Bump tensorflow from 1.13.1 to 1.14.0

[dependabot-preview]

Bumps tensorflow from 1.13.1 to 1.14.0.

Release notes

Sourced from tensorflow's releases.

TensorFlow 1.14.0

Release 1.14.0

Major Features and Improvements

• This is the first 1.x release containing the compat.v2 module. This module is required to allow libraries to publish code which works in both 1.x and 2.x. After this release, no backwards incompatible changes are allowed in the 2.0 Python API.
• Turn on MKL-DNN contraction kernels by default. MKL-DNN dynamically dispatches the best kernel implementation based on CPU vector architecture. To disable them, build with --define=tensorflow_mkldnn_contraction_kernel=0.
• Non-Windows system libraries are now versioned. This should be a no-op for most users as it affects only system package maintainers or those building extensions to TensorFlow:
  • Python wheels (Pip packages) contain one library file.
    • Linux: libtensorflow_framework.so.1
    • MacOS: libtensorflow_framework.1.dylib
  • Our libtensorflow tarball archives contain the libtensorflow library and two symlinks. MacOS .dylib libraries are the same, but match MacOS library naming requirements (i.e. libtensorflow.1.dylib):
    • libtensorflow.so.1.14.0, the main library
    • libtensorflow.so.1, symlinked to the main library
    • libtensorflow.so, symlinked to .so.1

Behavioral changes

• Set default loss reduction as AUTO for improving reliability of loss scaling with distribution strategy and custom training loops. AUTO indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to SUM_OVER_BATCH_SIZE. When used in distribution strategy scope, outside of built-in training loops such as tf.keras compile and fit, we expect reduction value to be 'None' or 'SUM'. Using other values will raise an error.
• Wraps losses passed to the compile API (strings and v1 losses) which are not instances of v2 Loss class in LossWrapper class. => All losses will now use SUM_OVER_BATCH_SIZE reduction as default.
• Disable run_eagerly and distribution strategy if there are symbolic tensors added to the model using add_metric or add_loss.
• tf.linspace(start, stop, num) now always uses "stop" as last value (for num > 1)
• The behavior of tf.gather is now correct when axis=None and batch_dims<0.
• Only create a GCS directory object if the object does not already exist.
• In map_vectorization optimization, reduce the degree of parallelism in the vectorized map node.
• Bug fix: loss and gradients should now more reliably be correctly scaled w.r.t. the global batch size when using a tf.distribute.Strategy.
• Updating cosine similarity loss - removed the negate sign from cosine similarity.
• DType is no longer convertible to an int. Use dtype.as_datatype_enum instead of int(dtype) to get the same result.
• Changed default for gradient accumulation for TPU embeddings to true.
• Callbacks now log values in eager mode when a deferred build model is used.
• Transitive dependencies on :pooling_ops were removed. Some users may need to add explicit dependencies on :pooling_ops if they reference the operators from that library.

Bug Fixes and Other Changes

• Documentation
• Deprecations and Symbol renames.
  • The GPU configuration env parameter TF_CUDA_HOST_MEM_LIMIT_IN_MB has been changed to TF_GPU_HOST_MEM_LIMIT_IN_MB.
  • Remove unused StringViewVariantWrapper
  • Delete unused Fingerprint64Map op registration
  • SignatureDef util functions have been deprecated.
  • Renamed tf.image functions to remove duplicate "image" where it is redundant.
  • tf.keras.experimental.export renamed to tf.keras.experimental.export_saved_model
  • Standardize the LayerNormalization API by replacing the args norm_axis and params_axis with axis.
  • Tensor::UnsafeCopyFromInternal deprecated in favor Tensor::BitcastFrom
• Keras & Python API
  • Add v2 module aliases for:
    • tf.initializers => tf.keras.initializers
    • tf.losses => tf.keras.losses & tf.metrics => tf.keras.metrics
    • tf.optimizers => tf.keras.optimizers
  • Add tf.keras.layers.AbstractRNNCell as the preferred implementation of RNN cell for TF v2. User can use it to implement RNN cell with custom behavior.
  • Adding clear_losses API to be able to clear losses at the end of forward pass in a custom training loop in eager.

... (truncated)

Changelog

Sourced from tensorflow's changelog.

Release 1.14.0

Major Features and Improvements

• This is the first 1.x release containing the compat.v2 module. This module is required to allow libraries to publish code which works in both 1.x and 2.x. After this release, no backwards incompatible changes are allowed in the 2.0 Python API.
• Turn on MKL-DNN contraction kernels by default. MKL-DNN dynamically dispatches the best kernel implementation based on CPU vector architecture. To disable them, build with --define=tensorflow_mkldnn_contraction_kernel=0.
• Non-Windows system libraries are now versioned. This should be a no-op for most users as it affects only system package maintainers or those building extensions to TensorFlow:
  • Python wheels (Pip packages) contain one library file.
    • Linux: libtensorflow_framework.so.1
    • MacOS: libtensorflow_framework.1.dylib
  • Our libtensorflow tarball archives contain the libtensorflow library and two symlinks. MacOS .dylib libraries are the same, but match MacOS library naming requirements (i.e. libtensorflow.1.dylib):
    • libtensorflow.so.1.14.0, the main library
    • libtensorflow.so.1, symlinked to the main library
    • libtensorflow.so, symlinked to .so.1

Behavioral changes

• Set default loss reduction as AUTO for improving reliability of loss scaling with distribution strategy and custom training loops. AUTO indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to SUM_OVER_BATCH_SIZE. When used in distribution strategy scope, outside of built-in training loops such as tf.keras compile and fit, we expect reduction value to be 'None' or 'SUM'. Using other values will raise an error.
• Wraps losses passed to the compile API (strings and v1 losses) which are not instances of v2 Loss class in LossWrapper class. => All losses will now use SUM_OVER_BATCH_SIZE reduction as default.
• Disable run_eagerly and distribution strategy if there are symbolic tensors added to the model using add_metric or add_loss.
• tf.linspace(start, stop, num) now always uses "stop" as last value (for num > 1)
• The behavior of tf.gather is now correct when axis=None and batch_dims<0.
• Only create a GCS directory object if the object does not already exist.
• In map_vectorization optimization, reduce the degree of parallelism in the vectorized map node.
• Bug fix: loss and gradients should now more reliably be correctly scaled w.r.t. the global batch size when using a tf.distribute.Strategy.
• Updating cosine similarity loss - removed the negate sign from cosine similarity.
• DType is no longer convertible to an int. Use dtype.as_datatype_enum instead of int(dtype) to get the same result.
• Changed default for gradient accumulation for TPU embeddings to true.
• Callbacks now log values in eager mode when a deferred build model is used.
• Transitive dependencies on :pooling_ops were removed. Some users may need to add explicit dependencies on :pooling_ops if they reference the operators from that library.

Bug Fixes and Other Changes

• Documentation
• Deprecations and Symbol renames.
  • The GPU configuration env parameter TF_CUDA_HOST_MEM_LIMIT_IN_MB has been changed to TF_GPU_HOST_MEM_LIMIT_IN_MB.
  • Remove unused StringViewVariantWrapper
  • Delete unused Fingerprint64Map op registration
  • SignatureDef util functions have been deprecated.
  • Renamed tf.image functions to remove duplicate "image" where it is redundant.
  • tf.keras.experimental.export renamed to tf.keras.experimental.export_saved_model
  • Standardize the LayerNormalization API by replacing the args norm_axis and params_axis with axis.
  • Tensor::UnsafeCopyFromInternal deprecated in favor Tensor::BitcastFrom
• Keras & Python API
  • Add v2 module aliases for:
    • tf.initializers => tf.keras.initializers
    • tf.losses => tf.keras.losses & tf.metrics => tf.keras.metrics
    • tf.optimizers => tf.keras.optimizers
  • Add tf.keras.layers.AbstractRNNCell as the preferred implementation of RNN cell for TF v2. User can use it to implement RNN cell with custom behavior.
  • Adding clear_losses API to be able to clear losses at the end of forward pass in a custom training loop in eager.
  • Add support for passing list of lists to the metrics param in Keras compile.

... (truncated)

Commits

• 87989f6 Add Sergii Khomenko to contributor list
• af24dc9 Merge pull request #29924 from Intel-tensorflow/fixbatchnorm
• d657759 Changing the compat date
• 90f03ae Update RELEASE.md
• bf060d6 Update version numbers to 1.14.0
• a4311d3 Merge pull request #29888 from bananabowl/cherrypicks_33MUJ
• ed1d898 Merge pull request #29812 from tensorflow/bananabowl-patch-2
• 7e62543 Push forward compat date of gradients-of-log/exp/sqrt change
• ccdbc17 Revert forward horizon change, since Nov 11 was the tested horizon in RC1
• 6a9584c Merge pull request #29802 from wchargin/cherrypicks_B97ZJ
• Additional commits viewable in compare view

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Superseded by #34.