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# Copyright 2015 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.
# ==============================================================================
"""Tests for tensorflow.ops.histogram_ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
class HistogramFixedWidthTest(tf.test.TestCase):
def setUp(self):
self.rng = np.random.RandomState(0)
def test_empty_input_gives_all_zero_counts(self):
# Bins will be:
# (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
value_range = [0.0, 5.0]
values = []
expected_bin_counts = [0, 0, 0, 0, 0]
with self.test_session():
hist = tf.histogram_fixed_width(values, value_range, nbins=5)
# Hist should start "fresh" with every eval.
self.assertAllClose(expected_bin_counts, hist.eval())
self.assertAllClose(expected_bin_counts, hist.eval())
def test_one_update_on_constant_input(self):
# Bins will be:
# (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
value_range = [0.0, 5.0]
values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
expected_bin_counts = [2, 1, 1, 0, 2]
with self.test_session():
hist = tf.histogram_fixed_width(values, value_range, nbins=5)
# Hist should start "fresh" with every eval.
self.assertAllClose(expected_bin_counts, hist.eval())
self.assertAllClose(expected_bin_counts, hist.eval())
def test_one_update_on_constant_2d_input(self):
# Bins will be:
# (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
value_range = [0.0, 5.0]
values = [[-1.0, 0.0, 1.5], [2.0, 5.0, 15]]
expected_bin_counts = [2, 1, 1, 0, 2]
with self.test_session():
hist = tf.histogram_fixed_width(values, value_range, nbins=5)
# Hist should start "fresh" with every eval.
self.assertAllClose(expected_bin_counts, hist.eval())
self.assertAllClose(expected_bin_counts, hist.eval())
def test_two_updates_on_constant_input(self):
# Bins will be:
# (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
value_range = [0.0, 5.0]
values_1 = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
values_2 = [1.5, 4.5, 4.5, 4.5, 0.0, 0.0]
expected_bin_counts_1 = [2, 1, 1, 0, 2]
expected_bin_counts_2 = [2, 1, 0, 0, 3]
with self.test_session():
values = tf.placeholder(tf.float32, shape=[6])
hist = tf.histogram_fixed_width(values, value_range, nbins=5)
# The values in hist should depend on the current feed and nothing else.
self.assertAllClose(expected_bin_counts_1,
hist.eval(feed_dict={values: values_1}))
self.assertAllClose(expected_bin_counts_2,
hist.eval(feed_dict={values: values_2}))
self.assertAllClose(expected_bin_counts_1,
hist.eval(feed_dict={values: values_1}))
self.assertAllClose(expected_bin_counts_1,
hist.eval(feed_dict={values: values_1}))
def test_two_updates_on_scalar_input(self):
# Bins will be:
# (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
value_range = [0.0, 5.0]
values_1 = 1.5
values_2 = 2.5
expected_bin_counts_1 = [0, 1, 0, 0, 0]
expected_bin_counts_2 = [0, 0, 1, 0, 0]
with self.test_session():
values = tf.placeholder(tf.float32, shape=[])
hist = tf.histogram_fixed_width(values, value_range, nbins=5)
# The values in hist should depend on the current feed and nothing else.
self.assertAllClose(expected_bin_counts_2,
hist.eval(feed_dict={values: values_2}))
self.assertAllClose(expected_bin_counts_1,
hist.eval(feed_dict={values: values_1}))
self.assertAllClose(expected_bin_counts_1,
hist.eval(feed_dict={values: values_1}))
self.assertAllClose(expected_bin_counts_2,
hist.eval(feed_dict={values: values_2}))
def test_multiple_random_accumulating_updates_results_in_right_dist(self):
# Accumulate the updates in a new variable. Resultant
# histogram should be uniform. Use only 3 bins because with many bins it
# would be unlikely that all would be close to 1/n. If someone ever wants
# to test that, it would be better to check that the cdf was linear.
value_range = [1.0, 4.14159]
with self.test_session() as sess:
values = tf.placeholder(tf.float32, shape=[4, 4, 4])
hist = tf.histogram_fixed_width(values,
value_range,
nbins=3,
dtype=tf.int64)
hist_accum = tf.Variable(tf.zeros_initializer([3], dtype=tf.int64))
hist_accum = hist_accum.assign_add(hist)
tf.initialize_all_variables().run()
for _ in range(100):
# Map the rv: U[0, 1] --> U[value_range[0], value_range[1]].
values_arr = (
value_range[0] +
(value_range[1] - value_range[0]) * self.rng.rand(4, 4, 4))
hist_accum_arr = sess.run(hist_accum, feed_dict={values: values_arr})
pmf = hist_accum_arr / float(hist_accum_arr.sum())
np.testing.assert_allclose(1 / 3, pmf, atol=0.02)
if __name__ == '__main__':
tf.test.main()