflow/contrib/learn/python/learn/estimators/base.py
index 39131f059b0..ab00ae76f78 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/base.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/base.py
@@ -1,5 +1,4 @@
-"""Base estimator class."""
-# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -12,18 +11,17 @@
# 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.
+
+"""Base estimator class."""
+
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
-import datetime
import json
import os
-import shutil
from six import string_types
-import numpy as np
-
from google.protobuf import text_format
from tensorflow.python.platform import gfile
diff --git a/tensorflow/contrib/learn/python/learn/estimators/dnn.py b/tensorflow/contrib/learn/python/learn/estimators/dnn.py
index 017667699bc..5447d9ec052 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/dnn.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/dnn.py
@@ -1,5 +1,4 @@
-"""Deep Neural Network estimators."""
-# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -12,6 +11,9 @@
# 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.
+
+"""Deep Neural Network estimators."""
+
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
diff --git a/tensorflow/contrib/learn/python/learn/estimators/linear.py b/tensorflow/contrib/learn/python/learn/estimators/linear.py
index d58ab35f5ee..ef73c44013a 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/linear.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/linear.py
@@ -1,5 +1,4 @@
-"""Linear Estimators."""
-# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -12,6 +11,9 @@
# 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.
+
+"""Linear Estimators."""
+
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
diff --git a/tensorflow/contrib/learn/python/learn/estimators/rnn.py b/tensorflow/contrib/learn/python/learn/estimators/rnn.py
index b703f607657..719a19a5bc8 100644
--- a/tensorflow/contrib/learn/python/learn/estimators/rnn.py
+++ b/tensorflow/contrib/learn/python/learn/estimators/rnn.py
@@ -1,5 +1,4 @@
-"""Recurrent Neural Network estimators."""
-# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -12,6 +11,9 @@
# 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.
+
+"""Recurrent Neural Network estimators."""
+
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
diff --git a/tensorflow/contrib/learn/python/learn/io/data_feeder.py b/tensorflow/contrib/learn/python/learn/io/data_feeder.py
index 04bbd997482..b3ed3bc7d92 100644
--- a/tensorflow/contrib/learn/python/learn/io/data_feeder.py
+++ b/tensorflow/contrib/learn/python/learn/io/data_feeder.py
@@ -1,6 +1,4 @@
-"""Implementations of different data feeders to provide data for TF trainer."""
-
-# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -14,6 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
+"""Implementations of different data feeders to provide data for TF trainer."""
+
# TODO(ipolosukhin): Replace this module with feed-dict queue runners & queues.
from __future__ import absolute_import
diff --git a/tensorflow/contrib/learn/python/learn/models.py b/tensorflow/contrib/learn/python/learn/models.py
index 8cabd390fc7..dddd152f368 100644
--- a/tensorflow/contrib/learn/python/learn/models.py
+++ b/tensorflow/contrib/learn/python/learn/models.py
@@ -1,5 +1,4 @@
-"""Various high level TF models."""
-# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -12,13 +11,16 @@
# 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.
+
+"""Various high level TF models."""
+
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
+from tensorflow.contrib.learn.python.learn.ops import autoencoder_ops
from tensorflow.contrib.learn.python.learn.ops import dnn_ops
from tensorflow.contrib.learn.python.learn.ops import losses_ops
-from tensorflow.contrib.learn.python.learn.ops import autoencoder_ops
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops as array_ops_
@@ -29,8 +31,7 @@ from tensorflow.python.ops import variable_scope as vs
def linear_regression_zero_init(X, y):
- """Creates a linear regression TensorFlow subgraph, in which weights and
- bias terms are initialized to exactly zero.
+ """Linear regression subgraph with zero-value initial weights and bias.
Args:
X: tensor or placeholder for input features.
@@ -43,8 +44,7 @@ def linear_regression_zero_init(X, y):
def logistic_regression_zero_init(X, y):
- """Creates a logistic regression TensorFlow subgraph, in which weights and
- bias terms are initialized to exactly zero.
+ """Logistic regression subgraph with zero-value initial weights and bias.
Args:
X: tensor or placeholder for input features.
@@ -85,7 +85,7 @@ def linear_regression(X, y, init_mean=None, init_stddev=1.0):
else:
output_shape = y_shape[1]
# Set up the requested initialization.
- if (init_mean is None):
+ if init_mean is None:
weights = vs.get_variable('weights', [X.get_shape()[1], output_shape])
bias = vs.get_variable('bias', [output_shape])
else:
@@ -134,7 +134,7 @@ def logistic_regression(X,
logging_ops.histogram_summary('logistic_regression.X', X)
logging_ops.histogram_summary('logistic_regression.y', y)
# Set up the requested initialization.
- if (init_mean is None):
+ if init_mean is None:
weights = vs.get_variable('weights',
[X.get_shape()[1], y.get_shape()[-1]])
bias = vs.get_variable('bias', [y.get_shape()[-1]])
@@ -188,35 +188,37 @@ def get_dnn_model(hidden_units, target_predictor_fn, dropout=None):
return dnn_estimator
+
def get_autoencoder_model(hidden_units, target_predictor_fn,
activation, add_noise=None, dropout=None):
- """Returns a function that creates a Autoencoder TensorFlow subgraph with given
- params.
+ """Returns a function that creates a Autoencoder TensorFlow subgraph.
- Args:
- hidden_units: List of values of hidden units for layers.
- target_predictor_fn: Function that will predict target from input
- features. This can be logistic regression,
- linear regression or any other model,
- that takes X, y and returns predictions and loss tensors.
- activation: activation function used to map inner latent layer onto
- reconstruction layer.
- add_noise: a function that adds noise to tensor_in,
- e.g. def add_noise(x):
- return(x + np.random.normal(0, 0.1, (len(x), len(x[0]))))
- dropout: When not none, causes dropout regularization to be used,
- with the specified probability of removing a given coordinate.
+ Args:
+ hidden_units: List of values of hidden units for layers.
+ target_predictor_fn: Function that will predict target from input
+ features. This can be logistic regression,
+ linear regression or any other model,
+ that takes X, y and returns predictions and loss
+ tensors.
+ activation: activation function used to map inner latent layer onto
+ reconstruction layer.
+ add_noise: a function that adds noise to tensor_in,
+ e.g. def add_noise(x):
+ return(x + np.random.normal(0, 0.1, (len(x), len(x[0]))))
+ dropout: When not none, causes dropout regularization to be used,
+ with the specified probability of removing a given coordinate.
+
+ Returns:
+ A function that creates the subgraph.
+ """
+ def dnn_autoencoder_estimator(X):
+ """Autoencoder estimator with target predictor function on top."""
+ encoder, decoder = autoencoder_ops.dnn_autoencoder(
+ X, hidden_units, activation,
+ add_noise=add_noise, dropout=dropout)
+ return encoder, decoder, target_predictor_fn(X, decoder)
+ return dnn_autoencoder_estimator
- Returns:
- A function that creates the subgraph.
- """
- def dnn_autoencoder_estimator(X):
- """Autoencoder estimator with target predictor function on top."""
- encoder, decoder = autoencoder_ops.dnn_autoencoder(
- X, hidden_units, activation,
- add_noise=add_noise, dropout=dropout)
- return encoder, decoder, target_predictor_fn(X, decoder)
- return dnn_autoencoder_estimator
## This will be in Tensorflow 0.7.
## TODO(ilblackdragon): Clean this up when it's released
diff --git a/tensorflow/contrib/learn/python/learn/monitors.py b/tensorflow/contrib/learn/python/learn/monitors.py
index 861db1758f5..79c629d9491 100644
--- a/tensorflow/contrib/learn/python/learn/monitors.py
+++ b/tensorflow/contrib/learn/python/learn/monitors.py
@@ -1,5 +1,4 @@
-"""Monitors to track model training, report on progress and request early stopping"""
-# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -13,6 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
+"""Monitors to track training, report progress and request early stopping."""
+
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
diff --git a/tensorflow/examples/skflow/boston.py b/tensorflow/examples/skflow/boston.py
index bf2066770c7..9d895bd8e38 100644
--- a/tensorflow/examples/skflow/boston.py
+++ b/tensorflow/examples/skflow/boston.py
@@ -1,4 +1,4 @@
-# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
diff --git a/tensorflow/examples/skflow/iris.py b/tensorflow/examples/skflow/iris.py
index c6c566b10fd..ea44428d541 100644
--- a/tensorflow/examples/skflow/iris.py
+++ b/tensorflow/examples/skflow/iris.py
@@ -1,4 +1,4 @@
-# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
diff --git a/tensorflow/examples/skflow/iris_custom_decay_dnn.py b/tensorflow/examples/skflow/iris_custom_decay_dnn.py
index f9c172725d9..b8b1a1dd140 100644
--- a/tensorflow/examples/skflow/iris_custom_decay_dnn.py
+++ b/tensorflow/examples/skflow/iris_custom_decay_dnn.py
@@ -1,4 +1,4 @@
-# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
diff --git a/tensorflow/examples/skflow/mnist.py b/tensorflow/examples/skflow/mnist.py
index 082ecb2f839..d1288a31e98 100644
--- a/tensorflow/examples/skflow/mnist.py
+++ b/tensorflow/examples/skflow/mnist.py
@@ -1,4 +1,4 @@
-# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
diff --git a/tensorflow/examples/skflow/resnet.py b/tensorflow/examples/skflow/resnet.py
index f1f39568d46..03a5d5e5191 100644
--- a/tensorflow/examples/skflow/resnet.py
+++ b/tensorflow/examples/skflow/resnet.py
@@ -1,4 +1,4 @@
-# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@@ -12,147 +12,155 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-"""
-This example builds deep residual network for mnist data.
+"""This example builds deep residual network for mnist data.
+
Reference Paper: http://arxiv.org/pdf/1512.03385.pdf
Note that this is still a work-in-progress. Feel free to submit a PR
to make this better.
"""
+
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
-import os
from collections import namedtuple
from math import sqrt
+import os
from sklearn import metrics
import tensorflow as tf
-from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.contrib import learn
+from tensorflow.examples.tutorials.mnist import input_data
def res_net(x, y, activation=tf.nn.relu):
- """Builds a residual network. Note that if the input tensor is 2D, it must be
- square in order to be converted to a 4D tensor.
+ """Builds a residual network.
- Borrowed structure from here: https://github.com/pkmital/tensorflow_tutorials/blob/master/10_residual_network.py
+ Note that if the input tensor is 2D, it must be square in order to be
+ converted to a 4D tensor.
- Args:
- x: Input of the network
- y: Output of the network
- activation: Activation function to apply after each convolution
- """
+ Borrowed structure from:
+ github.com/pkmital/tensorflow_tutorials/blob/master/10_residual_network.py
- # Configurations for each bottleneck block
- BottleneckBlock = namedtuple(
- 'BottleneckBlock', ['num_layers', 'num_filters', 'bottleneck_size'])
- blocks = [BottleneckBlock(3, 128, 32),
- BottleneckBlock(3, 256, 64),
- BottleneckBlock(3, 512, 128),
- BottleneckBlock(3, 1024, 256)]
+ Args:
+ x: Input of the network
+ y: Output of the network
+ activation: Activation function to apply after each convolution
- input_shape = x.get_shape().as_list()
+ Returns:
+ Predictions and loss tensors.
+ """
- # Reshape the input into the right shape if it's 2D tensor
- if len(input_shape) == 2:
- ndim = int(sqrt(input_shape[1]))
- x = tf.reshape(x, [-1, ndim, ndim, 1])
+ # Configurations for each bottleneck block.
+ BottleneckBlock = namedtuple(
+ 'BottleneckBlock', ['num_layers', 'num_filters', 'bottleneck_size'])
+ blocks = [BottleneckBlock(3, 128, 32),
+ BottleneckBlock(3, 256, 64),
+ BottleneckBlock(3, 512, 128),
+ BottleneckBlock(3, 1024, 256)]
- # First convolution expands to 64 channels
- with tf.variable_scope('conv_layer1'):
- net = learn.ops.conv2d(x, 64, [7, 7], batch_norm=True,
- activation=activation, bias=False)
+ input_shape = x.get_shape().as_list()
- # Max pool
- net = tf.nn.max_pool(
- net, [1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')
+ # Reshape the input into the right shape if it's 2D tensor
+ if len(input_shape) == 2:
+ ndim = int(sqrt(input_shape[1]))
+ x = tf.reshape(x, [-1, ndim, ndim, 1])
- # First chain of resnets
- with tf.variable_scope('conv_layer2'):
- net = learn.ops.conv2d(net, blocks[0].num_filters,
- [1, 1], [1, 1, 1, 1],
- padding='VALID', bias=True)
+ # First convolution expands to 64 channels
+ with tf.variable_scope('conv_layer1'):
+ net = learn.ops.conv2d(x, 64, [7, 7], batch_norm=True,
+ activation=activation, bias=False)
- # Create each bottleneck building block for each layer
- for block_i, block in enumerate(blocks):
- for layer_i in range(block.num_layers):
+ # Max pool
+ net = tf.nn.max_pool(
+ net, [1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')
- name = 'block_%d/layer_%d' % (block_i, layer_i)
+ # First chain of resnets
+ with tf.variable_scope('conv_layer2'):
+ net = learn.ops.conv2d(net, blocks[0].num_filters,
+ [1, 1], [1, 1, 1, 1],
+ padding='VALID', bias=True)
- # 1x1 convolution responsible for reducing dimension
- with tf.variable_scope(name + '/conv_in'):
- conv = learn.ops.conv2d(net, block.bottleneck_size,
- [1, 1], [1, 1, 1, 1],
- padding='VALID',
- activation=activation,
- batch_norm=True,
- bias=False)
+ # Create each bottleneck building block for each layer
+ for block_i, block in enumerate(blocks):
+ for layer_i in range(block.num_layers):
- with tf.variable_scope(name + '/conv_bottleneck'):
- conv = learn.ops.conv2d(conv, block.bottleneck_size,
- [3, 3], [1, 1, 1, 1],
- padding='SAME',
- activation=activation,
- batch_norm=True,
- bias=False)
+ name = 'block_%d/layer_%d' % (block_i, layer_i)
- # 1x1 convolution responsible for restoring dimension
- with tf.variable_scope(name + '/conv_out'):
- conv = learn.ops.conv2d(conv, block.num_filters,
- [1, 1], [1, 1, 1, 1],
- padding='VALID',
- activation=activation,
- batch_norm=True,
- bias=False)
+ # 1x1 convolution responsible for reducing dimension
+ with tf.variable_scope(name + '/conv_in'):
+ conv = learn.ops.conv2d(net, block.bottleneck_size,
+ [1, 1], [1, 1, 1, 1],
+ padding='VALID',
+ activation=activation,
+ batch_norm=True,
+ bias=False)
- # shortcut connections that turn the network into its counterpart
- # residual function (identity shortcut)
- net = conv + net
+ with tf.variable_scope(name + '/conv_bottleneck'):
+ conv = learn.ops.conv2d(conv, block.bottleneck_size,
+ [3, 3], [1, 1, 1, 1],
+ padding='SAME',
+ activation=activation,
+ batch_norm=True,
+ bias=False)
- try:
- # upscale to the next block size
- next_block = blocks[block_i + 1]
- with tf.variable_scope('block_%d/conv_upscale' % block_i):
- net = learn.ops.conv2d(net, next_block.num_filters,
- [1, 1], [1, 1, 1, 1],
- bias=False,
- padding='SAME')
- except IndexError:
- pass
+ # 1x1 convolution responsible for restoring dimension
+ with tf.variable_scope(name + '/conv_out'):
+ conv = learn.ops.conv2d(conv, block.num_filters,
+ [1, 1], [1, 1, 1, 1],
+ padding='VALID',
+ activation=activation,
+ batch_norm=True,
+ bias=False)
- net_shape = net.get_shape().as_list()
- net = tf.nn.avg_pool(net,
- ksize=[1, net_shape[1], net_shape[2], 1],
- strides=[1, 1, 1, 1], padding='VALID')
+ # shortcut connections that turn the network into its counterpart
+ # residual function (identity shortcut)
+ net = conv + net
- net_shape = net.get_shape().as_list()
- net = tf.reshape(net, [-1, net_shape[1] * net_shape[2] * net_shape[3]])
+ try:
+ # upscale to the next block size
+ next_block = blocks[block_i + 1]
+ with tf.variable_scope('block_%d/conv_upscale' % block_i):
+ net = learn.ops.conv2d(net, next_block.num_filters,
+ [1, 1], [1, 1, 1, 1],
+ bias=False,
+ padding='SAME')
+ except IndexError:
+ pass
- return learn.models.logistic_regression(net, y)
+ net_shape = net.get_shape().as_list()
+ net = tf.nn.avg_pool(net,
+ ksize=[1, net_shape[1], net_shape[2], 1],
+ strides=[1, 1, 1, 1], padding='VALID')
+
+ net_shape = net.get_shape().as_list()
+ net = tf.reshape(net, [-1, net_shape[1] * net_shape[2] * net_shape[3]])
+
+ return learn.models.logistic_regression(net, y)
# Download and load MNIST data.
mnist = input_data.read_data_sets('MNIST_data')
# Restore model if graph is saved into a folder.
-if os.path.exists("models/resnet/graph.pbtxt"):
- classifier = learn.TensorFlowEstimator.restore("models/resnet/")
+if os.path.exists('models/resnet/graph.pbtxt'):
+ classifier = learn.TensorFlowEstimator.restore('models/resnet/')
else:
- # Create a new resnet classifier.
- classifier = learn.TensorFlowEstimator(
- model_fn=res_net, n_classes=10, batch_size=100, steps=100,
- learning_rate=0.001, continue_training=True)
+ # Create a new resnet classifier.
+ classifier = learn.TensorFlowEstimator(
+ model_fn=res_net, n_classes=10, batch_size=100, steps=100,
+ learning_rate=0.001, continue_training=True)
while True:
- # Train model and save summaries into logdir.
- classifier.fit(mnist.train.images, mnist.train.labels, logdir="models/resnet/")
+ # Train model and save summaries into logdir.
+ classifier.fit(
+ mnist.train.images, mnist.train.labels, logdir='models/resnet/')
- # Calculate accuracy.
- score = metrics.accuracy_score(
- mnist.test.labels, classifier.predict(mnist.test.images, batch_size=64))
- print('Accuracy: {0:f}'.format(score))
+ # Calculate accuracy.
+ score = metrics.accuracy_score(
+ mnist.test.labels, classifier.predict(mnist.test.images, batch_size=64))
+ print('Accuracy: {0:f}'.format(score))
- # Save model graph and checkpoints.
- classifier.save("models/resnet/")
+ # Save model graph and checkpoints.
+ classifier.save('models/resnet/')