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keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/ops/numpy_test.py | 1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/ops/numpy.py | 1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/preprocessing/normalization.py | 1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/backend/tensorflow/numpy.py | 1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/normalization/unit_normalization.py | 1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/backend/numpy/numpy.py | 1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/backend/jax/numpy.py | 1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/backend/torch/numpy.py | 1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./examples/keras_io/tensorflow/timeseries/eeg_signal_classification.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/convolutional/separable_conv_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/activations/activation_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/pooling/__init__.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/pooling/max_pooling1d.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/utils/naming.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/attention/attention_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/core/input_layer.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/optimizers/adamw_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/optimizers/adafactor.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/regularization/gaussian_noise.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/utils/torch_utils.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./examples/keras_io/timeseries/timeseries_classification_from_scratch.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/rnn/conv_lstm3d.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/backend/jax/__init__.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./guides/understanding_masking_and_padding.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./guides/custom_train_step_in_tensorflow.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/backend/torch/optimizers/torch_adam.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/applications/__init__.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/regularization/__init__.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/backend/common/stateless_scope_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/constraints/__init__.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/preprocessing/random_contrast_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/backend/tensorflow/optimizer_sparse_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/utils/sequence_utils_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/metrics/hinge_metrics_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/backend/torch/optimizers/torch_adadelta.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/reshaping/reshape.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/core/input_layer_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./examples/demo_custom_torch_workflow.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/applications/vgg16.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/metrics/hinge_metrics.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/backend/torch/rnn.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/saving/__init__.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./examples/demo_functional.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/trainers/data_adapters/py_dataset_adapter.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/backend/tensorflow/image.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/normalization/group_normalization_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/preprocessing/category_encoding_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./examples/keras_io/pytorch/torchvision_keras.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/activations/relu_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/reshaping/zero_padding3d_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/callbacks/model_checkpoint.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./guides/writing_a_custom_training_loop_in_torch.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/backend/tensorflow/optimizer.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/preprocessing/hashing.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/preprocessing/normalization_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/merging/add.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/normalization/batch_normalization_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/rnn/conv_lstm1d.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/merging/dot.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/rnn/conv_lstm_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/losses/losses_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/regularizers/__init__.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/convolutional/depthwise_conv2d.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/utils/tracking_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./benchmarks/torch_ctl_benchmark/README.md | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/backend/tensorflow/__init__.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./guides/training_with_built_in_methods.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/backend/torch/__init__.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./integration_tests/torch_workflow_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/datasets/cifar10.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./benchmarks/torch_ctl_benchmark/__init__.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/reshaping/up_sampling3d_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/preprocessing/random_flip_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/utils/text_dataset_utils.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/preprocessing/integer_lookup_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/backend/common/backend_utils.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/rnn/time_distributed.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./benchmarks/torch_ctl_benchmark/conv_model_benchmark.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/saving/object_registration.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/trainers/epoch_iterator_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./examples/keras_io/tensorflow/generative/dcgan_overriding_train_step.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./examples/keras_io/tensorflow/vision/siamese_network.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/optimizers/adam_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/preprocessing/random_translation_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/backend/tensorflow/distribute_test.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/optimizers/adagrad.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/layers/rnn/rnn.py | -1 | python |
keras-team/keras | 18,820 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes | james77777778 | b502889d8d20411eab2a84acf035d31b3e2ae517 | cbe397c0989eac38aa445d4c29e8469a07c694f9 | 2023-11-23 06:26:31+00:00 | 2023-11-23 21:54:21+00:00 | Improve dtype inference of `add`, `divide`, `maximum`, `minimum`, `multiply` and `subtract` when using python native dtypes. This PR has addressed the dtype inference for these ops with python `int` and `float` numbers.
The manually casting in the layer implementation from the previous PR can now be removed.
This should enhance UX when implementing new layers to support mixed precision training. | ./keras/utils/backend_utils.py | -1 | python |
keras-team/keras | 18,816 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like` | james77777778 | 47cee523c4d1a9a271db78f0f36e3f6fb33b818a | b502889d8d20411eab2a84acf035d31b3e2ae517 | 2023-11-23 02:24:01+00:00 | 2023-11-23 21:53:52+00:00 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like`. The dtype inference is complicated for `ops.power` so I left it as a todo item.
I might finish all ops for consistent dtype inference in 1 or 2 PR.
Currently, the status of ops applied `backend.result_type` is as follows (A-S):
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [ ] cumprod (left for #18734 #18813)
- [ ] cumsum (left for #18734 #18813)
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [ ] nonzero (TODO item)
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [ ] power (TODO item)
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [ ] take
- [ ] take_along_axis
- [x] tan
- [x] tanh
- [ ] tensordot
- [ ] tile
- [ ] trace
- [ ] transpose
- [x] tri
- [ ] tril
- [ ] triu
- [ ] true_divide
- [ ] vdot
- [ ] vstack
- [ ] where
- [x] zeros
- [x] zeros_like
</details>
| ./keras/ops/numpy_test.py | 1 | python |
keras-team/keras | 18,816 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like` | james77777778 | 47cee523c4d1a9a271db78f0f36e3f6fb33b818a | b502889d8d20411eab2a84acf035d31b3e2ae517 | 2023-11-23 02:24:01+00:00 | 2023-11-23 21:53:52+00:00 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like`. The dtype inference is complicated for `ops.power` so I left it as a todo item.
I might finish all ops for consistent dtype inference in 1 or 2 PR.
Currently, the status of ops applied `backend.result_type` is as follows (A-S):
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [ ] cumprod (left for #18734 #18813)
- [ ] cumsum (left for #18734 #18813)
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [ ] nonzero (TODO item)
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [ ] power (TODO item)
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [ ] take
- [ ] take_along_axis
- [x] tan
- [x] tanh
- [ ] tensordot
- [ ] tile
- [ ] trace
- [ ] transpose
- [x] tri
- [ ] tril
- [ ] triu
- [ ] true_divide
- [ ] vdot
- [ ] vstack
- [ ] where
- [x] zeros
- [x] zeros_like
</details>
| ./keras/ops/numpy.py | 1 | python |
keras-team/keras | 18,816 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like` | james77777778 | 47cee523c4d1a9a271db78f0f36e3f6fb33b818a | b502889d8d20411eab2a84acf035d31b3e2ae517 | 2023-11-23 02:24:01+00:00 | 2023-11-23 21:53:52+00:00 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like`. The dtype inference is complicated for `ops.power` so I left it as a todo item.
I might finish all ops for consistent dtype inference in 1 or 2 PR.
Currently, the status of ops applied `backend.result_type` is as follows (A-S):
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [ ] cumprod (left for #18734 #18813)
- [ ] cumsum (left for #18734 #18813)
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [ ] nonzero (TODO item)
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [ ] power (TODO item)
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [ ] take
- [ ] take_along_axis
- [x] tan
- [x] tanh
- [ ] tensordot
- [ ] tile
- [ ] trace
- [ ] transpose
- [x] tri
- [ ] tril
- [ ] triu
- [ ] true_divide
- [ ] vdot
- [ ] vstack
- [ ] where
- [x] zeros
- [x] zeros_like
</details>
| ./keras/backend/tensorflow/numpy.py | 1 | python |
keras-team/keras | 18,816 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like` | james77777778 | 47cee523c4d1a9a271db78f0f36e3f6fb33b818a | b502889d8d20411eab2a84acf035d31b3e2ae517 | 2023-11-23 02:24:01+00:00 | 2023-11-23 21:53:52+00:00 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like`. The dtype inference is complicated for `ops.power` so I left it as a todo item.
I might finish all ops for consistent dtype inference in 1 or 2 PR.
Currently, the status of ops applied `backend.result_type` is as follows (A-S):
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [ ] cumprod (left for #18734 #18813)
- [ ] cumsum (left for #18734 #18813)
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [ ] nonzero (TODO item)
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [ ] power (TODO item)
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [ ] take
- [ ] take_along_axis
- [x] tan
- [x] tanh
- [ ] tensordot
- [ ] tile
- [ ] trace
- [ ] transpose
- [x] tri
- [ ] tril
- [ ] triu
- [ ] true_divide
- [ ] vdot
- [ ] vstack
- [ ] where
- [x] zeros
- [x] zeros_like
</details>
| ./keras/backend/numpy/numpy.py | 1 | python |
keras-team/keras | 18,816 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like` | james77777778 | 47cee523c4d1a9a271db78f0f36e3f6fb33b818a | b502889d8d20411eab2a84acf035d31b3e2ae517 | 2023-11-23 02:24:01+00:00 | 2023-11-23 21:53:52+00:00 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like`. The dtype inference is complicated for `ops.power` so I left it as a todo item.
I might finish all ops for consistent dtype inference in 1 or 2 PR.
Currently, the status of ops applied `backend.result_type` is as follows (A-S):
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [ ] cumprod (left for #18734 #18813)
- [ ] cumsum (left for #18734 #18813)
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [ ] nonzero (TODO item)
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [ ] power (TODO item)
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [ ] take
- [ ] take_along_axis
- [x] tan
- [x] tanh
- [ ] tensordot
- [ ] tile
- [ ] trace
- [ ] transpose
- [x] tri
- [ ] tril
- [ ] triu
- [ ] true_divide
- [ ] vdot
- [ ] vstack
- [ ] where
- [x] zeros
- [x] zeros_like
</details>
| ./keras/backend/jax/numpy.py | 1 | python |
keras-team/keras | 18,816 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like` | james77777778 | 47cee523c4d1a9a271db78f0f36e3f6fb33b818a | b502889d8d20411eab2a84acf035d31b3e2ae517 | 2023-11-23 02:24:01+00:00 | 2023-11-23 21:53:52+00:00 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like`. The dtype inference is complicated for `ops.power` so I left it as a todo item.
I might finish all ops for consistent dtype inference in 1 or 2 PR.
Currently, the status of ops applied `backend.result_type` is as follows (A-S):
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [ ] cumprod (left for #18734 #18813)
- [ ] cumsum (left for #18734 #18813)
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [ ] nonzero (TODO item)
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [ ] power (TODO item)
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [ ] take
- [ ] take_along_axis
- [x] tan
- [x] tanh
- [ ] tensordot
- [ ] tile
- [ ] trace
- [ ] transpose
- [x] tri
- [ ] tril
- [ ] triu
- [ ] true_divide
- [ ] vdot
- [ ] vstack
- [ ] where
- [x] zeros
- [x] zeros_like
</details>
| ./keras/backend/torch/numpy.py | 1 | python |
keras-team/keras | 18,816 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like` | james77777778 | 47cee523c4d1a9a271db78f0f36e3f6fb33b818a | b502889d8d20411eab2a84acf035d31b3e2ae517 | 2023-11-23 02:24:01+00:00 | 2023-11-23 21:53:52+00:00 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like`. The dtype inference is complicated for `ops.power` so I left it as a todo item.
I might finish all ops for consistent dtype inference in 1 or 2 PR.
Currently, the status of ops applied `backend.result_type` is as follows (A-S):
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [ ] cumprod (left for #18734 #18813)
- [ ] cumsum (left for #18734 #18813)
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [ ] nonzero (TODO item)
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [ ] power (TODO item)
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [ ] take
- [ ] take_along_axis
- [x] tan
- [x] tanh
- [ ] tensordot
- [ ] tile
- [ ] trace
- [ ] transpose
- [x] tri
- [ ] tril
- [ ] triu
- [ ] true_divide
- [ ] vdot
- [ ] vstack
- [ ] where
- [x] zeros
- [x] zeros_like
</details>
| ./keras/layers/pooling/global_max_pooling3d.py | -1 | python |
keras-team/keras | 18,816 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like` | james77777778 | 47cee523c4d1a9a271db78f0f36e3f6fb33b818a | b502889d8d20411eab2a84acf035d31b3e2ae517 | 2023-11-23 02:24:01+00:00 | 2023-11-23 21:53:52+00:00 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like`. The dtype inference is complicated for `ops.power` so I left it as a todo item.
I might finish all ops for consistent dtype inference in 1 or 2 PR.
Currently, the status of ops applied `backend.result_type` is as follows (A-S):
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [ ] cumprod (left for #18734 #18813)
- [ ] cumsum (left for #18734 #18813)
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [ ] nonzero (TODO item)
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [ ] power (TODO item)
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [ ] take
- [ ] take_along_axis
- [x] tan
- [x] tanh
- [ ] tensordot
- [ ] tile
- [ ] trace
- [ ] transpose
- [x] tri
- [ ] tril
- [ ] triu
- [ ] true_divide
- [ ] vdot
- [ ] vstack
- [ ] where
- [x] zeros
- [x] zeros_like
</details>
| ./integration_tests/model_visualization_test.py | -1 | python |
keras-team/keras | 18,816 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like` | james77777778 | 47cee523c4d1a9a271db78f0f36e3f6fb33b818a | b502889d8d20411eab2a84acf035d31b3e2ae517 | 2023-11-23 02:24:01+00:00 | 2023-11-23 21:53:52+00:00 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like`. The dtype inference is complicated for `ops.power` so I left it as a todo item.
I might finish all ops for consistent dtype inference in 1 or 2 PR.
Currently, the status of ops applied `backend.result_type` is as follows (A-S):
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [ ] cumprod (left for #18734 #18813)
- [ ] cumsum (left for #18734 #18813)
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [ ] nonzero (TODO item)
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [ ] power (TODO item)
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [ ] take
- [ ] take_along_axis
- [x] tan
- [x] tanh
- [ ] tensordot
- [ ] tile
- [ ] trace
- [ ] transpose
- [x] tri
- [ ] tril
- [ ] triu
- [ ] true_divide
- [ ] vdot
- [ ] vstack
- [ ] where
- [x] zeros
- [x] zeros_like
</details>
| ./keras/optimizers/adadelta_test.py | -1 | python |
keras-team/keras | 18,816 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like` | james77777778 | 47cee523c4d1a9a271db78f0f36e3f6fb33b818a | b502889d8d20411eab2a84acf035d31b3e2ae517 | 2023-11-23 02:24:01+00:00 | 2023-11-23 21:53:52+00:00 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like`. The dtype inference is complicated for `ops.power` so I left it as a todo item.
I might finish all ops for consistent dtype inference in 1 or 2 PR.
Currently, the status of ops applied `backend.result_type` is as follows (A-S):
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [ ] cumprod (left for #18734 #18813)
- [ ] cumsum (left for #18734 #18813)
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [ ] nonzero (TODO item)
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [ ] power (TODO item)
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [ ] take
- [ ] take_along_axis
- [x] tan
- [x] tanh
- [ ] tensordot
- [ ] tile
- [ ] trace
- [ ] transpose
- [x] tri
- [ ] tril
- [ ] triu
- [ ] true_divide
- [ ] vdot
- [ ] vstack
- [ ] where
- [x] zeros
- [x] zeros_like
</details>
| ./guides/writing_a_custom_training_loop_in_tensorflow.py | -1 | python |
keras-team/keras | 18,816 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like` | james77777778 | 47cee523c4d1a9a271db78f0f36e3f6fb33b818a | b502889d8d20411eab2a84acf035d31b3e2ae517 | 2023-11-23 02:24:01+00:00 | 2023-11-23 21:53:52+00:00 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like`. The dtype inference is complicated for `ops.power` so I left it as a todo item.
I might finish all ops for consistent dtype inference in 1 or 2 PR.
Currently, the status of ops applied `backend.result_type` is as follows (A-S):
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [ ] cumprod (left for #18734 #18813)
- [ ] cumsum (left for #18734 #18813)
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [ ] nonzero (TODO item)
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [ ] power (TODO item)
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [ ] take
- [ ] take_along_axis
- [x] tan
- [x] tanh
- [ ] tensordot
- [ ] tile
- [ ] trace
- [ ] transpose
- [x] tri
- [ ] tril
- [ ] triu
- [ ] true_divide
- [ ] vdot
- [ ] vstack
- [ ] where
- [x] zeros
- [x] zeros_like
</details>
| ./keras/legacy/preprocessing/image.py | -1 | python |
keras-team/keras | 18,816 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like` | james77777778 | 47cee523c4d1a9a271db78f0f36e3f6fb33b818a | b502889d8d20411eab2a84acf035d31b3e2ae517 | 2023-11-23 02:24:01+00:00 | 2023-11-23 21:53:52+00:00 | Apply `backend.result_type` to `ones_like`, `outer`, `pad`, `prod`, `ravel`, `repeat`, `reshape`, `roll`, `round`, `sign`, `sort`, `split`, `square`, `squeeze`, `stack`, `std`, `swapaxes` and `zeros_like`. The dtype inference is complicated for `ops.power` so I left it as a todo item.
I might finish all ops for consistent dtype inference in 1 or 2 PR.
Currently, the status of ops applied `backend.result_type` is as follows (A-S):
<details>
- [x] abs
- [x] absolute
- [x] add
- [x] all
- [x] amax
- [x] amin
- [x] append
- [x] arange
- [x] arccos
- [x] arccosh
- [x] arcsin
- [x] arcsinh
- [x] arctan
- [x] arctan2
- [x] arctanh
- [x] argmax
- [x] argmin
- [x] argsort
- [x] array
- [x] average
- [x] bincount
- [x] broadcast_to
- [x] ceil
- [x] clip
- [x] concatenate
- [ ] conj (Keras does not directly support complex data)
- [ ] conjugate (Keras does not directly support complex data)
- [x] copy
- [x] cos
- [x] cosh
- [x] count_nonzero
- [x] cross
- [ ] cumprod (left for #18734 #18813)
- [ ] cumsum (left for #18734 #18813)
- [x] diag
- [x] diagonal
- [x] diff
- [x] digitize
- [x] divide
- [x] dot
- [x] einsum
- [x] empty
- [x] equal
- [x] exp
- [x] expand_dims
- [x] expm1
- [x] eye
- [x] flip
- [x] floor
- [x] full
- [x] full_like
- [x] greater
- [x] greater_equal
- [x] hstack
- [x] identity
- [ ] imag (Keras does not directly support complex data)
- [ ] interp (Keras lacks this op)
- [x] isclose
- [x] isfinite
- [x] isinf
- [x] isnan
- [x] less
- [x] less_equal
- [x] linspace
- [x] log
- [x] log10
- [x] log1p
- [x] log2
- [x] logaddexp
- [x] logical_and
- [x] logical_not
- [x] logical_or
- [x] logspace
- [x] matmul
- [x] max
- [x] maximum
- [x] mean
- [x] median
- [x] meshgrid
- [ ] mgrid (Keras lacks this op)
- [x] min
- [x] minimum
- [x] mod
- [x] moveaxis
- [x] multiply
- [x] nan_to_num
- [ ] ndim
- [ ] nonzero (TODO item)
- [x] not_equal
- [x] ones
- [x] ones_like
- [x] outer
- [x] pad
- [ ] percentile (Keras lacks this op)
- [ ] power (TODO item)
- [x] prod
- [x] quantile
- [x] ravel
- [ ] real (Keras does not directly support complex data)
- [ ] reciprocal (Keras lacks this op)
- [x] repeat
- [x] reshape
- [x] roll
- [x] round
- [x] sign
- [x] sin
- [x] sinh
- [ ] size
- [x] sort
- [x] split
- [x] sqrt
- [x] square
- [x] squeeze
- [x] stack
- [x] std
- [x] subtract
- [x] sum
- [x] swapaxes
- [ ] take
- [ ] take_along_axis
- [x] tan
- [x] tanh
- [ ] tensordot
- [ ] tile
- [ ] trace
- [ ] transpose
- [x] tri
- [ ] tril
- [ ] triu
- [ ] true_divide
- [ ] vdot
- [ ] vstack
- [ ] where
- [x] zeros
- [x] zeros_like
</details>
| ./keras/layers/rnn/rnn_test.py | -1 | python |