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Probable causes:
If you’re new to using TensorBoard, and want to find out how to add data and set up your event files, check out the README and perhaps the TensorBoard tutorial.
If you think TensorBoard is configured properly, please see the section of the README devoted to missing data problems and consider filing an issue on GitHub.
Probable causes:
To lay out the dashboard, pass a Layout protobuffer
to the set_layout method. For example,
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
# (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.*'],
)),
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(
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)
If you’re new to using TensorBoard, and want to find out how to add data and set up your event files, check out the README and perhaps the TensorBoard tutorial.
Probable causes:
If you’re new to using TensorBoard, and want to find out how to add data and set up your event files, check out the README and perhaps the TensorBoard tutorial.
If you think TensorBoard is configured properly, please see the section of the README devoted to missing data problems and consider filing an issue on GitHub.
Probable causes:
If you’re new to using TensorBoard, and want to find out how to add data and set up your event files, check out the README and perhaps the TensorBoard tutorial.
If you think TensorBoard is configured properly, please see the section of the README devoted to missing data problems and consider filing an issue on GitHub.
import tensorflow as tf
from tensorflow.python import debug as tf_debug
sess = tf.Session()
sess = tf_debug.TensorBoardDebugWrapperSession(sess, "[[_host]]:[[_port]]")
sess.run(my_fetches)
import tensorflow as tf
from tensorflow.python import debug as tf_debug
hook = tf_debug.TensorBoardDebugHook("[[_host]]:[[_port]]")
my_estimator.fit(x=x_data, y=y_data, steps=1000, monitors=[hook])
import tensorflow as tf
from tensorflow.python import debug as tf_debug
import keras
keras.backend.set_session(
tf_debug.TensorBoardDebugWrapperSession(tf.Session(), "[[_host]]:[[_port]]"))
# Define your keras model, called "model".
model.fit(...)
| Feeds | Fetches | Targets | #(Devices) | Count |
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| Tensor | Count | DType | Shape | Value |
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Alerts are sorted from top to bottom by increasing timestamp.
| First Offense | Tensor (Device) | Event Counts |
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No numeric alerts so far. That is likely good. Alerts indicate the presence of NaN or (+/-) Infinity values, which may be concerning.
Graph |
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OpNode
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Unconnected series*
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Connected series*
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Summary
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Control dependency edge
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To store a graph, create a
tf.summary.FileWriter
and pass the graph either via the constructor, or by calling its
add_graph() method. You may want to check out the
graph visualizer tutorial.
If you’re new to using TensorBoard, and want to find out how to add data and set up your event files, check out the README and perhaps the TensorBoard tutorial.
If you think TensorBoard is configured properly, please see the section of the README devoted to missing data problems and consider filing an issue on GitHub.
Probable causes:
If you’re new to using TensorBoard, and want to find out how to add data and set up your event files, check out the README and perhaps the TensorBoard tutorial.
If you think TensorBoard is configured properly, please see the section of the README devoted to missing data problems and consider filing an issue on GitHub.
Probable causes:
If you’re new to using TensorBoard, and want to find out how to add data and set up your event files, check out the README and perhaps the TensorBoard tutorial.
If you think TensorBoard is configured properly, please see the section of the README devoted to missing data problems and consider filing an issue on GitHub.
Probable causes:
If you’re new to using TensorBoard, and want to find out how to add data and set up your event files, check out the README and perhaps the TensorBoard tutorial.
If you think TensorBoard is configured properly, please see the section of the README devoted to missing data problems and consider filing an issue on GitHub.
Probable causes:
If you’re new to using TensorBoard, and want to find out how to add data and set up your event files, check out the README and perhaps the TensorBoard tutorial.
If you think TensorBoard is configured properly, please see the section of the README devoted to missing data problems and consider filing an issue on GitHub.
Probable causes:
beholder.update().
To use Beholder, import and instantiate the
Beholder class, and call its
update method with a Session argument
after every train step:
from tensorboard.plugins.beholder import Beholder beholder = Beholder(LOG_DIRECTORY) # inside train loop beholder.update( session=sess, arrays=list_of_np_arrays, # optional argument frame=two_dimensional_np_array, # optional argument )
If using tf.train.MonitoredSession, you can use
BeholderHook:
from tensorboard.plugins.beholder import BeholderHook beholder_hook = BeholderHook(LOG_DIRECTORY) with MonitoredSession(..., hooks=[beholder_hook]) as sess: sess.run(train_op)
If you think everything is set up properly, please see the README for more information and consider filing an issue on GitHub.
Note: Beholder currently only works well on local file systems.
Please select a session group to see its metric-graphs here.
Please enable some metrics to see content here.
Probable causes:
If you’re new to using TensorBoard, and want to find out how to add data and set up your event files, check out the README and perhaps the TensorBoard tutorial.
If you think TensorBoard is configured properly, please see the section of the README devoted to missing data problems and consider filing an issue on GitHub.
Probable causes:
If you’re new to using TensorBoard, and want to find out how to add data and set up your event files, check out the README and perhaps the TensorBoard tutorial.
If you think TensorBoard is configured properly, please see the section of the README devoted to missing data problems and consider filing an issue on GitHub.