def generate_run(self, run_name):
tf.compat.v1.reset_default_graph()
sess = tf.compat.v1.Session()
placeholder = tf.compat.v1.placeholder(tf.float32, shape=[3])
if run_name == self._RUN_WITH_LEGACY_DISTRIBUTION:
tf.compat.v1.summary.histogram(self._LEGACY_DISTRIBUTION_TAG,
placeholder)
elif run_name == self._RUN_WITH_DISTRIBUTION:
summary.op(
self._DISTRIBUTION_TAG,
placeholder,
display_name=self._DISPLAY_NAME,
description=self._DESCRIPTION,
)
elif run_name == self._RUN_WITH_SCALARS:
tf.compat.v1.summary.scalar(
self._SCALAR_TAG, tf.reduce_mean(input_tensor=placeholder))
else:
assert False, "Invalid run name: %r" % run_name
summ = tf.compat.v1.summary.merge_all()
subdir = os.path.join(self.logdir, run_name)
with test_util.FileWriterCache.get(subdir) as writer:
writer.add_graph(sess.graph)
for step in xrange(self._STEPS):
feed_dict = {placeholder: [1 + step, 2 + step, 3 + step]}
s = sess.run(summ, feed_dict=feed_dict)
writer.add_summary(s, global_step=step)
def generate_run(self, run_name):
tf.reset_default_graph()
sess = tf.Session()
placeholder = tf.placeholder(tf.float32, shape=[3])
if run_name == self._RUN_WITH_LEGACY_HISTOGRAM:
tf.summary.histogram(self._LEGACY_HISTOGRAM_TAG, placeholder)
elif run_name == self._RUN_WITH_HISTOGRAM:
summary.op(self._HISTOGRAM_TAG,
placeholder,
display_name=self._DISPLAY_NAME,
description=self._DESCRIPTION)
elif run_name == self._RUN_WITH_SCALARS:
tf.summary.scalar(self._SCALAR_TAG, tf.reduce_mean(placeholder))
else:
assert False, 'Invalid run name: %r' % run_name
summ = tf.summary.merge_all()
subdir = os.path.join(self.logdir, run_name)
writer = tf.summary.FileWriter(subdir)
writer.add_graph(sess.graph)
for step in xrange(self._STEPS):
feed_dict = {placeholder: [1 + step, 2 + step, 3 + step]}
s = sess.run(summ, feed_dict=feed_dict)
writer.add_summary(s, global_step=step)
writer.close()
def generate_run(self, run_name):
tf.reset_default_graph()
sess = tf.Session()
placeholder = tf.placeholder(tf.float32, shape=[3])
if run_name == self._RUN_WITH_LEGACY_HISTOGRAM:
tf.summary.histogram(self._LEGACY_HISTOGRAM_TAG, placeholder)
elif run_name == self._RUN_WITH_HISTOGRAM:
summary.op(self._HISTOGRAM_TAG, placeholder,
display_name=self._DISPLAY_NAME,
description=self._DESCRIPTION)
elif run_name == self._RUN_WITH_SCALARS:
tf.summary.scalar(self._SCALAR_TAG, tf.reduce_mean(placeholder))
else:
assert False, 'Invalid run name: %r' % run_name
summ = tf.summary.merge_all()
subdir = os.path.join(self.logdir, run_name)
writer = tf.summary.FileWriter(subdir)
writer.add_graph(sess.graph)
for step in xrange(self._STEPS):
feed_dict = {placeholder: [1 + step, 2 + step, 3 + step]}
s = sess.run(summ, feed_dict=feed_dict)
writer.add_summary(s, global_step=step)
writer.close()
def compute_and_check_summary_pb(self,
name='nemo',
data=None,
bucket_count=None,
display_name=None,
description=None,
data_tensor=None,
bucket_count_tensor=None,
feed_dict=None):
"""Use both `op` and `pb` to get a summary, asserting equality.
Returns:
a `Summary` protocol buffer
"""
if data is None:
data = self.gaussian
if data_tensor is None:
data_tensor = tf.constant(data)
if bucket_count_tensor is None:
bucket_count_tensor = bucket_count
op = summary.op(name,
data_tensor,
bucket_count=bucket_count_tensor,
display_name=display_name,
description=description)
pb = summary.pb(name,
data,
bucket_count=bucket_count,
display_name=display_name,
description=description)
pb_via_op = self.pb_via_op(op, feed_dict=feed_dict)
self.assertProtoEquals(pb, pb_via_op)
return pb
def test_new_style_histogram(self):
op = histogram_summary.op('important_data',
tf.random_normal(shape=[10, 10]),
bucket_count=100,
display_name='Important data',
description='secrets of the universe')
value = self._value_from_op(op)
assert value.HasField('tensor'), value
self._assert_noop(value)
def test_new_style_histogram(self):
op = histogram_summary.op('important_data',
tf.random_normal(shape=[10, 10]),
bucket_count=100,
display_name='Important data',
description='secrets of the universe')
value = self._value_from_op(op)
assert value.HasField('tensor'), value
self._assert_noop(value)
def test_new_style_histogram(self):
with tf.compat.v1.Graph().as_default():
op = histogram_summary.op(
"important_data",
tf.random.normal(shape=[10, 10]),
bucket_count=100,
display_name="Important data",
description="secrets of the universe",
)
value = self._value_from_op(op)
assert value.HasField("tensor"), value
self._assert_noop(value)
def histogram(self, *args, **kwargs):
# Map new name to the old name.
if "buckets" in kwargs:
kwargs["bucket_count"] = kwargs.pop("buckets")
return summary_pb2.Summary.FromString(
summary.op(*args, **kwargs).numpy())
def histogram(self, *args, **kwargs):
# Map new name to the old name.
if 'buckets' in kwargs:
kwargs['bucket_count'] = kwargs.pop('buckets')
return tf.Summary.FromString(summary.op(*args, **kwargs).numpy())
def run_all(logdir, verbose=False):
"""Generate a bunch of histogram data, and write it to logdir."""
del verbose
tf.compat.v1.set_random_seed(0)
k = tf.compat.v1.placeholder(tf.float32)
# Make a normal distribution, with a shifting mean
mean_moving_normal = tf.random.normal(shape=[1000], mean=(5*k), stddev=1)
# Record that distribution into a histogram summary
histogram_summary.op("normal/moving_mean",
mean_moving_normal,
description="A normal distribution whose mean changes "
"over time.")
# Make a normal distribution with shrinking variance
shrinking_normal = tf.random.normal(shape=[1000], mean=0, stddev=1-(k))
# Record that distribution too
histogram_summary.op("normal/shrinking_variance", shrinking_normal,
description="A normal distribution whose variance "
"shrinks over time.")
# Let's combine both of those distributions into one dataset
normal_combined = tf.concat([mean_moving_normal, shrinking_normal], 0)
# We add another histogram summary to record the combined distribution
histogram_summary.op("normal/bimodal", normal_combined,
description="A combination of two normal distributions, "
"one with a moving mean and one with "
"shrinking variance. The result is a "
"distribution that starts as unimodal and "
"becomes more and more bimodal over time.")
# Add a gamma distribution
gamma = tf.random.gamma(shape=[1000], alpha=k)
histogram_summary.op("gamma", gamma,
description="A gamma distribution whose shape "
"parameter, α, changes over time.")
# And a poisson distribution
poisson = tf.compat.v1.random_poisson(shape=[1000], lam=k)
histogram_summary.op("poisson", poisson,
description="A Poisson distribution, which only "
"takes on integer values.")
# And a uniform distribution
uniform = tf.random.uniform(shape=[1000], maxval=k*10)
histogram_summary.op("uniform", uniform,
description="A simple uniform distribution.")
# Finally, combine everything together!
all_distributions = [mean_moving_normal, shrinking_normal,
gamma, poisson, uniform]
all_combined = tf.concat(all_distributions, 0)
histogram_summary.op("all_combined", all_combined,
description="An amalgamation of five distributions: a "
"uniform distribution, a gamma "
"distribution, a Poisson distribution, and "
"two normal distributions.")
summaries = tf.compat.v1.summary.merge_all()
# Setup a session and summary writer
sess = tf.compat.v1.Session()
writer = tf.summary.FileWriter(logdir)
# Setup a loop and write the summaries to disk
N = 400
for step in xrange(N):
k_val = step/float(N)
summ = sess.run(summaries, feed_dict={k: k_val})
writer.add_summary(summ, global_step=step)
def run_all(logdir, verbose=False):
"""Generate a bunch of histogram data, and write it to logdir."""
del verbose
tf.set_random_seed(0)
k = tf.placeholder(tf.float32)
# Make a normal distribution, with a shifting mean
mean_moving_normal = tf.random_normal(shape=[1000], mean=(5*k), stddev=1)
# Record that distribution into a histogram summary
histogram_summary.op("normal/moving_mean",
mean_moving_normal,
description="A normal distribution whose mean changes "
"over time.")
# Make a normal distribution with shrinking variance
shrinking_normal = tf.random_normal(shape=[1000], mean=0, stddev=1-(k))
# Record that distribution too
histogram_summary.op("normal/shrinking_variance", shrinking_normal,
description="A normal distribution whose variance "
"shrinks over time.")
# Let's combine both of those distributions into one dataset
normal_combined = tf.concat([mean_moving_normal, shrinking_normal], 0)
# We add another histogram summary to record the combined distribution
histogram_summary.op("normal/bimodal", normal_combined,
description="A combination of two normal distributions, "
"one with a moving mean and one with "
"shrinking variance. The result is a "
"distribution that starts as unimodal and "
"becomes more and more bimodal over time.")
# Add a gamma distribution
gamma = tf.random_gamma(shape=[1000], alpha=k)
histogram_summary.op("gamma", gamma,
description="A gamma distribution whose shape "
"parameter, α, changes over time.")
# And a poisson distribution
poisson = tf.random_poisson(shape=[1000], lam=k)
histogram_summary.op("poisson", poisson,
description="A Poisson distribution, which only "
"takes on integer values.")
# And a uniform distribution
uniform = tf.random_uniform(shape=[1000], maxval=k*10)
histogram_summary.op("uniform", uniform,
description="A simple uniform distribution.")
# Finally, combine everything together!
all_distributions = [mean_moving_normal, shrinking_normal,
gamma, poisson, uniform]
all_combined = tf.concat(all_distributions, 0)
histogram_summary.op("all_combined", all_combined,
description="An amalgamation of five distributions: a "
"uniform distribution, a gamma "
"distribution, a Poisson distribution, and "
"two normal distributions.")
summaries = tf.summary.merge_all()
# Setup a session and summary writer
sess = tf.Session()
writer = tf.summary.FileWriter(logdir)
# Setup a loop and write the summaries to disk
N = 400
for step in xrange(N):
k_val = step/float(N)
summ = sess.run(summaries, feed_dict={k: k_val})
writer.add_summary(summ, global_step=step)
def run_all(logdir, verbose=False):
"""Generate a bunch of histogram data, and write it to logdir."""
del verbose
tf.set_random_seed(0)
k = tf.placeholder(tf.float32)
# Make a normal distribution, with a shifting mean
mean_moving_normal = tf.random_normal(shape=[1000], mean=(5 * k), stddev=1)
# Record that distribution into a histogram summary
histogram_summary.op("normal/moving_mean", mean_moving_normal)
# Make a normal distribution with shrinking variance
shrinking_normal = tf.random_normal(shape=[1000], mean=0, stddev=1 - (k))
# Record that distribution too
histogram_summary.op("normal/shrinking_variance", shrinking_normal)
# Let's combine both of those distributions into one dataset
normal_combined = tf.concat([mean_moving_normal, shrinking_normal], 0)
# We add another histogram summary to record the combined distribution
histogram_summary.op("normal/bimodal", normal_combined)
# Add a gamma distribution
gamma = tf.random_gamma(shape=[1000], alpha=k)
histogram_summary.op("gamma", gamma)
# And a poisson distribution
poisson = tf.random_poisson(shape=[1000], lam=k)
histogram_summary.op("poisson", poisson)
# And a uniform distribution
uniform = tf.random_uniform(shape=[1000], maxval=k * 10)
histogram_summary.op("uniform", uniform)
# Finally, combine everything together!
all_distributions = [
mean_moving_normal, shrinking_normal, gamma, poisson, uniform
]
all_combined = tf.concat(all_distributions, 0)
histogram_summary.op("all_combined", all_combined)
summaries = tf.summary.merge_all()
# Setup a session and summary writer
sess = tf.Session()
writer = tf.summary.FileWriter(logdir)
# Setup a loop and write the summaries to disk
N = 400
for step in xrange(N):
k_val = step / float(N)
summ = sess.run(summaries, feed_dict={k: k_val})
writer.add_summary(summ, global_step=step)