Fix copyrights and a few other lint errors.
Change: 123250570
This commit is contained in:
A. Unique TensorFlower
TensorFlower Gardener
parent
fd5ebfa768
commit
5c145f0e3c
@ -1,5 +1,4 @@
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"""Main Scikit Flow module."""
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# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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@ -13,6 +12,8 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""High level API for learning with TensorFlow."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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@ -1,5 +1,4 @@
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"""Base utilities for loading datasets."""
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# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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@ -13,6 +12,8 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Base utilities for loading datasets."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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@ -1,5 +1,4 @@
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"""Scikit Flow Estimators."""
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# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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@ -12,12 +11,16 @@
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Estimators."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.contrib.learn.python.learn.estimators.autoencoder import TensorFlowDNNAutoencoder
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from tensorflow.contrib.learn.python.learn.estimators.base import TensorFlowEstimator, TensorFlowBaseTransformer
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from tensorflow.contrib.learn.python.learn.estimators.base import TensorFlowBaseTransformer
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from tensorflow.contrib.learn.python.learn.estimators.base import TensorFlowEstimator
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from tensorflow.contrib.learn.python.learn.estimators.dnn import DNNClassifier
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from tensorflow.contrib.learn.python.learn.estimators.dnn import DNNRegressor
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from tensorflow.contrib.learn.python.learn.estimators.dnn import TensorFlowDNNClassifier
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@ -1,5 +1,4 @@
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"""sklearn cross-support."""
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# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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@ -12,6 +11,9 @@
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""sklearn cross-support."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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@ -20,6 +22,8 @@ import collections
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import os
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import numpy as np
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import six
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def _pprint(d):
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return ', '.join(['%s=%s' % (key, str(value)) for key, value in d.items()])
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@ -102,6 +106,7 @@ class _BaseEstimator(object):
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_pprint(self.get_params(deep=False)),)
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# pylint: disable=old-style-class
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class _ClassifierMixin():
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"""Mixin class for all classifiers."""
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pass
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@ -111,8 +116,10 @@ class _RegressorMixin():
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"""Mixin class for all regression estimators."""
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pass
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class _TransformerMixin():
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"""Mixin class for all transformer estimators."""
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"""Mixin class for all transformer estimators."""
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class _NotFittedError(ValueError, AttributeError):
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"""Exception class to raise if estimator is used before fitting.
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@ -134,6 +141,8 @@ class _NotFittedError(ValueError, AttributeError):
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https://github.com/scikit-learn/scikit-learn/master/sklearn/exceptions.py
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"""
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# pylint: enable=old-style-class
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def _accuracy_score(y_true, y_pred):
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score = y_true == y_pred
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@ -149,8 +158,7 @@ def _mean_squared_error(y_true, y_pred):
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def _train_test_split(*args, **options):
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n_array = len(args)
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# pylint: disable=missing-docstring
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test_size = options.pop('test_size', None)
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train_size = options.pop('train_size', None)
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random_state = options.pop('random_state', None)
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@ -159,7 +167,7 @@ def _train_test_split(*args, **options):
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train_size = 0.75
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elif train_size is None:
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train_size = 1 - test_size
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train_size = train_size * args[0].shape[0]
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train_size *= args[0].shape[0]
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np.random.seed(random_state)
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indices = np.random.permutation(args[0].shape[0])
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@ -173,6 +181,7 @@ def _train_test_split(*args, **options):
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# If "TENSORFLOW_SKLEARN" flag is defined then try to import from sklearn.
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TRY_IMPORT_SKLEARN = os.environ.get('TENSORFLOW_SKLEARN', False)
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if TRY_IMPORT_SKLEARN:
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# pylint: disable=g-import-not-at-top,g-multiple-import,unused-import
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from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin, TransformerMixin
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from sklearn.metrics import accuracy_score, log_loss, mean_squared_error
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from sklearn.cross_validation import train_test_split
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"""Deep Autoencoder estimators."""
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# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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@ -12,105 +11,115 @@
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Deep Autoencoder estimators."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.ops import nn
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from tensorflow.contrib.learn.python.learn.estimators.base import TensorFlowBaseTransformer
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import numpy as np
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from tensorflow.contrib.learn.python.learn import models
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from tensorflow.contrib.learn.python.learn.estimators.base import TensorFlowBaseTransformer
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from tensorflow.python.ops import nn
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class TensorFlowDNNAutoencoder(TensorFlowBaseTransformer):
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"""TensorFlow Autoencoder Regressor model.
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"""TensorFlow Autoencoder Regressor model.
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Parameters:
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hidden_units: List of hidden units per layer.
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batch_size: Mini batch size.
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activation: activation function used to map inner latent layer onto
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reconstruction layer.
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add_noise: a function that adds noise to tensor_in,
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e.g. def add_noise(x):
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return(x + np.random.normal(0, 0.1, (len(x), len(x[0]))))
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steps: Number of steps to run over data.
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optimizer: Optimizer name (or class), for example "SGD", "Adam",
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"Adagrad".
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learning_rate: If this is constant float value, no decay function is used.
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Instead, a customized decay function can be passed that accepts
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global_step as parameter and returns a Tensor.
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e.g. exponential decay function:
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def exp_decay(global_step):
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return tf.train.exponential_decay(
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learning_rate=0.1, global_step,
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decay_steps=2, decay_rate=0.001)
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continue_training: when continue_training is True, once initialized
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model will be continuely trained on every call of fit.
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config: RunConfig object that controls the configurations of the session,
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e.g. num_cores, gpu_memory_fraction, etc.
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verbose: Controls the verbosity, possible values:
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0: the algorithm and debug information is muted.
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1: trainer prints the progress.
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2: log device placement is printed.
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dropout: When not None, the probability we will drop out a given
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coordinate.
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"""
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def __init__(self, hidden_units, n_classes=0, batch_size=32,
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steps=200, optimizer="Adagrad", learning_rate=0.1,
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clip_gradients=5.0, activation=nn.relu, add_noise=None,
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continue_training=False, config=None,
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verbose=1, dropout=None):
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self.hidden_units = hidden_units
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self.dropout = dropout
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self.activation = activation
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self.add_noise = add_noise
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super(TensorFlowDNNAutoencoder, self).__init__(
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model_fn=self._model_fn,
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n_classes=n_classes,
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batch_size=batch_size, steps=steps, optimizer=optimizer,
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learning_rate=learning_rate, clip_gradients=clip_gradients,
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continue_training=continue_training,
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config=config, verbose=verbose)
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Parameters:
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hidden_units: List of hidden units per layer.
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batch_size: Mini batch size.
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activation: activation function used to map inner latent layer onto
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reconstruction layer.
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add_noise: a function that adds noise to tensor_in,
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e.g. def add_noise(x):
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return(x + np.random.normal(0, 0.1, (len(x), len(x[0]))))
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steps: Number of steps to run over data.
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optimizer: Optimizer name (or class), for example "SGD", "Adam",
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"Adagrad".
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learning_rate: If this is constant float value, no decay function is used.
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Instead, a customized decay function can be passed that accepts
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global_step as parameter and returns a Tensor.
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e.g. exponential decay function:
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def exp_decay(global_step):
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return tf.train.exponential_decay(
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learning_rate=0.1, global_step,
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decay_steps=2, decay_rate=0.001)
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continue_training: when continue_training is True, once initialized
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model will be continuely trained on every call of fit.
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config: RunConfig object that controls the configurations of the session,
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e.g. num_cores, gpu_memory_fraction, etc.
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verbose: Controls the verbosity, possible values:
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0: the algorithm and debug information is muted.
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1: trainer prints the progress.
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2: log device placement is printed.
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dropout: When not None, the probability we will drop out a given
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coordinate.
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"""
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def _model_fn(self, X, y):
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encoder, decoder, autoencoder_estimator = models.get_autoencoder_model(
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self.hidden_units,
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models.linear_regression,
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activation=self.activation,
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add_noise=self.add_noise,
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dropout=self.dropout)(X)
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self.encoder = encoder
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self.decoder = decoder
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return autoencoder_estimator
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def __init__(self, hidden_units, n_classes=0, batch_size=32,
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steps=200, optimizer="Adagrad", learning_rate=0.1,
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clip_gradients=5.0, activation=nn.relu, add_noise=None,
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continue_training=False, config=None,
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verbose=1, dropout=None):
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self.hidden_units = hidden_units
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self.dropout = dropout
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self.activation = activation
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self.add_noise = add_noise
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super(TensorFlowDNNAutoencoder, self).__init__(
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model_fn=self._model_fn,
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n_classes=n_classes,
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batch_size=batch_size, steps=steps, optimizer=optimizer,
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learning_rate=learning_rate, clip_gradients=clip_gradients,
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continue_training=continue_training,
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config=config, verbose=verbose)
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def generate(self, hidden=None):
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"""Generate new data using trained construction layer"""
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if hidden is None:
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last_layer = len(self.hidden_units) - 1
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bias = self.get_tensor_value('encoder/dnn/layer%d/Linear/Bias:0' % last_layer)
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import numpy as np
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hidden = np.random.normal(size=bias.shape)
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hidden = np.reshape(hidden, (1, len(hidden)))
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return self._session.run(self.decoder, feed_dict={self.encoder: hidden})
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def _model_fn(self, X, y):
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encoder, decoder, autoencoder_estimator = models.get_autoencoder_model(
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self.hidden_units,
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models.linear_regression,
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activation=self.activation,
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add_noise=self.add_noise,
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dropout=self.dropout)(X)
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self.encoder = encoder
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self.decoder = decoder
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return autoencoder_estimator
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@property
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def weights_(self):
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"""Returns weights of the autoencoder's weight layers."""
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weights = []
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for layer in range(len(self.hidden_units)):
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weights.append(self.get_tensor_value('encoder/dnn/layer%d/Linear/Matrix:0' % layer))
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for layer in range(len(self.hidden_units)):
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weights.append(self.get_tensor_value('decoder/dnn/layer%d/Linear/Matrix:0' % layer))
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weights.append(self.get_tensor_value('linear_regression/weights:0'))
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return weights
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def generate(self, hidden=None):
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"""Generate new data using trained construction layer."""
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if hidden is None:
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last_layer = len(self.hidden_units) - 1
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bias = self.get_tensor_value(
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"encoder/dnn/layer%d/Linear/Bias:0" % last_layer)
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hidden = np.random.normal(size=bias.shape)
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hidden = np.reshape(hidden, (1, len(hidden)))
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return self._session.run(self.decoder, feed_dict={self.encoder: hidden})
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@property
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def bias_(self):
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"""Returns bias of the autoencoder's bias layers."""
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biases = []
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for layer in range(len(self.hidden_units)):
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biases.append(self.get_tensor_value('encoder/dnn/layer%d/Linear/Bias:0' % layer))
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for layer in range(len(self.hidden_units)):
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biases.append(self.get_tensor_value('decoder/dnn/layer%d/Linear/Bias:0' % layer))
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biases.append(self.get_tensor_value('linear_regression/bias:0'))
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return biases
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@property
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def weights_(self):
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"""Returns weights of the autoencoder's weight layers."""
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weights = []
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for layer in range(len(self.hidden_units)):
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weights.append(self.get_tensor_value(
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"encoder/dnn/layer%d/Linear/Matrix:0" % layer))
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for layer in range(len(self.hidden_units)):
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weights.append(self.get_tensor_value(
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"decoder/dnn/layer%d/Linear/Matrix:0" % layer))
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weights.append(self.get_tensor_value("linear_regression/weights:0"))
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return weights
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@property
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def bias_(self):
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"""Returns bias of the autoencoder's bias layers."""
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biases = []
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for layer in range(len(self.hidden_units)):
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biases.append(self.get_tensor_value(
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"encoder/dnn/layer%d/Linear/Bias:0" % layer))
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for layer in range(len(self.hidden_units)):
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biases.append(self.get_tensor_value(
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"decoder/dnn/layer%d/Linear/Bias:0" % layer))
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biases.append(self.get_tensor_value("linear_regression/bias:0"))
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return biases
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@ -1,5 +1,4 @@
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"""Base estimator class."""
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# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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@ -12,18 +11,17 @@
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Base estimator class."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import datetime
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import json
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import os
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import shutil
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from six import string_types
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import numpy as np
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from google.protobuf import text_format
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from tensorflow.python.platform import gfile
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@ -1,5 +1,4 @@
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"""Deep Neural Network estimators."""
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# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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@ -12,6 +11,9 @@
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Deep Neural Network estimators."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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@ -1,5 +1,4 @@
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"""Linear Estimators."""
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# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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@ -12,6 +11,9 @@
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
|
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# limitations under the License.
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"""Linear Estimators."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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@ -1,5 +1,4 @@
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"""Recurrent Neural Network estimators."""
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# Copyright 2015-present The Scikit Flow Authors. All Rights Reserved.
|
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
|
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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@ -12,6 +11,9 @@
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
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"""Recurrent Neural Network estimators."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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@ -1,6 +1,4 @@
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"""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
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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.
|
||||
|
||||
@ -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.
|
||||
|
||||
@ -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.
|
||||
|
||||
@ -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.
|
||||
|
||||
@ -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/')
|
||||
|
||||
Loading…
Reference in New Issue
Block a user