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# Copyright 2018 The TensorFlow Global Objectives 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.
# 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.
# ==============================================================================
"""Example for using global objectives.
Illustrate, using synthetic data, how using the precision_at_recall loss
significanly improves the performace of a linear classifier.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# Dependency imports
import numpy as np
from sklearn.metrics import precision_score
import tensorflow as tf
from global_objectives import loss_layers
# When optimizing using global_objectives, if set to True then the saddle point
# optimization steps are performed internally by the Tensorflow optimizer,
# otherwise by dedicated saddle-point steps as part of the optimization loop.
USE_GO_SADDLE_POINT_OPT = False
TARGET_RECALL = 0.98
TRAIN_ITERATIONS = 150
LEARNING_RATE = 1.0
GO_DUAL_RATE_FACTOR = 15.0
NUM_CHECKPOINTS = 6
EXPERIMENT_DATA_CONFIG = {
'positives_centers': [[0, 1.0], [1, -0.5]],
'negatives_centers': [[0, -0.5], [1, 1.0]],
'positives_variances': [0.15, 0.1],
'negatives_variances': [0.15, 0.1],
'positives_counts': [500, 50],
'negatives_counts': [3000, 100]
}
def create_training_and_eval_data_for_experiment(**data_config):
"""Creates train and eval data sets.
Note: The synthesized binary-labeled data is a mixture of four Gaussians - two
positives and two negatives. The centers, variances, and sizes for each of
the two positives and negatives mixtures are passed in the respective keys
of data_config:
Args:
**data_config: Dictionary with Array entries as follows:
positives_centers - float [2,2] two centers of positives data sets.
negatives_centers - float [2,2] two centers of negatives data sets.
positives_variances - float [2] Variances for the positives sets.
negatives_variances - float [2] Variances for the negatives sets.
positives_counts - int [2] Counts for each of the two positives sets.
negatives_counts - int [2] Counts for each of the two negatives sets.
Returns:
A dictionary with two shuffled data sets created - one for training and one
for eval. The dictionary keys are 'train_data', 'train_labels', 'eval_data',
and 'eval_labels'. The data points are two-dimentional floats, and the
labels are in {0,1}.
"""
def data_points(is_positives, index):
variance = data_config['positives_variances'
if is_positives else 'negatives_variances'][index]
center = data_config['positives_centers'
if is_positives else 'negatives_centers'][index]
count = data_config['positives_counts'
if is_positives else 'negatives_counts'][index]
return variance*np.random.randn(count, 2) + np.array([center])
def create_data():
return np.concatenate([data_points(False, 0),
data_points(True, 0),
data_points(True, 1),
data_points(False, 1)], axis=0)
def create_labels():
"""Creates an array of 0.0 or 1.0 labels for the data_config batches."""
return np.array([0.0]*data_config['negatives_counts'][0] +
[1.0]*data_config['positives_counts'][0] +
[1.0]*data_config['positives_counts'][1] +
[0.0]*data_config['negatives_counts'][1])
permutation = np.random.permutation(
sum(data_config['positives_counts'] + data_config['negatives_counts']))
train_data = create_data()[permutation, :]
eval_data = create_data()[permutation, :]
train_labels = create_labels()[permutation]
eval_labels = create_labels()[permutation]
return {
'train_data': train_data,
'train_labels': train_labels,
'eval_data': eval_data,
'eval_labels': eval_labels
}
def train_model(data, use_global_objectives):
"""Trains a linear model for maximal accuracy or precision at given recall."""
def precision_at_recall(scores, labels, target_recall):
"""Computes precision - at target recall - over data."""
positive_scores = scores[labels == 1.0]
threshold = np.percentile(positive_scores, 100 - target_recall*100)
predicted = scores >= threshold
return precision_score(labels, predicted)
w = tf.Variable(tf.constant([-1.0, -1.0], shape=[2, 1]), trainable=True,
name='weights', dtype=tf.float32)
b = tf.Variable(tf.zeros([1]), trainable=True, name='biases',
dtype=tf.float32)
logits = tf.matmul(tf.cast(data['train_data'], tf.float32), w) + b
labels = tf.constant(
data['train_labels'],
shape=[len(data['train_labels']), 1],
dtype=tf.float32)
if use_global_objectives:
loss, other_outputs = loss_layers.precision_at_recall_loss(
labels, logits,
TARGET_RECALL,
dual_rate_factor=GO_DUAL_RATE_FACTOR)
loss = tf.reduce_mean(loss)
else:
loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits))
global_step = tf.Variable(0, trainable=False)
learning_rate = tf.train.polynomial_decay(
LEARNING_RATE,
global_step,
TRAIN_ITERATIONS, (LEARNING_RATE / TRAIN_ITERATIONS),
power=1.0,
cycle=False,
name='learning_rate')
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
if (not use_global_objectives) or USE_GO_SADDLE_POINT_OPT:
training_op = optimizer.minimize(loss, global_step=global_step)
else:
lambdas = other_outputs['lambdas']
primal_update_op = optimizer.minimize(loss, var_list=[w, b])
dual_update_op = optimizer.minimize(
loss, global_step=global_step, var_list=[lambdas])
# Training loop:
with tf.Session() as sess:
checkpoint_step = TRAIN_ITERATIONS // NUM_CHECKPOINTS
sess.run(tf.global_variables_initializer())
step = sess.run(global_step)
while step <= TRAIN_ITERATIONS:
if (not use_global_objectives) or USE_GO_SADDLE_POINT_OPT:
_, step, loss_value, w_value, b_value = sess.run(
[training_op, global_step, loss, w, b])
else:
_, w_value, b_value = sess.run([primal_update_op, w, b])
_, loss_value, step = sess.run([dual_update_op, loss, global_step])
if use_global_objectives:
go_outputs = sess.run(other_outputs.values())
if step % checkpoint_step == 0:
precision = precision_at_recall(
np.dot(data['train_data'], w_value) + b_value,
data['train_labels'], TARGET_RECALL)
tf.logging.info('Loss = %f Precision = %f', loss_value, precision)
if use_global_objectives:
for i, output_name in enumerate(other_outputs.keys()):
tf.logging.info('\t%s = %f', output_name, go_outputs[i])
w_value, b_value = sess.run([w, b])
return precision_at_recall(np.dot(data['eval_data'], w_value) + b_value,
data['eval_labels'],
TARGET_RECALL)
def main(unused_argv):
del unused_argv
experiment_data = create_training_and_eval_data_for_experiment(
**EXPERIMENT_DATA_CONFIG)
global_objectives_loss_precision = train_model(experiment_data, True)
tf.logging.info('global_objectives precision at requested recall is %f',
global_objectives_loss_precision)
cross_entropy_loss_precision = train_model(experiment_data, False)
tf.logging.info('cross_entropy precision at requested recall is %f',
cross_entropy_loss_precision)
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run()
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