tf.compat.v1.profiler.Profiler

Log provided 'op_log', and add additional model information below.

tf.compat.v1.profiler.write_op_log(
    graph, log_dir, op_log=None, run_meta=None, add_trace=True
)

The API also assigns ops in tf.compat.v1.trainable_variables() an op type called '_trainable_variables'. The API also logs 'flops' statistics for ops with op.RegisterStatistics() defined. flops calculation depends on Tensor shapes defined in 'graph', which might not be complete. 'run_meta', if provided, completes the shape information with best effort.

Args
`graph`	tf.Graph. If None and eager execution is not enabled, use default graph.
`log_dir`	directory to write the log file.
`op_log`	(Optional) OpLogProto proto to be written. If not provided, an new one is created.
`run_meta`	(Optional) RunMetadata proto that helps flops computation using run time shape information.
`add_trace`	Whether to add python code trace information. Used to support "code" view.

TensorFlow

tf.compat / v1 / v1.profiler.Profiler

Args

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.compat / v1 / v1.profiler.Profiler

Args