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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>
| ./examples/demo_torch_multi_gpu.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>
| ./examples/keras_io/tensorflow/structured_data/structured_data_classification_with_feature_space.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/metrics/metrics_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/layers/rnn/conv_lstm.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>
| ./examples/keras_io/tensorflow/vision/mirnet.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>
| ./examples/keras_io/vision/knowledge_distillation.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/normalization/layer_normalization_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/layers/reshaping/cropping2d_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/backend/jax/trainer.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/metrics/__init__.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/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>
| ./examples/keras_io/vision/token_learner.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>
| ./examples/keras_io/vision/oxford_pets_image_segmentation.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/distribution_lib.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>
| ./examples/keras_io/tensorflow/generative/wgan_gp.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/metrics/f_score_metrics.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/distribution_lib.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>
| ./examples/keras_io/tensorflow/generative/dcgan_overriding_train_step.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/convolutional/separable_conv_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/layers/preprocessing/index_lookup.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/api_export.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/merging/base_merge.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/preprocessing/normalization_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>
| ./examples/keras_io/tensorflow/keras_recipes/tensorflow_numpy_models.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/function.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/adam.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>
| ./examples/keras_io/tensorflow/audio/uk_ireland_accent_recognition.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>
| ./examples/keras_io/tensorflow/vision/bit.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/loss_scale_optimizer.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>
| ./examples/keras_io/vision/eanet.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>
| ./examples/keras_io/vision/gradient_centralization.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/applications/nasnet.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/adam_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/image_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/adamw_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/layers/core/identity.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/datasets/boston_housing.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/models/sequential_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/distribution/distribution_lib_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/backend/common/stateless_scope.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/utils/numerical_utils_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/layers/preprocessing/center_crop_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/layers/preprocessing/text_vectorization.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/activations/elu_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/applications/mobilenet_v2.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/common/backend_utils_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/backend/common/dtypes.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/initializers/constant_initializers.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/datasets/cifar10.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>
| ./examples/keras_io/tensorflow/vision/perceiver_image_classification.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/core/lambda_layer_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/layers/preprocessing/string_lookup_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/applications/efficientnet_v2.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/saving/object_registration.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/ops/numpy_test.py | 1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/ops/numpy.py | 1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/backend/tensorflow/numpy.py | 1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/backend/numpy/numpy.py | 1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/backend/jax/numpy.py | 1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/backend/torch/numpy.py | 1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/trainers/data_adapters/tf_dataset_adapter_test.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./examples/keras_io/tensorflow/vision/captcha_ocr.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/callbacks/lambda_callback.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./benchmarks/model_benchmark/benchmark_utils.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/layers/normalization/group_normalization_test.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/applications/inception_v3.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/backend/common/name_scope.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/metrics/hinge_metrics_test.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/backend/jax/random.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/layers/core/wrapper_test.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./examples/keras_io/tensorflow/generative/deep_dream.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/optimizers/lion_test.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/ops/nn_test.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/callbacks/callback_list.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/callbacks/backup_and_restore_callback_test.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./examples/keras_io/tensorflow/vision/cutmix.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/layers/rnn/stacked_rnn_cells_test.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/trainers/data_adapters/py_dataset_adapter.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/utils/python_utils.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/layers/core/einsum_dense_test.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/utils/numerical_utils.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/backend/torch/optimizers/torch_adamw.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/ops/operation.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/ops/operation_utils_test.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/layers/preprocessing/integer_lookup_test.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/initializers/constant_initializers.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./examples/keras_io/nlp/addition_rnn.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/layers/convolutional/base_separable_conv.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/layers/activations/relu_test.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/layers/pooling/global_average_pooling_test.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/optimizers/ftrl_test.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/layers/preprocessing/feature_space.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/backend/jax/image.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/backend/tensorflow/nn.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/metrics/iou_metrics.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/layers/merging/maximum.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/backend/tensorflow/sparse.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/backend/tensorflow/layer.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./keras/backend/torch/trainer.py | -1 | python |
keras-team/keras | 18,815 | Added constant_values support to pad | jackd | 03f4a4fe3c0b2be47681e98275c11cd4bc786c8f | b4c3a0e163603855f03316b0b97f2c9c25e133eb | 2023-11-23 01:24:08+00:00 | 2023-11-24 18:00:28+00:00 | Added constant_values support to pad. Added `constant_values` kwarg to `ops.pad`. This argument is ignored if `mode != 'constant'`, which is different to `numpy` which raises. | ./examples/keras_io/vision/keypoint_detection.py | -1 | python |