def _custom_step(features, labels):
del labels
logits = model(features)
with summary_ops_v2.record_if(True), writer.as_default():
scalar_summary_v2.scalar('logits',
math_ops.reduce_sum(logits),
step=model.optimizer.iterations)
return logits
def _custom_step(features, labels):
del labels
logits = model(features)
with tf.summary.record_if(True), writer.as_default():
scalar_summary_v2.scalar(
"logits",
tf.reduce_sum(logits),
step=model.optimizer.iterations,
)
return logits
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"
tf.summary.write("high_tide", tensor=i, step=i, metadata=metadata)
logdir = os.path.join(self.logdir, "pictures")
with tf.summary.create_file_writer(logdir).as_default():
purple = tf.constant([[[255, 0, 255]]], dtype=tf.uint8)
for i in xrange(1, 11):
image_summary.image("purple", [tf.tile(purple, [i, i, 1])], step=i)
def generate_testdata(self, include_text=True, logdir=None):
run_names = ["fry", "leela"]
for run_name in run_names:
subdir = os.path.join(self.logdir, run_name)
writer = tf.compat.v2.summary.create_file_writer(subdir)
with writer.as_default():
step = 0
for gem in GEMS:
message = run_name + " *loves* " + gem
if include_text:
text("message", message, step)
step += 1
vector_message = ["one", "two", "three", "four"]
if include_text:
text("vector", vector_message, 0)
scalar("twelve", tf.constant(12), 0)
writer.close()
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 scalar using compat v2 implementation.
scalar_summary_v2.scalar('custom_scalar_summary_v2',
math_ops.reduce_sum(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 call(self, x):
# Add summary scalar using compat v2 implementation.
scalar_summary_v2.scalar("custom_scalar_summary_v2", tf.reduce_sum(x))
return x
def computation(x):
x = x + 1.0
if x < 5:
scalar_summary_v2.scalar("x", x, step=0)
x = x * 2.0
return x + 1.0
def host_computation(x):
scalar_summary_v2.scalar("x", x, step=0)
return x * 2.0
