Operaciones de TensorFlow disponibles
En esta página, se describen las API de Python para TensorFlow y los operadores de grafos disponibles en Cloud TPU.
API de Python disponibles
La siguiente lista es una guía para el conjunto de las API de TensorFlow disponibles. Esta lista no es exhaustiva. Las funciones de biblioteca que no aparecen en esta lista pueden funcionar si constan de las funciones básicas disponibles.
Consulta la guía de rendimiento para obtener recomendaciones sobre los operadores específicos.
Módulo | API de Python disponible | Comentarios |
---|---|---|
tf |
tf.abs |
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tf.acosh |
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tf.add |
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tf.add_n |
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tf.angle |
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tf.arg_max |
El argumento dimension debe ser una constante de tiempo de compilación. |
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tf.arg_min |
El argumento dimension debe ser una constante de tiempo de compilación. |
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tf.asinh |
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tf.assign |
Disponible solo para la variable de recurso. | |
tf.assign_add |
Disponible solo para la variable de recurso. | |
tf.assign_sub |
Disponible solo para la variable de recurso. | |
tf.atan |
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tf.atan2 |
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tf.atanh |
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tf.batch_to_space |
Los argumentos crops y block_shape deben ser una constante de tiempo de compilación. |
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tf.batch_to_space_nd |
El argumento crops debe ser una constante de tiempo de compilación. |
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tf.broadcast_dynamic_shape |
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tf.broadcast_static_shape |
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tf.case |
Experimental (flujo de control). Es posible que aún no funcione de manera confiable. | |
tf.cast |
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tf.ceil |
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tf.cholesky |
Experimental. Puede tener problemas con la precisión numérica. | |
tf.cholesky_solve |
Experimental. Puede tener problemas con la precisión numérica. | |
tf.clip_by_average_norm |
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tf.clip_by_global_norm |
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tf.clip_by_norm |
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tf.clip_by_value |
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tf.complex |
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tf.concat |
concat_dim debe ser una constante de tiempo de compilación. |
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tf.cond |
Experimental (flujo de control). Es posible que aún no funcione de manera confiable. | |
tf.conj |
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tf.constant |
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tf.convert_to_tensor |
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tf.cos |
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tf.cosh |
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tf.cross |
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tf.cumprod |
axis debe ser una constante de tiempo de compilación. |
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tf.cumsum |
axis debe ser una constante de tiempo de compilación. |
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tf.depth_to_space |
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tf.diag |
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tf.diag_part |
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tf.div |
La división int32 es más lenta que otros tipos. |
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tf.divide |
La división int32 es más lenta que otros tipos. |
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tf.dynamic_stitch |
indices debe ser una constante de tiempo de compilación. |
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tf.einsum |
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tf.equal |
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tf.erf |
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tf.erfc |
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tf.exp |
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tf.expand_dims |
dims debe ser una constante de tiempo de compilación. |
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tf.expm1 |
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tf.extract_image_patches |
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tf.eye |
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tf.fake_quant_with_min_max_args |
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tf.fake_quant_with_min_max_args_gradient |
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tf.fake_quant_with_min_max_vars |
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tf.fake_quant_with_min_max_vars_gradient |
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tf.fft |
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tf.fft2d |
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tf.fft3d |
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tf.fill |
El argumento dims debe ser una constante de tiempo de compilación. |
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tf.floor |
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tf.floordiv |
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tf.floormod |
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tf.foldl |
Experimental (flujo de control). | |
tf.foldr |
Experimental (flujo de control). | |
tf.gather |
axis debe ser una constante de tiempo de compilación. |
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tf.gather_nd |
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tf.greater |
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tf.greater_equal |
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tf.hessians |
Experimental (flujo de control). | |
tf.identity |
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tf.identity_n |
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tf.ifft |
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tf.ifft2d |
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tf.ifft3d |
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tf.imag |
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tf.invert_permutation |
El argumento x debe ser una constante de tiempo de compilación. |
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tf.is_finite |
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tf.is_inf |
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tf.is_nan |
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tf.is_non_decreasing |
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tf.is_strictly_increasing |
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tf.less |
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tf.less_equal |
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tf.linspace |
Los argumentos start , stop y num deben ser constantes de tiempo de compilación. |
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tf.log |
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tf.log1p |
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tf.log_sigmoid |
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tf.logical_and |
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tf.logical_or |
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tf.logical_not |
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tf.logical_xor |
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tf.matmul |
Usa un matmul bfloat16 con acumulación float32 . |
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tf.matrix_band_part |
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tf.matrix_diag |
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tf.matrix_diag_part |
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tf.matrix_set_diag |
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tf.matrix_triangular_solve |
Experimental. Puede tener problemas con la precisión numérica. | |
tf.maximum |
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tf.meshgrid |
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tf.minimum |
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tf.mod |
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tf.multinomial |
El argumento num_samples debe ser una constante de tiempo de compilación. |
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tf.multiply |
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tf.negative |
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tf.no_op |
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tf.norm |
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tf.not_equal |
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tf.one_hot |
depth debe ser una constante de tiempo de compilación. |
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tf.ones |
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tf.ones_like |
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tf.pad |
El argumento paddings debe ser una constante de tiempo de compilación. La gradiente de relleno REFLECT aún no está disponible. |
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tf.pow |
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tf.random_normal |
shape debe ser una constante de tiempo de compilación. |
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tf.random_uniform |
shape debe ser una constante de tiempo de compilación. |
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tf.range |
Los argumentos start , limit y delta deben ser constantes de tiempo de compilación. |
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tf.rank |
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tf.real |
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tf.realdiv |
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tf.reciprocal |
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tf.reduce_all |
axis debe ser una constante de tiempo de compilación. |
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tf.reduce_any |
axis debe ser una constante de tiempo de compilación. |
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tf.reduce_logsumexp |
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tf.reduce_max |
axis debe ser una constante de tiempo de compilación. |
|
tf.reduce_min |
axis debe ser una constante de tiempo de compilación. |
|
tf.reduce_prod |
axis debe ser una constante de tiempo de compilación. |
|
tf.reduce_sum |
axis debe ser una constante de tiempo de compilación. |
|
tf.reshape |
El argumento shape debe ser una constante de tiempo de compilación. |
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tf.reverse |
El argumento dims debe ser una constante de tiempo de compilación. |
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tf.reverse_sequence |
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tf.reverse_v2 |
El argumento axis debe ser una constante de tiempo de compilación. |
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tf.rint |
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tf.round |
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tf.rsqrt |
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tf.saturate_cast |
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tf.scalar_mul |
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tf.scan |
Experimental (flujo de control). | |
tf.scatter_nd |
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tf.sequence_mask |
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tf.shape |
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tf.shape_n |
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tf.sigmoid |
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tf.sign |
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tf.sin |
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tf.sinh |
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tf.size |
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tf.slice |
size debe ser una constante de tiempo de compilación. Además, begin debe ser una constante de tiempo de compilación, o bien size no debe ser negativo. La propagación inversa solo se admite si begin y size son constantes de tiempo de compilación. |
|
tf.space_to_batch |
paddings y block_shape deben ser constantes de tiempo de compilación. |
|
tf.space_to_batch_nd |
paddings debe ser una constante de tiempo de compilación. |
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tf.space_to_depth |
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tf.split |
axis debe ser una constante de tiempo de compilación. |
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tf.sqrt |
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tf.square |
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tf.squared_difference |
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tf.squeeze |
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tf.stack |
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tf.stop_gradient |
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tf.strided_slice |
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tf.tan |
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tf.tanh |
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tf.tensordot |
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tf.tile |
El argumento multiples debe ser una constante de tiempo de compilación. |
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tf.to_bfloat16 |
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tf.to_float |
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tf.to_int32 |
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tf.to_int64 |
La compatibilidad de int64 es limitada. |
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tf.trace |
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tf.transpose |
El argumento perm debe ser una constante de tiempo de compilación. |
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tf.truediv |
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tf.truncated_normal |
shape debe ser una constante de tiempo de compilación. |
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tf.truncatediv |
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tf.truncatemod |
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tf.unsorted_segment_sum |
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tf.unstack |
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tf.where |
Tanto x como y no pueden ser None . Si tanto x como y son None , el operador no tendría una forma estática. |
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tf.while_loop |
Para calcular el gradiente de un bucle mientras, se requiere el argumento maximum_iterations . |
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tf.zeros |
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tf.zeros_like |
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tf.Tensor.__getitem__ |
El inicio, el final y las segmentaciones de un segmento deben ser constantes de tiempo de compilación. | |
tf.bitwise |
tf.bitwise_and |
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tf.bitwise_or |
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tf.bitwise_invert |
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tf.contrib.stateless |
tf.contrib.stateless.stateless_random_normal |
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tf.contrib.stateless.stateless_random_uniform |
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tf.image |
tf.image.adjust_brightness |
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tf.image.adjust_contrast |
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tf.image.adjust_gamma |
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tf.image.adjust_hue |
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tf.image.adjust_saturation |
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tf.image.central_crop |
El factor de recorte debe ser una constante de tiempo de compilación. | |
tf.image.convert_image_dtype |
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tf.image.flip_left_right |
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tf.image.flip_up_down |
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tf.image.grayscale_to_rgb |
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tf.image.hsv_to_rgb |
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tf.image.resize_bilinear |
Solo align_corners=True está disponible. size debe ser una constante de tiempo de compilación. |
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tf.image.random_brightness |
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tf.image.random_contrast |
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tf.image.random_flip_left_right |
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tf.image.random_flip_up_down |
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tf.image.random_hue |
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tf.image.random_saturation |
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tf.image.rgb_to_hsv |
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tf.image.rgb_to_grayscale |
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tf.image.rot90 |
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tf.image.total_variation |
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tf.image.transpose_image |
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tf.layers |
tf.layers.average_pooling1d |
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tf.layers.average_pooling2d |
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tf.layers.average_pooling1d |
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tf.layers.batch_normalization |
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tf.layers.conv1d |
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tf.layers.conv2d |
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tf.layers.conv2d_transpose |
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tf.layers.conv3d |
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tf.layers.conv3d_transpose |
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tf.layers.dense |
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tf.layers.dropout |
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tf.layers.flatten |
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tf.layers.max_pooling1d |
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tf.layers.max_pooling2d |
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tf.layers.max_pooling3d |
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tf.layers.separable_conv2d |
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tf.nn |
tf.nn.atrous_conv2d |
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tf.nn.atrous_conv2d_transpose |
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tf.nn.avg_pool |
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tf.nn.avg_pool3d |
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tf.nn.batch_normalization |
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tf.nn.bias_add |
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tf.nn.conv1d |
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tf.nn.conv2d |
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tf.nn.conv2d_backprop_filter |
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tf.nn.conv2d_backprop_input |
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tf.nn.conv2d_transpose |
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tf.nn.conv3d |
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tf.nn.conv3d_backprop_filter |
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tf.nn.conv3d_backprop_input |
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tf.nn.conv3d_transpose |
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tf.nn.convolution |
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tf.nn.crelu |
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tf.nn.depthwise_conv2d |
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tf.nn.depthwise_conv2d_native |
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tf.nn.depthwise_conv2d_native_backprop_filter |
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tf.nn.depthwise_conv2d_native_backprop_input |
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tf.nn.dropout |
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tf.nn.dynamic_rnn |
Experimental. | |
tf.nn.elu |
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tf.nn.fused_batch_norm |
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tf.nn.l2_loss |
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tf.nn.l2_normalize |
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tf.nn.leaky_relu |
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tf.nn.local_response_normalization |
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tf.nn.log_poisson_loss |
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tf.nn.log_softmax |
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tf.nn.max_pool |
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tf.nn.max_pool3d |
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tf.nn.moments |
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tf.nn.normalize_moments |
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tf.nn.pool |
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tf.nn.relu |
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tf.nn.relu6 |
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tf.nn.relu_layer |
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tf.nn.selu |
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tf.nn.separable_conv2d |
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tf.nn.sigmoid_cross_entropy_with_logits |
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tf.nn.softmax |
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tf.nn.softmax_cross_entropy_with_logits |
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tf.nn.softplus |
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tf.nn.softsign |
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tf.nn.sparse_softmax_cross_entropy_with_logits |
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tf.nn.static_bidirectional_rnn |
Experimental. | |
tf.nn.static_rnn |
Experimental. | |
tf.nn.weighted_cross_entropy_with_logits |
Experimental. | |
tf.nn.weighted_moments |
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tf.nn.with_space_to_batch |
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tf.nn.xw_plus_b |
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tf.nn.zero_fraction |
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tf.spectral |
tf.spectral.fft |
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tf.spectral.fft2d |
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tf.spectral.fft3d |
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tf.spectral.ifft |
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tf.spectral.ifft2d |
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tf.spectral.ifft3d |
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tf.spectral.irfft |
fft_length debe ser una constante de tiempo de compilación. |
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tf.spectral.irfft2d |
fft_length debe ser una constante de tiempo de compilación. |
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tf.spectral.irfft3d |
fft_length debe ser una constante de tiempo de compilación. |
|
tf.spectral.rfft |
fft_length debe ser una constante de tiempo de compilación. |
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tf.spectral.rfft2d |
fft_length debe ser una constante de tiempo de compilación. |
|
tf.spectral.rfft3d |
fft_length debe ser una constante de tiempo de compilación. |
API de Python no disponibles
Esta lista no es exhaustiva. A continuación, se muestran las opciones que no están disponibles en Cloud TPU:
Módulo | API de Python no disponible | Comentarios |
---|---|---|
tf |
tf.accumulate_n |
Usa las variables de referencia. |
tf.acos |
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tf.asin |
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tf.betainc |
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tf.bitcast |
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tf.add_check_numerics_ops |
Los programas que contienen operadores numéricos de verificación se deben ejecutar; sin embargo, este operador se ignora actualmente. | |
tf.assert_... |
Los programas que contienen confirmaciones se deben ejecutar; sin embargo, las confirmaciones se ignoran. | |
tf.check_numerics |
Los programas que contienen operadores numéricos de verificación se deben ejecutar; sin embargo, este operador se ignora actualmente. | |
tf.confusion_matrix |
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tf.count_nonzero |
Usa la reducción de int64 . |
|
tf.count_up_to |
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tf.create_partitioned_variables |
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tf.dequantize |
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tf.digamma |
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tf.dynamic_partition |
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tf.edit_distance |
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tf.fake_quant_with_min_max_vars_per_channel |
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tf.fake_quant_with_min_max_vars_per_channel_gradient |
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tf.histogram_fixed_width |
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tf.igamma |
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tf.igammac |
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tf.lbeta |
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tf.lgamma |
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tf.matrix_determinant |
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tf.matrix_inverse |
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tf.matrix_solve |
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tf.matrix_solve_ls |
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tf.polygamma |
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tf.py_func |
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tf.qr |
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tf.quantize_v2 |
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tf.quantized_concat |
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tf.random_crop |
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tf.random_gamma |
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tf.random_poisson |
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tf.random_shuffle |
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tf.scatter_add |
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tf.scatter_div |
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tf.scatter_mul |
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tf.scatter_nd_add |
||
tf.scatter_nd_sub |
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tf.scatter_nd_update |
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tf.segment_mean |
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tf.segment_max |
||
tf.segment_min |
||
tf.segment_prod |
||
tf.segment_sum |
||
tf.self_adjoint_eig |
||
tf.self_adjoint_eigvals |
||
tf.setdiff1d |
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tf.sparse_... |
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tf.string_... |
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tf.substr |
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tf.svd |
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tf.to_double |
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tf.unique |
||
tf.unsorted_segment_max |
||
tf.zeta |
||
tf.bitwise.bitwise_xor |
||
tf.contrib.stateless.stateless_truncated_normal |
Operadores de grafo disponibles
Operador | Restricción de tipo |
---|---|
Abs |
T={bfloat16,float,int32,int64} |
Acos |
T={bfloat16,complex64,float,int32,int64} |
Acosh |
T={bfloat16,complex64,float} |
Add |
T={bfloat16,complex64,float,int32,int64} |
AddN |
T={bfloat16,complex64,float,int32,int64,uint32,uint64} |
AdjustContrastv2 |
T={float} |
AdjustHue |
T={float} |
AdjustSaturation |
T={float} |
All |
Tidx={int32,int64} |
AllToAll |
T={bfloat16,float} |
Angle |
Tout={float} T={complex64} |
Any |
Tidx={int32,int64} |
ApproximateEqual |
T={bfloat16,complex64,float,int32,int64,uint32,uint64} |
ArgMax |
Tidx={int32,int64} output_type={int32,int64} T={bfloat16,complex64,float,int32,int64,uint32,uint64} |
ArgMin |
Tidx={int32,int64} output_type={int32,int64} T={bfloat16,complex64,float,int32,int64,uint32,uint64} |
Asin |
T={bfloat16,complex64,float,int32,int64} |
Asinh |
T={bfloat16,complex64,float} |
Assert |
T={bfloat16,bool,complex64,float,int32,int64,string,uint32,uint64} |
AssignAddVariableOp |
dtype={bfloat16,complex64,float,int32,int64,uint32,uint64} |
AssignSubVariableOp |
dtype={bfloat16,complex64,float,int32,int64,uint32,uint64} |
AssignVariableOp |
dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Atan |
T={bfloat16,complex64,float,int32,int64} |
Atan2 |
T={bfloat16,float} |
Atanh |
T={bfloat16,complex64,float} |
AvgPool |
T={bfloat16,float} |
AvgPool3D |
T={bfloat16,float} |
AvgPool3DGrad |
T={bfloat16,float} |
AvgPoolGrad |
T={bfloat16,float} |
BatchMatMul |
T={bfloat16,complex64,float,int32,int64} |
BatchToSpace |
Tidx={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
BatchToSpaceND |
Tcrops={int32,int64} Tblock_shape={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
BiasAdd |
T={bfloat16,complex64,float,int32,int64,uint32,uint64} |
BiasAddGrad |
T={bfloat16,complex64,float,int32,int64,uint32,uint64} |
BiasAddV1 |
T={bfloat16,complex64,float,int32,int64,uint32,uint64} |
Bitcast |
type={bfloat16,complex64,float,int32,int64,uint32,uint64} T={bfloat16,complex64,float,int32,int64,uint32,uint64} |
BitwiseAnd |
T={int32,int64,uint32,uint64} |
BitwiseOr |
T={int32,int64,uint32,uint64} |
BitwiseXor |
T={int32,int64,uint32,uint64} |
BroadcastArgs |
T={int32,int64} |
BroadcastGradientArgs |
T={int32,int64} |
BroadcastTo |
Tidx={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Bucketize |
T={float,int32,int64} |
Cast |
DstT={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} SrcT={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Ceil |
T={bfloat16,float} |
CheckNumerics |
T={bfloat16,float} |
Cholesky |
T={float} |
ClipByValue |
T={bfloat16,complex64,float,int32,int64,uint32,uint64} |
CollectivePermute |
T={bfloat16,float} |
Complex |
Tout={complex64} T={float} |
ComplexAbs |
Tout={float} T={complex64} |
Concat |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
ConcatOffset |
|
ConcatV2 |
Tidx={int32} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Conj |
T={complex64} |
ConjugateTranspose |
Tperm={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Const |
dtype={bfloat16,bool,complex64,float,int32,int64,string,uint32,uint64} |
ControlTrigger |
|
Conv2D |
T={bfloat16,float} |
Conv2DBackpropFilter |
T={bfloat16,float} |
Conv2DBackpropInput |
T={bfloat16,float} |
Conv3D |
T={bfloat16,float} |
Conv3DBackpropFilterV2 |
T={bfloat16,float} |
Conv3DBackpropInputV2 |
Tshape={int32,int64} T={bfloat16,float} |
Cos |
T={bfloat16,complex64,float} |
Cosh |
T={bfloat16,complex64,float} |
Cross |
T={bfloat16,float,int32,int64,uint32,uint64} |
CrossReplicaSum |
T={bfloat16,float} |
Cumprod |
Tidx={int32,int64} T={bfloat16,float,int32} |
Cumsum |
Tidx={int32,int64} T={bfloat16,float,int32} |
DataFormatVecPermute |
T={int32,int64} |
DepthToSpace |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
DepthwiseConv2dNative |
T={bfloat16,float} |
DepthwiseConv2dNativeBackpropFilter |
T={bfloat16,float} |
DepthwiseConv2dNativeBackpropInput |
T={bfloat16,float} |
Diag |
T={bfloat16,complex64,float,int32,int64} |
DiagPart |
T={bfloat16,complex64,float,int32,int64} |
Digamma |
T={bfloat16,float} |
Div |
T={bfloat16,complex64,float,int32,int64} |
DivNoNan |
T={float} |
DynamicStitch |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Elu |
T={bfloat16,float} |
EluGrad |
T={bfloat16,float} |
Empty |
dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
EmptyTensorList |
shape_type={int32,int64} element_dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Equal |
T={bfloat16,bool,complex64,float,int32,int64} |
Erf |
T={bfloat16,float} |
Erfc |
T={bfloat16,float} |
Exp |
T={bfloat16,complex64,float} |
ExpandDims |
Tdim={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Expm1 |
T={bfloat16,complex64,float} |
ExtractImagePatches |
T={bfloat16,float,int32,int64,uint32,uint64} |
FFT |
Tcomplex={complex64} |
FFT2D |
Tcomplex={complex64} |
FFT3D |
Tcomplex={complex64} |
FakeParam |
dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
FakeQuantWithMinMaxArgs |
|
FakeQuantWithMinMaxArgsGradient |
|
FakeQuantWithMinMaxVars |
|
FakeQuantWithMinMaxVarsGradient |
|
Fill |
index_type={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Floor |
T={bfloat16,float} |
FloorDiv |
T={bfloat16,complex64,float,int32,int64} |
FloorMod |
T={bfloat16,float,int32,int64} |
FusedBatchNorm |
T={float} |
FusedBatchNormGrad |
T={float} |
FusedBatchNormGradV2 |
U={float} T={bfloat16,float} |
FusedBatchNormV2 |
U={float} T={bfloat16,float} |
Gather |
Tindices={int32,int64} Tparams={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
GatherNd |
Tindices={int32,int64} Tparams={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
GatherV2 |
Taxis={int32,int64} Tindices={int32,int64} Tparams={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
GetItem |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Greater |
T={bfloat16,float,int32,int64,uint32,uint64} |
GreaterEqual |
T={bfloat16,float,int32,int64,uint32,uint64} |
HSVToRGB |
T={bfloat16,float} |
IFFT |
Tcomplex={complex64} |
IFFT2D |
Tcomplex={complex64} |
IFFT3D |
Tcomplex={complex64} |
IRFFT |
|
IRFFT2D |
|
IRFFT3D |
|
Identity |
T={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64} |
IdentityN |
T={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64} |
If |
Tout={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64} Tin={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64} Tcond={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64} |
Imag |
Tout={float} T={complex64} |
InfeedDequeue |
dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
InfeedDequeueTuple |
dtypes={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
InplaceAdd |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
InplaceUpdate |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Inv |
T={bfloat16,complex64,float,int32,int64} |
Invert |
T={int32,int64,uint32,uint64} |
InvertPermutation |
T={int32} |
IsFinite |
T={bfloat16,float} |
IsInf |
T={bfloat16,float} |
IsNan |
T={bfloat16,float} |
L2Loss |
T={bfloat16,float} |
LRN |
T={bfloat16,float} |
LRNGrad |
T={bfloat16,float} |
LeakyRelu |
T={bfloat16,float} |
LeakyReluGrad |
T={bfloat16,float} |
LeftShift |
T={int32,int64,uint32,uint64} |
Less |
T={bfloat16,float,int32,int64,uint32,uint64} |
LessEqual |
T={bfloat16,float,int32,int64,uint32,uint64} |
Lgamma |
T={bfloat16,float} |
LinSpace |
Tidx={int32,int64} T={bfloat16,float} |
ListDiff |
out_idx={int32,int64} T={int32,int64} |
Log |
T={bfloat16,complex64,float} |
Log1p |
T={bfloat16,complex64,float} |
LogSoftmax |
T={bfloat16,float} |
LogicalAnd |
|
LogicalNot |
|
LogicalOr |
|
MatMul |
T={bfloat16,complex64,float} |
MatrixBandPart |
Tindex={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
MatrixDiag |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
MatrixDiagPart |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
MatrixSetDiag |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
MatrixTriangularSolve |
T={complex64,float} |
Max |
Tidx={int32,int64} T={bfloat16,complex64,float,int32,int64,uint32,uint64} |
MaxPool |
T={bfloat16,float,int32,int64} |
MaxPool3D |
T={bfloat16,float} |
MaxPool3DGrad |
TInput={bfloat16,float} T={bfloat16,float} |
MaxPool3DGradGrad |
T={float} |
MaxPoolGrad |
T={bfloat16,float,int32,int64,uint32,uint64} |
MaxPoolGradGrad |
T={float} |
MaxPoolGradGradV2 |
T={float} |
MaxPoolGradV2 |
T={bfloat16,float,int32,int64,uint32,uint64} |
MaxPoolV2 |
T={bfloat16,float,int32,int64} |
Maximum |
T={bfloat16,float,int32,int64} |
Mean |
Tidx={int32,int64} T={bfloat16,complex64,float,int32,int64,uint32,uint64} |
Min |
Tidx={int32,int64} T={bfloat16,complex64,float,int32,int64,uint32,uint64} |
Minimum |
T={bfloat16,float,int32,int64} |
MirrorPad |
Tpaddings={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Mod |
T={bfloat16,float,int32,int64} |
Mul |
T={bfloat16,complex64,float,int32,int64} |
Multinomial |
output_dtype={int32,int64} T={bfloat16,float,int32,int64,uint32,uint64} |
Neg |
T={bfloat16,complex64,float,int32,int64} |
NoOp |
|
NonMaxSuppressionV4 |
T={float} |
NotEqual |
T={bfloat16,bool,complex64,float,int32,int64} |
OneHot |
TI={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
OnesLike |
T={bfloat16,bool,complex64,float,int32,int64} |
OutfeedEnqueue |
dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
OutfeedEnqueueTuple |
dtypes={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Pack |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Pad |
Tpaddings={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
PadV2 |
Tpaddings={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
ParallelDynamicStitch |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
PlaceholderWithDefault |
dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Pow |
T={bfloat16,complex64,float,int32,int64} |
PreventGradient |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Prod |
Tidx={int32,int64} T={bfloat16,complex64,float,int32,int64,uint32,uint64} |
Qr |
T={float} |
QuantizeAndDequantizeV2 |
T={bfloat16,float} |
QuantizeAndDequantizeV3 |
T={bfloat16,float} |
RFFT |
|
RFFT2D |
|
RFFT3D |
|
RGBToHSV |
T={bfloat16,float} |
RandomShuffle |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
RandomStandardNormal |
T={int32,int64} dtype={bfloat16,float} |
RandomUniform |
T={int32,int64} dtype={bfloat16,float} |
RandomUniformInt |
T={int32,int64} Tout={int32,int64} |
Range |
Tidx={bfloat16,float,int32,int64} |
Rank |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
ReadVariableOp |
dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Real |
Tout={float} T={complex64} |
RealDiv |
T={bfloat16,complex64,float,int32,int64} |
Reciprocal |
T={bfloat16,complex64,float,int32,int64} |
ReciprocalGrad |
T={bfloat16,complex64,float} |
RecvTPUEmbeddingActivations |
|
Relu |
T={bfloat16,float,int32,int64,uint32,uint64} |
Relu6 |
T={bfloat16,float,int32,int64,uint32,uint64} |
Relu6Grad |
T={bfloat16,float,int32,int64,uint32,uint64} |
ReluGrad |
T={bfloat16,float,int32,int64,uint32,uint64} |
Reshape |
Tshape={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
ResizeBilinear |
T={bfloat16,float,int32,int64} |
ResizeBilinearGrad |
T={bfloat16,float} |
ResizeNearestNeighbor |
T={float,int32,int64} |
ResourceApplyAdaMax |
T={bfloat16,float} |
ResourceApplyAdadelta |
T={bfloat16,float} |
ResourceApplyAdagrad |
T={bfloat16,float} |
ResourceApplyAdagradDA |
T={bfloat16,float} |
ResourceApplyAdam |
T={bfloat16,float} |
ResourceApplyAddSign |
T={bfloat16,float} |
ResourceApplyCenteredRMSProp |
T={bfloat16,float} |
ResourceApplyFtrl |
T={bfloat16,float} |
ResourceApplyFtrlV2 |
T={bfloat16,float} |
ResourceApplyGradientDescent |
T={bfloat16,float} |
ResourceApplyKerasMomentum |
T={bfloat16,float} |
ResourceApplyMomentum |
T={bfloat16,float} |
ResourceApplyPowerSign |
T={bfloat16,float} |
ResourceApplyProximalAdagrad |
T={bfloat16,float} |
ResourceApplyProximalGradientDescent |
T={bfloat16,float} |
ResourceApplyRMSProp |
T={bfloat16,float} |
ResourceGather |
Tindices={int32,int64} dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
ResourceScatterAdd |
Tindices={int32,int64} dtype={bfloat16,complex64,float,int32,int64,uint32,uint64} |
ResourceScatterDiv |
Tindices={int32,int64} dtype={bfloat16,complex64,float,int32,int64,uint32,uint64} |
ResourceScatterMax |
Tindices={int32,int64} dtype={bfloat16,complex64,float,int32,int64,uint32,uint64} |
ResourceScatterMin |
Tindices={int32,int64} dtype={bfloat16,complex64,float,int32,int64,uint32,uint64} |
ResourceScatterMul |
Tindices={int32,int64} dtype={bfloat16,complex64,float,int32,int64,uint32,uint64} |
ResourceScatterNdAdd |
Tindices={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
ResourceScatterNdSub |
Tindices={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
ResourceScatterNdUpdate |
Tindices={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
ResourceScatterSub |
Tindices={int32,int64} dtype={bfloat16,complex64,float,int32,int64,uint32,uint64} |
ResourceScatterUpdate |
Tindices={int32,int64} dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
ResourceStridedSliceAssign |
Index={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Reverse |
T={bool,complex64,float,int32,int64} |
ReverseSequence |
Tlen={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
ReverseV2 |
T={bfloat16,bool,complex64,float,int32,int64} Tidx={int32,int64} |
RightShift |
T={int32,int64,uint32,uint64} |
Rint |
T={bfloat16,float} |
Round |
T={bfloat16,complex64,float,int32,int64} |
Rsqrt |
T={bfloat16,complex64,float} |
RsqrtGrad |
T={bfloat16,complex64,float} |
ScatterNd |
Tindices={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Select |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Selu |
T={bfloat16,float} |
SeluGrad |
T={bfloat16,float} |
SendTPUEmbeddingGradients |
|
Shape |
out_type={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
ShapeN |
out_type={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Sigmoid |
T={bfloat16,complex64,float} |
SigmoidGrad |
T={bfloat16,complex64,float} |
Sign |
T={bfloat16,complex64,float,int32,int64} |
Sin |
T={bfloat16,complex64,float} |
Sinh |
T={bfloat16,complex64,float} |
Size |
out_type={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Slice |
Index={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Snapshot |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Softmax |
T={bfloat16,float} |
SoftmaxCrossEntropyWithLogits |
T={bfloat16,float} |
Softplus |
T={bfloat16,float} |
SoftplusGrad |
T={bfloat16,float} |
Softsign |
T={bfloat16,float} |
SoftsignGrad |
T={bfloat16,float} |
SpaceToBatch |
Tpaddings={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
SpaceToBatchND |
Tblock_shape={int32,int64} Tpaddings={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
SpaceToDepth |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
SparseMatMul |
Tb={bfloat16,float} Ta={bfloat16,float} |
SparseSoftmaxCrossEntropyWithLogits |
Tlabels={int32,int64} T={bfloat16,float} |
SparseToDense |
Tindices={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Split |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
SplitV |
Tlen={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Sqrt |
T={bfloat16,complex64,float} |
SqrtGrad |
T={bfloat16,complex64,float} |
Square |
T={bfloat16,complex64,float,int32,int64} |
SquaredDifference |
T={bfloat16,complex64,float,int32,int64} |
Squeeze |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
StackCloseV2 |
|
StackPopV2 |
elem_type={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
StackPushV2 |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
StackV2 |
elem_type={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
StatelessIf |
Tout={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64} Tin={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64} Tcond={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64} |
StatelessMultinomial |
output_dtype={int32,int64} Tseed={int32} T={bfloat16,float} |
StatelessRandomNormal |
Tseed={int32} T={int32,int64} dtype={bfloat16,float} |
StatelessRandomUniform |
Tseed={int32} T={int32,int64} dtype={bfloat16,float} |
StatelessRandomUniformInt |
Tseed={int32} T={int32,int64} dtype={int32,int64} |
StatelessTruncatedNormal |
Tseed={int32} T={int32,int64} dtype={bfloat16,float} |
StatelessWhile |
T={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64} |
StopGradient |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
StridedSlice |
Index={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
StridedSliceGrad |
Index={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Sub |
T={bfloat16,complex64,float,int32,int64} |
Sum |
Tidx={int32,int64} T={bfloat16,complex64,float,int32,int64,uint32,uint64} |
SymbolicGradient |
Tout={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} Tin={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
TPUEmbeddingActivations |
|
Tan |
T={bfloat16,complex64,float,int32,int64} |
Tanh |
T={bfloat16,complex64,float} |
TanhGrad |
T={bfloat16,complex64,float} |
TensorArrayCloseV3 |
|
TensorArrayConcatV3 |
dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
TensorArrayGatherV3 |
dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
TensorArrayGradV3 |
|
TensorArrayReadV3 |
dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
TensorArrayScatterV3 |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
TensorArraySizeV3 |
|
TensorArraySplitV3 |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
TensorArrayV3 |
dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
TensorArrayWriteV3 |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
TensorListElementShape |
shape_type={int32,int64} |
TensorListPopBack |
element_dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
TensorListPushBack |
element_dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
TensorListReserve |
shape_type={int32,int64} element_dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
Tile |
Tmultiples={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
TopKV2 |
T={bfloat16,float,int32,uint32} |
Transpose |
Tperm={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
TruncateDiv |
T={bfloat16,complex64,float,int32,int64} |
TruncateMod |
T={bfloat16,float,int32,int64} |
TruncatedNormal |
T={int32,int64} dtype={float} |
Unpack |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
UnsortedSegmentMax |
Tnumsegments={int32,int64} Tindices={int32,int64} T={bfloat16,float,int32,int64,uint32,uint64} |
UnsortedSegmentMin |
Tnumsegments={int32,int64} Tindices={int32,int64} T={bfloat16,float,int32,int64,uint32,uint64} |
UnsortedSegmentProd |
Tnumsegments={int32,int64} Tindices={int32,int64} T={bfloat16,complex64,float,int32,int64,uint32,uint64} |
UnsortedSegmentSum |
Tnumsegments={int32,int64} Tindices={int32,int64} T={bfloat16,complex64,float,int32,int64,uint32,uint64} |
VarIsInitializedOp |
|
VariableShape |
out_type={int32,int64} |
While |
T={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64} |
Xdivy |
T={complex64,float} |
XlaBroadcastHelper |
Tindices={int32,int64} T={bfloat16,complex64,float,int32,int64,uint32,uint64} |
XlaConv |
Tindices={int32,int64} T={bfloat16,complex64,float,int32,int64,uint32,uint64} |
XlaDequantize |
|
XlaDot |
T={bfloat16,complex64,float,int32,int64,uint32,uint64} |
XlaDynamicSlice |
Tindices={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
XlaDynamicUpdateSlice |
Tindices={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
XlaHostCompute |
Toutputs={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} Tinputs={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
XlaIf |
Tout={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64} Tin={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64} Tcond={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64} |
XlaKeyValueSort |
V={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} K={bfloat16,float,int32,int64,uint32,uint64} |
XlaPad |
Tindices={int32,int64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
XlaRecv |
dtype={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
XlaRecvFromHost |
Toutput={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
XlaReduce |
T={bfloat16,complex64,float,int32,int64,uint32,uint64} |
XlaReduceWindow |
Tindices={int32,int64} T={bfloat16,complex64,float,int32,int64,uint32,uint64} |
XlaSelectAndScatter |
Tindices={int32,int64} T={bfloat16,complex64,float,int32,int64,uint32,uint64} |
XlaSend |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
XlaSendToHost |
Tinput={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
XlaSort |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
XlaWhile |
T={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64} |
Xlogy |
T={complex64,float} |
ZerosLike |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
_Arg |
T={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64} |
_ArrayToList |
out_types={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
_ListToArray |
T={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} Tin={bfloat16,bool,complex64,float,int32,int64,uint32,uint64} |
_Retval |
T={bfloat16,bool,complex64,float,int32,int64,resource,uint32,uint64} |