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Commits on Nov 11, 2017

• Improvement to benchmark.

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• More gradient-related microbenchmarks.

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• Fixes API difference between eager and graph cond.

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• Tensor template argument to gradienttape was unnecessary.

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• Instances per second in the eager microbenchmarks.

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• EagerVariableStore.trainable_variables()

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• Removes void*s from the tape gradient code, replacing with templates.

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• More idiomatic tests for defuns using variables.

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• Moves imperative_grad to C

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Commits on Nov 10, 2017

• Tensor template argument to gradienttape was unnecessary.

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Commits on Nov 9, 2017

• Instances per second in the eager microbenchmarks.

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• EagerVariableStore.trainable_variables()

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• Removes void*s from the tape gradient code, replacing with templates.

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Commits on Nov 8, 2017

• More idiomatic tests for defuns using variables.

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• Moves imperative_grad to C

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Commits on Nov 7, 2017

• Unicode handling in py2 when eager execution is enabled.

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• Fixes indendation bug in make_vjp

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Commits on Nov 6, 2017

• Memory leak detected when running a heap checker in our tests.

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Commits on Nov 3, 2017

• Allowing __iter__ over 1+dimensional tensors with known shapes.

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Commits on Oct 30, 2017

• scatter_update for resource variables

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• No need for unique variable names in eager.

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• Always run iterator deleter in eager mode for safety.

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• EagerVariableStore, for compatibility with functional layers.

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Commits on Oct 27, 2017

• Fix issue with gradients of functions which return multiple values.

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• custom_gradient functions should be able to return their inputs

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• Shorter import for tfe.

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• Saves summaries in the mnist example.

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Commits on Oct 26, 2017

• Implements count_up_to for resource variables.

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• Adding summaries to the resnet example.

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• Proper destructuring of function arguments.

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• Support general callables in backprop.

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Commits on Oct 25, 2017

• Explicitly disables variable inplace assignment methods.

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• Disables storing variables in the default variable store for eager.

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Commits on Oct 24, 2017

• bool() for eager variables

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• Exception instead of crashing on resource.numpy()

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