- 02 11月, 2017 6 次提交
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由 Francois Chollet 提交于
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由 Francois Chollet 提交于
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由 Francois Chollet 提交于
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由 Simon Gate 提交于
* Skip newlines in TTY AND ipykernel. After the addition of tty check output was ruined in jupyter. Go back to the old ways when running jupyter notebooks and ipython. * Trigger travis * Only check for ipykernel once when initiating progbar * Add colon
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由 Yu-Yang Huang 提交于
* Enable loading CuDNNLSTM weights into an LSTM layer * Convert the kernels during weight loading
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由 Oleg Zabluda 提交于
* Simplify reporthook-related logic * More of the same
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- 01 11月, 2017 6 次提交
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由 Oleg Zabluda 提交于
* Print time per step * pep8
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由 Fariz Rahman 提交于
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由 Oleg Zabluda 提交于
* Improve synchronized shuffle * pep8
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由 Oleg Zabluda 提交于
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由 Jason Saporta 提交于
* Added TensorFlow Dataset API example. * Made most recommended changes to dataset API example.
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由 Yu-Yang Huang 提交于
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- 31 10月, 2017 2 次提交
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由 Oleg Zabluda 提交于
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由 Michal Podhradsky 提交于
* Update cifar10.py * Update cifar100.py
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- 30 10月, 2017 1 次提交
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由 fchollet 提交于
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- 28 10月, 2017 3 次提交
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由 Ritwik Gupta 提交于
* Increase file save hash size Issue #8249 runs into a problem where the max hash size is low, resulting in files being potentially overwritten. The behavior of this functionality is as such: As batch size increases, the probability that any given file will have a conflict in the hash reduces. However, when the author wants to save images with a batch size of one, the probability of a conflict increases since {index} is always 1. This seems to just be an unforeseen bug because a batch size of one is uncommon. * Change hash size to 1e7
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由 Oleg Zabluda 提交于
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由 Oli Lalonde 提交于
For example, if len(x) is 3 and the batch size is 2, 3//2 == 1 but there really is 2 batches.
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- 27 10月, 2017 4 次提交
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由 Oleg Zabluda 提交于
* Add missing parameter name to K.identity() * Remove unneeded parenthesis
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由 Oleg Zabluda 提交于
Resolves https://github.com/fchollet/keras/issues/8232
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由 Oleg Zabluda 提交于
* Simplify - remove K.cast() * pep8
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由 Oleg Zabluda 提交于
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- 26 10月, 2017 7 次提交
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由 nzw 提交于
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由 Oleg Zabluda 提交于
* Simplify, clarify, sync style examples/mnist_net2net.py * Pass globals as arguments into functions * Pass epochs as arguments into functions * Remove default epochs value
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由 Julien Rebetez 提交于
* Add a model.check_trainable_weights_consistency This will raise a UserWarning when the user modifies model.trainable and tries to print a model summary or launch a fit without having called .compile. Calling .compile() is necessary because trainable weights are collected in compile (model._collected_trainable_weights). * Fix comments and cosmetics on count_params * Make Model.check_trainable_weights_consistency private Also fix its docstring * Fix docstring of test_trainable_weights_count_consistency
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由 Julien Rebetez 提交于
* Expose and document utils.layer_utils.print_summary as utils.print_summary Fixes #8105 * Fix docs autogen when running on python 3.6.2 When a class inherits from object without defining __init__, AttributeError is raised instead of TypeError. So we need to catch both exceptions to be compatible. * Fix process_XXX_docstring functions to work with models function The process_function_docstring would fail to parse the “# Argument” and “# Returns” of class functions. This was because it expected the “# Argument” string to be preceeded by exactly a newline and 4 spaces, but for class methods, that would be a newline and 8 spaces. This fixes the problem by expecting a newline and some number of space.
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由 François Chollet 提交于
* Quick fix cudnn rnns * Tests passing.
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由 Akshay Chawla 提交于
* Normalize device names in multi_gpu_model (#8213) * added _normalize_device_name function to training_utils.py --> convert "/device:GPU:0" to "/gpu:0" * call _normalize_device_name() after obtaining available devices in multi_gpu_model() before verifying against target_devices. * Successfully run tests in tests/keras/utils/multi_gpu_test.py * Fix PEP8 violations in keras/utils/training_utils.py using autopep8
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由 Francois Chollet 提交于
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- 25 10月, 2017 5 次提交
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由 Anders Huss 提交于
* Added support for passing external constants to RNN, which will pass them on to the cell * Added class for allowing functional composition of RNN Cells, supporting constants * put back accidentally commented out recurrent tests * added basic example of functional cell * new class AttentionRNN * restored RNN layer * renamed constants to attended in FunctionRNNCell, avoided duplicating outputs in wrapped model * minor clean-up of docs * Minor cleanup & improvments in docs, fixed PEP breaking formatting in attention test * removed FunctionalRNNCell and AttentionRNN, added back support for constants in RNN * fixed PEP8 violations * fixed minor review comments * added test case for when both inital_state and constants are passed to RNN.__call__
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由 Oleg Zabluda 提交于
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由 Oleg Zabluda 提交于
Rename random_seed => seed for consistency with the rest of the code. Maybe resolves https://github.com/fchollet/keras/issues/8227, although maybe GeneratorEnqueuer.seed should be eliminated altogether, because currently it's not used anywhere in Keras code.
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由 Noah Stier 提交于
* Fix samplewise normalization in ImageDataGenerator Normalize pixels uniformly by the image mean and std * dont specify axes for samplewise image normalization
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由 Hans Gaiser 提交于
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- 24 10月, 2017 6 次提交
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由 Oleg Zabluda 提交于
* Sync Sequential.compile() with Model.compile() * Remove loss_weights in Sequential.compile() It may be confusing for some people for a model with only one output. If you need this feature, use functional Model API. * Put Sequential.loss_weights attribute back. its removal breaks tests and stuff.
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由 Icyblade Dai 提交于
* add support for pandas DataFrame * multiple updates according to @fchollet's review * DataFrame should be handled correctly if list/dict is passed as model inputs/outputs
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由 Francois Chollet 提交于
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由 Collin Donahue-Oponski 提交于
* Clarify docs for `shuffle` arg of `fit_generator` * Clarify docs on `shuffle` arg of `model.fit_generator` * Clarify docs for `shuffle` arg of `fit_generator` * Clarify docs for `shuffle` arg of `fit_generator`
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由 Francois Chollet 提交于
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