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tensorflow/c/experimental/gradients/math_grad.cc
absl::Span<AbstractTensorHandle*> grad_inputs) override { // TODO(vnvo2409): Add control dependency /* Given upstream grad U and a Log1p op: Y = log(1 + X), the gradients are: * * dX = U / (1 + X) * */ AbstractTensorHandle* upstream_grad = grad_outputs[0]; AbstractTensorHandle* X = forward_inputs_[0];
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RELEASE.md
By default, Keras always writes the logs to stdout. However, this is not optimal in a non-interactive environment, where you don't have access to stdout, but can only view the logs. You can use `tf.keras.utils.disable_interactive_logging()` to write the logs to ABSL logging. You can also use `tf.keras.utils.enable_interactive_logging()`
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tensorflow/BUILD
"//tensorflow/lite/kernels:reference_ops", "//tensorflow/lite/schema:schema_fbs", "//tensorflow/lite/toco/logging:conversion_log_util", "//tensorflow/lite/toco/logging:toco_conversion_log_proto_cc", "//tensorflow/lite/toco:model_flags_proto_cc", "//tensorflow/lite/toco:toco_convert", "//tensorflow/lite/toco:toco_flags_proto_cc",
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.bazelrc
# Do not risk cache corruption. See: # https://github.com/bazelbuild/bazel/issues/3360 build:linux --experimental_guard_against_concurrent_changes # Configure short or long logs build:short_logs --output_filter=DONT_MATCH_ANYTHING build:verbose_logs --output_filter= # Instruction set optimizations # TODO(gunan): Create a feature in toolchains for avx/avx2 to # avoid having to define linux/win separately.
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.github/ISSUE_TEMPLATE/tensorflow_issue_template.yaml
placeholder: Tell us what you see! value: render: shell validations: required: true - type: textarea id: logs attributes: label: Relevant log output description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
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ci/official/containers/linux_arm64/devel.usertools/squash_testlogs.py
files = subprocess.check_output( ["grep", "-rlE", '(failures|errors)="[1-9]', sys.argv[1]] ) except subprocess.CalledProcessError as e: print("No failures found to log!") exit(0) # For test cases, only show the ones that failed that have text (a log) seen = collections.Counter() runfiles_matcher = re.compile(r"(/.*\.runfiles/)") for f in files.strip().splitlines(): # Just ignore any failures, they're probably not important
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.github/ISSUE_TEMPLATE/tflite-op-request.md
Also, please include a link to a GraphDef or the model if possible. **Any other info / logs** Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files
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ci/official/utilities/extract_resultstore_links.py
else: tests_failed = re.search(TESTS_FAILED_RE, backtrack_line) if build_failed or tests_failed: log_fragment = '\n'.join( log_lines[max(k - 20, 0):min(end_line + 1, len(log_lines) - 1)]) lines['log_fragment'] = log_fragment lines['status'] = (InvokeStatus.build_failed if build_failed else InvokeStatus.tests_failed) if verbose:
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ci/official/utilities/setup.sh
export TFCI_OUTPUT_DIR=$(realpath "$TFCI_OUTPUT_DIR") mkdir -p "$TFCI_OUTPUT_DIR" # In addition to dumping all script output to the terminal, place it into # $TFCI_OUTPUT_DIR/script.log exec > >(tee "$TFCI_OUTPUT_DIR/script.log") 2>&1 # Setup tfrun, a helper function for executing steps that can either be run # locally or run under Docker. setup_docker.sh, below, redefines it as "docker # exec".
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tensorflow/c/c_api.h
// Register a listener method that processes printed messages. // // If any listeners are registered, the print operator will call all listeners // with the printed messages and immediately return without writing to the // logs. TF_CAPI_EXPORT extern void TF_RegisterLogListener( void (*listener)(const char*)); // Register a FileSystem plugin from filename `plugin_filename`. // // On success, place OK in status.
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