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# Copyright 2018 Google, Inc. 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.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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.
# ==============================================================================
"""
Can NOT be differentiated through.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import numpy as np
import sonnet as snt
import tensorflow as tf
from tensorflow.python.framework import function
from learning_unsupervised_learning import utils
from learning_unsupervised_learning.meta_objective import utils as meta_obj_utils
from sklearn import svm
from sklearn import linear_model
def build_fit(device, model_fn, num_classes, probs=True):
def _py_fit_predict(trX, trY, teX):
assert len(np.unique(trY)) == num_classes
model = model_fn()
model.fit(trX, trY)
trP = model.predict(trX)
teP = model.predict(teX)
if probs:
teP_probs = model.predict_log_proba(teX)
return trP.astype(np.int64), teP.astype(np.int64), teP_probs.astype(
np.float32)
else:
teP = model.predict(teX)
return trP.astype(np.int64), teP.astype(np.int64)
def return_fn(trX, trY, teX):
with tf.device(device):
with tf.device("/cpu:0"):
if probs:
return tf.py_func(
_py_fit_predict,
[tf.identity(trX),
tf.identity(trY),
tf.identity(teX)], [tf.int64, tf.int64, tf.float32])
else:
return tf.py_func(
_py_fit_predict,
[tf.identity(trX),
tf.identity(trY),
tf.identity(teX)], [tf.int64, tf.int64])
return return_fn
class SKLearn(meta_obj_utils.MultiTrialMetaObjective):
def __init__(
self,
local_device=None,
remote_device=None,
averages=1,
samples_per_class=10,
probs=False,
stddev=0.01,
n_samples=10,
name="SKLearn",
):
self._local_device = local_device
self._remote_device = remote_device
self.name = name
self.probs = probs
self.n_samples = n_samples
self.stddev = stddev
super(SKLearn, self).__init__(
name=name, samples_per_class=samples_per_class, averages=averages)
def _get_model(self):
raise NotImplemented()
def _build_once(self, dataset, feature_transformer):
with tf.device(self._local_device):
tr_batch = dataset()
te_batch = dataset()
num_classes = tr_batch.label_onehot.shape.as_list()[1]
all_batch = utils.structure_map_multi(lambda x: tf.concat(x, 0),
[tr_batch, te_batch])
features = feature_transformer(all_batch)
trX, teX = utils.structure_map_split(lambda x: tf.split(x, 2, axis=0),
features)
trY = tf.to_int64(tr_batch.label)
trY_onehot = tf.to_int32(tr_batch.label_onehot)
teY = tf.to_int64(te_batch.label)
teY_shape = teY.shape.as_list()
def blackbox((trX, trY, teX, teY)):
trY = tf.to_int32(tf.rint(trY))
teY = tf.to_int32(tf.rint(teY))
tf_fn = build_fit(
self._local_device,
self._get_model,
num_classes=num_classes,
probs=self.probs)
if self.probs:
trP, teP, teP_probs = tf_fn(trX, trY, teX)
else:
trP, teP = tf_fn(trX, trY, teX)
teY.set_shape(teY_shape)
if self.probs:
onehot = tf.one_hot(teY, num_classes)
crossent = -tf.reduce_sum(onehot * teP_probs, [1])
return tf.reduce_mean(crossent)
else:
# use error rate as the loss if no surrogate is avalible.
return 1 - tf.reduce_mean(
tf.to_float(tf.equal(teY, tf.to_int32(teP))))
test_loss = blackbox((trX, tf.to_float(trY), teX, tf.to_float(teY)))
stats = {}
tf_fn = build_fit(
self._local_device,
self._get_model,
num_classes=num_classes,
probs=self.probs)
if self.probs:
trP, teP, teP_probs = tf_fn(trX, trY, teX)
else:
trP, teP = tf_fn(trX, trY, teX)
stats["%s/accuracy_train" % self.name] = tf.reduce_mean(
tf.to_float(tf.equal(tf.to_int32(trY), tf.to_int32(trP))))
stats["%s/accuracy_test" % self.name] = tf.reduce_mean(
tf.to_float(tf.equal(tf.to_int32(teY), tf.to_int32(teP))))
stats["%s/test_loss" % self.name] = test_loss
return test_loss, stats
class LogisticRegression(SKLearn):
def __init__(self, C=1.0, name="LogisticRegression", probs=True, **kwargs):
self.C = C
super(LogisticRegression, self).__init__(name=name, probs=probs, **kwargs)
def _get_model(self):
return linear_model.LogisticRegression(C=self.C)
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