How to use the tensorboard.plugins.custom_scalar.layout_pb2.Chart function in tensorboard
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Orienfish / ur3-RL / deep_q_network_part_v2.py View on Github 
from tensorboard.plugins.custom_scalar import layout_pb2
# This action does not have to be performed at every step, so the action is not
# taken care of by an op in the graph. We only need to specify the layout once.
# We only need to specify the layout once (instead of per step).
layout_summary = summary.custom_scalar_pb(layout_pb2.Layout(
category=[
layout_pb2.Category(
title='losses',
chart=[
layout_pb2.Chart(
title='losses',
multiline=layout_pb2.MultilineChartContent(
tag=[r'loss.*'],
)),
layout_pb2.Chart(
title='baz',
margin=layout_pb2.MarginChartContent(
series=[
layout_pb2.MarginChartContent.Series(
value='loss/baz/scalar_summary',
lower='baz_lower/baz/scalar_summary',
upper='baz_upper/baz/scalar_summary'),
],
)),
]),
layout_pb2.Category(
title='trig functions',
chart=[
layout_pb2.Chart(
title='wave trig functions',
multiline=layout_pb2.MultilineChartContent(margin=layout_pb2.MarginChartContent(
series=[
layout_pb2.MarginChartContent.Series(
value="loss/baz/scalar_summary",
lower="loss/baz_lower/scalar_summary",
upper="loss/baz_upper/scalar_summary",
),
],
),
),
],
),
layout_pb2.Category(
title="trig functions",
chart=[
layout_pb2.Chart(
title="wave trig functions",
multiline=layout_pb2.MultilineChartContent(
tag=[
r"trigFunctions/cosine",
r"trigFunctions/sine",
],
),
),
# The range of tangent is different. Give it its own chart.
layout_pb2.Chart(
title="tan",
multiline=layout_pb2.MultilineChartContent(
tag=[r"trigFunctions/tangent"],
),
),
],from tensorboard.plugins.custom_scalar import layout_pb2
# This action does not have to be performed at every step, so the action is not
# taken care of by an op in the graph. We only need to specify the layout once.
# We only need to specify the layout once (instead of per step).
layout_summary = summary.custom_scalar_pb(layout_pb2.Layout(
category=[
layout_pb2.Category(
title='losses',
chart=[
layout_pb2.Chart(
title='losses',
multiline=layout_pb2.MultilineChartContent(
tag=[r'loss.*'],
)),
layout_pb2.Chart(
title='baz',
margin=layout_pb2.MarginChartContent(
series=[
layout_pb2.MarginChartContent.Series(
value='loss/baz/scalar_summary',
lower='baz_lower/baz/scalar_summary',
upper='baz_upper/baz/scalar_summary'),
],
)),
]),
layout_pb2.Category(
title='trig functions',
chart=[
layout_pb2.Chart(
title='wave trig functions',
multiline=layout_pb2.MultilineChartContent(value='loss/baz/scalar_summary',
lower='baz_lower/baz/scalar_summary',
upper='baz_upper/baz/scalar_summary'),
],
)),
]),
layout_pb2.Category(
title='trig functions',
chart=[
layout_pb2.Chart(
title='wave trig functions',
multiline=layout_pb2.MultilineChartContent(
tag=[r'trigFunctions/cosine', r'trigFunctions/sine'],
)),
# The range of tangent is different. Let's give it its own chart.
layout_pb2.Chart(
title='tan',
multiline=layout_pb2.MultilineChartContent(
tag=[r'trigFunctions/tangent'],
)),
],
# This category we care less about. Let's make it initially closed.
closed=True),
]))
writer.add_summary(layout_summary)from tensorboard.plugins.custom_scalar import layout_pb2
# This action does not have to be performed at every step, so the action is not
# taken care of by an op in the graph. We only need to specify the layout once.
# We only need to specify the layout once (instead of per step).
layout_summary = summary.custom_scalar_pb(layout_pb2.Layout(
category=[
layout_pb2.Category(
title='losses',
chart=[
layout_pb2.Chart(
title='losses',
multiline=layout_pb2.MultilineChartContent(
tag=[r'loss.*'],
)),
layout_pb2.Chart(
title='baz',
margin=layout_pb2.MarginChartContent(
series=[
layout_pb2.MarginChartContent.Series(
value='loss/baz/scalar_summary',
lower='baz_lower/baz/scalar_summary',
upper='baz_upper/baz/scalar_summary'),
],
)),
]),
layout_pb2.Category(
title='trig functions',
chart=[
layout_pb2.Chart(
title='wave trig functions',
multiline=layout_pb2.MultilineChartContent(value='loss/baz/scalar_summary',
lower='baz_lower/baz/scalar_summary',
upper='baz_upper/baz/scalar_summary'),
],
)),
]),
layout_pb2.Category(
title='trig functions',
chart=[
layout_pb2.Chart(
title='wave trig functions',
multiline=layout_pb2.MultilineChartContent(
tag=[r'trigFunctions/cosine', r'trigFunctions/sine'],
)),
# The range of tangent is different. Let's give it its own chart.
layout_pb2.Chart(
title='tan',
multiline=layout_pb2.MultilineChartContent(
tag=[r'trigFunctions/tangent'],
)),
],
# This category we care less about. Let's make it initially closed.
closed=True),
]))
writer.add_summary(layout_summary)def layout_dashboard(writer):
from tensorboard import summary
from tensorboard.plugins.custom_scalar import layout_pb2
# This action does not have to be performed at every step, so the action is not
# taken care of by an op in the graph. We only need to specify the layout once.
# We only need to specify the layout once (instead of per step).
layout_summary = summary.custom_scalar_pb(layout_pb2.Layout(
category=[
layout_pb2.Category(
title='losses',
chart=[
layout_pb2.Chart(
title='losses',
multiline=layout_pb2.MultilineChartContent(
tag=[r'loss.*'],
)),
layout_pb2.Chart(
title='baz',
margin=layout_pb2.MarginChartContent(
series=[
layout_pb2.MarginChartContent.Series(
value='loss/baz/scalar_summary',
lower='baz_lower/baz/scalar_summary',
upper='baz_upper/baz/scalar_summary'),
],
)),
]),
layout_pb2.Category(def gen_multiline_charts(cfg):
charts = []
for title, tags in cfg:
charts.append(
layout_pb2.Chart(title=title, multiline=layout_pb2.MultilineChartContent(tag=tags))
)
return chartsLOGDIR
) as writer:
# We only need to specify the layout once (instead of per step).
layout_summary = summary_lib.custom_scalar_pb(
layout_pb2.Layout(
category=[
layout_pb2.Category(
title="losses",
chart=[
layout_pb2.Chart(
title="losses",
multiline=layout_pb2.MultilineChartContent(
tag=[r"loss(?!.*margin.*)"],
),
),
layout_pb2.Chart(
title="baz",
margin=layout_pb2.MarginChartContent(
series=[
layout_pb2.MarginChartContent.Series(
value="loss/baz/scalar_summary",
lower="loss/baz_lower/scalar_summary",
upper="loss/baz_upper/scalar_summary",
),
],
),
),
],
),
layout_pb2.Category(
title="trig functions",
chart=[tensorboard
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91 / 100Popular tensorboard functions
- tensorboard.backend.event_processing.event_accumulator.EventAccumulator
- tensorboard.backend.http_util.Respond
- tensorboard.compat.proto.summary_pb2.Summary
- tensorboard.compat.proto.types_pb2
- tensorboard.compat.tensorflow_stub.dtypes.DType
- tensorboard.compat.tf
- tensorboard.compat.tf2
- tensorboard.FileWriter
- tensorboard.plugins.base_plugin
- tensorboard.plugins.custom_scalar.layout_pb2
- tensorboard.plugins.custom_scalar.layout_pb2.Category
- tensorboard.plugins.custom_scalar.layout_pb2.Chart
- tensorboard.plugins.custom_scalar.layout_pb2.Layout
- tensorboard.plugins.custom_scalar.layout_pb2.MarginChartContent
- tensorboard.plugins.hparams.api_pb2
- tensorboard.plugins.projector.visualize_embeddings
- tensorboard.SummaryWriter
- tensorboard.util.tb_logging
- tensorboard.util.tb_logging.get_logger
- tensorboard.util.tensor_util.make_ndarray