def setUp(self):
super(MultiplexerDataProviderTest, self).setUp()
self.logdir = self.get_temp_dir()
logdir = os.path.join(self.logdir, "polynomials")
with tf.summary.create_file_writer(logdir).as_default():
for i in xrange(10):
scalar_summary.scalar("square",
i**2,
step=2 * i,
description="boxen")
scalar_summary.scalar("cube", i**3, step=3 * i)
logdir = os.path.join(self.logdir, "waves")
with tf.summary.create_file_writer(logdir).as_default():
for i in xrange(10):
scalar_summary.scalar("sine", tf.sin(float(i)), step=i)
scalar_summary.scalar("square",
tf.sign(tf.sin(float(i))),
step=i)
# Summary with rank-0 data but not owned by the scalars plugin.
metadata = summary_pb2.SummaryMetadata()
metadata.plugin_data.plugin_name = "marigraphs"
metadata.data_class = summary_pb2.DATA_CLASS_SCALAR
tf.summary.write("high_tide",
tensor=i,
step=i,
metadata=metadata)
# Summary with rank-1 data of scalar data class (bad!).
metadata = summary_pb2.SummaryMetadata()
metadata.plugin_data.plugin_name = "greetings"
metadata.data_class = summary_pb2.DATA_CLASS_SCALAR
tf.summary.write("bad",
tensor=[i, i],
step=i,
metadata=metadata)
logdir = os.path.join(self.logdir, "lebesgue")
with tf.summary.create_file_writer(logdir).as_default():
data = [
("very smooth", (0.0, 0.25, 0.5, 0.75, 1.0), "uniform"),
("very smoothn't", (0.0, 0.01, 0.99, 1.0), "bimodal"),
]
for (description, distribution, name) in data:
tensor = tf.constant([distribution], dtype=tf.float64)
for i in xrange(1, 11):
histogram_summary.histogram(name,
tensor * i,
step=i,
description=description)
def call(self, x):
# Add summary histogram using compat v2 implementation.
histogram_summary_v2.histogram('custom_histogram_summary_v2', x)
return x
def setUp(self):
super(MultiplexerDataProviderTest, self).setUp()
self.logdir = self.get_temp_dir()
self.ctx = context.RequestContext()
logdir = os.path.join(self.logdir, "polynomials")
with tf.summary.create_file_writer(logdir).as_default():
for i in range(10):
scalar_summary.scalar(
"square", i ** 2, step=2 * i, description="boxen"
)
scalar_summary.scalar("cube", i ** 3, step=3 * i)
logdir = os.path.join(self.logdir, "waves")
with tf.summary.create_file_writer(logdir).as_default():
for i in range(10):
scalar_summary.scalar("sine", tf.sin(float(i)), step=i)
scalar_summary.scalar(
"square", tf.sign(tf.sin(float(i))), step=i
)
# Summary with rank-0 data but not owned by the scalars plugin.
metadata = summary_pb2.SummaryMetadata()
metadata.plugin_data.plugin_name = "marigraphs"
metadata.data_class = summary_pb2.DATA_CLASS_SCALAR
tf.summary.write(
"high_tide", tensor=i, step=i, metadata=metadata
)
# Summary with rank-1 data of scalar data class (bad!).
metadata = summary_pb2.SummaryMetadata()
metadata.plugin_data.plugin_name = "greetings"
metadata.data_class = summary_pb2.DATA_CLASS_SCALAR
tf.summary.write(
"bad", tensor=[i, i], step=i, metadata=metadata
)
logdir = os.path.join(self.logdir, "lebesgue")
with tf.summary.create_file_writer(logdir).as_default():
data = [
("very smooth", (0.0, 0.25, 0.5, 0.75, 1.0), "uniform"),
("very smoothn't", (0.0, 0.01, 0.99, 1.0), "bimodal"),
]
for (description, distribution, name) in data:
tensor = tf.constant([distribution], dtype=tf.float64)
for i in range(1, 11):
histogram_summary.histogram(
name, tensor * i, step=i, description=description
)
logdir = os.path.join(self.logdir, "mondrian")
with tf.summary.create_file_writer(logdir).as_default():
data = [
("red", (221, 28, 38), "top-right"),
("blue", (1, 91, 158), "bottom-left"),
("yellow", (239, 220, 111), "bottom-right"),
]
for (name, color, description) in data:
image_1x1 = tf.constant([[[color]]], dtype=tf.uint8)
for i in range(1, 11):
# Use a non-monotonic sequence of sample sizes to
# test `max_length` calculation.
k = 6 - abs(6 - i) # 1, .., 6, .., 2
# a `k`-sample image summary of `i`-by-`i` images
image = tf.tile(image_1x1, [k, i, i, 1])
image_summary.image(
name,
image,
step=i,
description=description,
max_outputs=99,
)
def host_computation(x):
histogram_summary_v2.histogram("x", x, step=0)
return x * 2.0
def histo(name, data, step=None, buckets=None):
if step is None:
step = tflex.get_or_create_global_step()
#return histogram_summary.summary_v2.histogram(name, data, step=step)
return histogram_summary_v2.histogram(name, data, step=step, buckets=buckets)
