diff --git a/python/paddle/tensor/creation.py b/python/paddle/tensor/creation.py index 3ce7b44eea2a07cc0351edc80796ae347160f734..834137c7501bd29c32172cf17ad1698954ab5ccd 100644 --- a/python/paddle/tensor/creation.py +++ b/python/paddle/tensor/creation.py @@ -1044,33 +1044,34 @@ def triu(x, diagonal=0, name=None): Examples: .. code-block:: python - import numpy as np import paddle - data = np.arange(1, 13, dtype="int64").reshape(3,-1) - # array([[ 1, 2, 3, 4], - # [ 5, 6, 7, 8], - # [ 9, 10, 11, 12]]) - + x = paddle.arange(1, 13, dtype="int64").reshape([3,-1]) + # Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, + # [[1 , 2 , 3 , 4 ], + # [5 , 6 , 7 , 8 ], + # [9 , 10, 11, 12]]) # example 1, default diagonal - x = paddle.to_tensor(data) triu1 = paddle.tensor.triu(x) - # array([[ 1, 2, 3, 4], - # [ 0, 6, 7, 8], - # [ 0, 0, 11, 12]]) + # Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, + # [[1 , 2 , 3 , 4 ], + # [0 , 6 , 7 , 8 ], + # [0 , 0 , 11, 12]]) # example 2, positive diagonal value triu2 = paddle.tensor.triu(x, diagonal=2) - # array([[0, 0, 3, 4], - # [0, 0, 0, 8], - # [0, 0, 0, 0]]) + # Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, + # [[0, 0, 3, 4], + # [0, 0, 0, 8], + # [0, 0, 0, 0]]) # example 3, negative diagonal value triu3 = paddle.tensor.triu(x, diagonal=-1) - # array([[ 1, 2, 3, 4], - # [ 5, 6, 7, 8], - # [ 0, 10, 11, 12]]) + # Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True, + # [[1 , 2 , 3 , 4 ], + # [5 , 6 , 7 , 8 ], + # [0 , 10, 11, 12]]) """ if in_dygraph_mode(): @@ -1178,24 +1179,27 @@ def diagflat(x, offset=0, name=None): x = paddle.to_tensor([1, 2, 3]) y = paddle.diagflat(x) - print(y.numpy()) - # [[1 0 0] - # [0 2 0] - # [0 0 3]] + print(y) + # Tensor(shape=[3, 3], dtype=int64, place=Place(cpu), stop_gradient=True, + # [[1, 0, 0], + # [0, 2, 0], + # [0, 0, 3]]) y = paddle.diagflat(x, offset=1) - print(y.numpy()) - # [[0 1 0 0] - # [0 0 2 0] - # [0 0 0 3] - # [0 0 0 0]] + print(y) + # Tensor(shape=[4, 4], dtype=int64, place=Place(cpu), stop_gradient=True, + # [[0, 1, 0, 0], + # [0, 0, 2, 0], + # [0, 0, 0, 3], + # [0, 0, 0, 0]]) y = paddle.diagflat(x, offset=-1) - print(y.numpy()) - # [[0 0 0 0] - # [1 0 0 0] - # [0 2 0 0] - # [0 0 3 0]] + print(y) + # Tensor(shape=[4, 4], dtype=int64, place=Place(cpu), stop_gradient=True, + # [[0, 0, 0, 0], + # [1, 0, 0, 0], + # [0, 2, 0, 0], + # [0, 0, 3, 0]]) .. code-block:: python :name: code-example-2 @@ -1204,27 +1208,30 @@ def diagflat(x, offset=0, name=None): x = paddle.to_tensor([[1, 2], [3, 4]]) y = paddle.diagflat(x) - print(y.numpy()) - # [[1 0 0 0] - # [0 2 0 0] - # [0 0 3 0] - # [0 0 0 4]] + print(y) + # Tensor(shape=[4, 4], dtype=int64, place=Place(cpu), stop_gradient=True, + # [[1, 0, 0, 0], + # [0, 2, 0, 0], + # [0, 0, 3, 0], + # [0, 0, 0, 4]]) y = paddle.diagflat(x, offset=1) - print(y.numpy()) - # [[0 1 0 0 0] - # [0 0 2 0 0] - # [0 0 0 3 0] - # [0 0 0 0 4] - # [0 0 0 0 0]] + print(y) + # Tensor(shape=[5, 5], dtype=int64, place=Place(cpu), stop_gradient=True, + # [[0, 1, 0, 0, 0], + # [0, 0, 2, 0, 0], + # [0, 0, 0, 3, 0], + # [0, 0, 0, 0, 4], + # [0, 0, 0, 0, 0]]) y = paddle.diagflat(x, offset=-1) - print(y.numpy()) - # [[0 0 0 0 0] - # [1 0 0 0 0] - # [0 2 0 0 0] - # [0 0 3 0 0] - # [0 0 0 4 0]] + print(y) + # Tensor(shape=[5, 5], dtype=int64, place=Place(cpu), stop_gradient=True, + # [[0, 0, 0, 0, 0], + # [1, 0, 0, 0, 0], + # [0, 2, 0, 0, 0], + # [0, 0, 3, 0, 0], + # [0, 0, 0, 4, 0]]) """ padding_value = 0 if in_dygraph_mode(): @@ -1318,23 +1325,26 @@ def diag(x, offset=0, padding_value=0, name=None): paddle.disable_static() x = paddle.to_tensor([1, 2, 3]) y = paddle.diag(x) - print(y.numpy()) - # [[1 0 0] - # [0 2 0] - # [0 0 3]] + print(y) + # Tensor(shape=[3, 3], dtype=int64, place=Place(cpu), stop_gradient=True, + # [[1, 0, 0], + # [0, 2, 0], + # [0, 0, 3]]) y = paddle.diag(x, offset=1) - print(y.numpy()) - # [[0 1 0 0] - # [0 0 2 0] - # [0 0 0 3] - # [0 0 0 0]] + print(y) + # Tensor(shape=[4, 4], dtype=int64, place=Place(cpu), stop_gradient=True, + # [[0, 1, 0, 0], + # [0, 0, 2, 0], + # [0, 0, 0, 3], + # [0, 0, 0, 0]]) y = paddle.diag(x, padding_value=6) - print(y.numpy()) - # [[1 6 6] - # [6 2 6] - # [6 6 3]] + print(y) + # Tensor(shape=[3, 3], dtype=int64, place=Place(cpu), stop_gradient=True, + # [[1, 6, 6], + # [6, 2, 6], + # [6, 6, 3]]) .. code-block:: python :name: code-example-2 @@ -1344,16 +1354,19 @@ def diag(x, offset=0, padding_value=0, name=None): paddle.disable_static() x = paddle.to_tensor([[1, 2, 3], [4, 5, 6]]) y = paddle.diag(x) - print(y.numpy()) - # [1 5] + print(y) + # Tensor(shape=[2], dtype=int64, place=Place(cpu), stop_gradient=True, + # [1, 5]) y = paddle.diag(x, offset=1) - print(y.numpy()) - # [2 6] + print(y) + # Tensor(shape=[2], dtype=int64, place=Place(cpu), stop_gradient=True, + # [2, 6]) y = paddle.diag(x, offset=-1) - print(y.numpy()) - # [4] + print(y) + # Tensor(shape=[1], dtype=int64, place=Place(cpu), stop_gradient=True, + # [4]) """ if in_dygraph_mode(): return _C_ops.diag(x, offset, padding_value) @@ -1755,7 +1768,7 @@ def _memcpy(input, place=None, output=None): .. code-block:: python import paddle - import numpy as np + data = paddle.full(shape=[3, 2], fill_value=2.5, dtype='float64') # [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]] result = paddle._memcpy(data, place=paddle.CPUPlace()) # result2 = [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]] """ @@ -1816,10 +1829,10 @@ def complex(real, imag, name=None): x = paddle.arange(2, dtype=paddle.float32).unsqueeze(-1) y = paddle.arange(3, dtype=paddle.float32) z = paddle.complex(x, y) - print(z.numpy()) - - # [[0.+0.j 0.+1.j 0.+2.j] - # [1.+0.j 1.+1.j 1.+2.j]] + print(z) + # Tensor(shape=[2, 3], dtype=complex64, place=Place(cpu), stop_gradient=True, + # [[0j , 1j , 2j ], + # [(1+0j), (1+1j), (1+2j)]]) """ if in_dygraph_mode(): return _C_ops.complex(real, imag) diff --git a/python/paddle/tensor/linalg.py b/python/paddle/tensor/linalg.py index 7c4644bc405d32cec74900dec74f1b58c7d5ba2c..3d087c771a3c5a55b757bfee9eae4f3ee8e03126 100644 --- a/python/paddle/tensor/linalg.py +++ b/python/paddle/tensor/linalg.py @@ -292,38 +292,53 @@ def norm(x, p='fro', axis=None, keepdim=False, name=None): .. code-block:: python import paddle - import numpy as np - shape=[2, 3, 4] - np_input = np.arange(24).astype('float32') - 12 - np_input = np_input.reshape(shape) - x = paddle.to_tensor(np_input) - #[[[-12. -11. -10. -9.] [ -8. -7. -6. -5.] [ -4. -3. -2. -1.]] - # [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 8. 9. 10. 11.]]] + x = paddle.arange(24, dtype="float32").reshape([2, 3, 4]) - 12 + # x: Tensor(shape=[2, 3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, + # [[[-12., -11., -10., -9. ], + # [-8. , -7. , -6. , -5. ], + # [-4. , -3. , -2. , -1. ]], + + # [[ 0. , 1. , 2. , 3. ], + # [ 4. , 5. , 6. , 7. ], + # [ 8. , 9. , 10., 11.]]]) # compute frobenius norm along last two dimensions. out_fro = paddle.linalg.norm(x, p='fro', axis=[0,1]) - # out_fro.numpy() [17.435596 16.911535 16.7332 16.911535] + # out_fro: Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, + # [17.43559647, 16.91153526, 16.73320007, 16.91153526]) # compute 2-order vector norm along last dimension. out_pnorm = paddle.linalg.norm(x, p=2, axis=-1) - #out_pnorm.numpy(): [[21.118711 13.190906 5.477226] - # [ 3.7416575 11.224972 19.131126]] + # out_pnorm: Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, + # [[21.11871147, 13.19090557, 5.47722578 ], + # [3.74165750 , 11.22497177, 19.13112640]]) # compute 2-order norm along [0,1] dimension. out_pnorm = paddle.linalg.norm(x, p=2, axis=[0,1]) - #out_pnorm.numpy(): [17.435596 16.911535 16.7332 16.911535] + # out_pnorm: Tensor(shape=[4], dtype=float32, place=Place(cpu), stop_gradient=True, + # [17.43559647, 16.91153526, 16.73320007, 16.91153526]) # compute inf-order norm - out_pnorm = paddle.linalg.norm(x, p=np.inf) - #out_pnorm.numpy() = [12.] - out_pnorm = paddle.linalg.norm(x, p=np.inf, axis=0) - #out_pnorm.numpy(): [[12. 11. 10. 9.] [8. 7. 6. 7.] [8. 9. 10. 11.]] + out_pnorm = paddle.linalg.norm(x, p=float("inf")) + # out_pnorm = Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, + # [12.]) + + out_pnorm = paddle.linalg.norm(x, p=float("inf"), axis=0) + # out_pnorm: Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, + # [[12., 11., 10., 9. ], + # [8. , 7. , 6. , 7. ], + # [8. , 9. , 10., 11.]]) # compute -inf-order norm - out_pnorm = paddle.linalg.norm(x, p=-np.inf) - #out_pnorm.numpy(): [0.] - out_pnorm = paddle.linalg.norm(x, p=-np.inf, axis=0) - #out_pnorm.numpy(): [[0. 1. 2. 3.] [4. 5. 6. 5.] [4. 3. 2. 1.]] + out_pnorm = paddle.linalg.norm(x, p=-float("inf")) + # out_pnorm: Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True, + # [0.]) + + out_pnorm = paddle.linalg.norm(x, p=-float("inf"), axis=0) + # out_pnorm: Tensor(shape=[3, 4], dtype=float32, place=Place(cpu), stop_gradient=True, + # [[0., 1., 2., 3.], + # [4., 5., 6., 5.], + # [4., 3., 2., 1.]]) """ def frobenius_norm(input, dim=None, keepdim=False, name=None): @@ -634,10 +649,9 @@ def dist(x, y, p=2, name=None): .. code-block:: python import paddle - import numpy as np - x = paddle.to_tensor(np.array([[3, 3],[3, 3]]), "float32") - y = paddle.to_tensor(np.array([[3, 3],[3, 1]]), "float32") + x = paddle.to_tensor([[3, 3],[3, 3]], dtype="float32") + y = paddle.to_tensor([[3, 3],[3, 1]], dtype="float32") out = paddle.dist(x, y, 0) print(out) # out = [1.] @@ -1046,14 +1060,18 @@ def dot(x, y, name=None): .. code-block:: python import paddle - import numpy as np - x_data = np.random.uniform(0.1, 1, [10]).astype(np.float32) - y_data = np.random.uniform(1, 3, [10]).astype(np.float32) - x = paddle.to_tensor(x_data) - y = paddle.to_tensor(y_data) + # 1-D Tensor * 1-D Tensor + x = paddle.to_tensor([1, 2, 3]) + y = paddle.to_tensor([4, 5, 6]) + z = paddle.dot(x, y) + print(z) # [32] + + # 2-D Tensor * 2-D Tensor + x = paddle.to_tensor([[1, 2, 3], [2, 4, 6]]) + y = paddle.to_tensor([[4, 5, 6], [4, 5, 6]]) z = paddle.dot(x, y) - print(z) + print(z) # [[32], [64]] """ if in_dygraph_mode(): @@ -2454,7 +2472,6 @@ def multi_dot(x, name=None): .. code-block:: python import paddle - import numpy as np # A * B A = paddle.rand([3, 4]) @@ -3016,7 +3033,6 @@ def triangular_solve(x, # -x3 = 5 import paddle - import numpy as np x = paddle.to_tensor([[1, 1, 1], [0, 2, 1], @@ -3127,14 +3143,13 @@ def eigvalsh(x, UPLO='L', name=None): Examples: .. code-block:: python - import numpy as np import paddle - x_data = np.array([[1, -2j], [2j, 5]]) - x = paddle.to_tensor(x_data) + x = paddle.to_tensor([[1, -2j], [2j, 5]]) out_value = paddle.eigvalsh(x, UPLO='L') print(out_value) - #[0.17157288, 5.82842712] + # Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, + # [0.17157286, 5.82842731]) """ if in_dygraph_mode(): values, _ = _C_ops.eigvalsh(x, UPLO, x.stop_gradient) diff --git a/python/paddle/tensor/logic.py b/python/paddle/tensor/logic.py index eb12ae7395329fd7d5a9a0134ccadd27e865fd32..53649931ee6db666c35514d1caa99a948c722c61 100755 --- a/python/paddle/tensor/logic.py +++ b/python/paddle/tensor/logic.py @@ -150,14 +150,14 @@ def logical_or(x, y, out=None, name=None): .. code-block:: python import paddle - import numpy as np - x_data = np.array([True, False], dtype=np.bool_).reshape(2, 1) - y_data = np.array([True, False, True, False], dtype=np.bool_).reshape(2, 2) - x = paddle.to_tensor(x_data) - y = paddle.to_tensor(y_data) + x = paddle.to_tensor([True, False], dtype="bool").reshape([2, 1]) + y = paddle.to_tensor([True, False, True, False], dtype="bool").reshape([2, 2]) res = paddle.logical_or(x, y) - print(res) # [[ True True] [ True False]] + print(res) + # Tensor(shape=[2, 2], dtype=bool, place=Place(cpu), stop_gradient=True, + # [[True , True ], + # [True , False]]) """ if in_dygraph_mode(): return _C_ops.logical_or(x, y) @@ -195,14 +195,14 @@ def logical_xor(x, y, out=None, name=None): .. code-block:: python import paddle - import numpy as np - x_data = np.array([True, False], dtype=np.bool_).reshape([2, 1]) - y_data = np.array([True, False, True, False], dtype=np.bool_).reshape([2, 2]) - x = paddle.to_tensor(x_data) - y = paddle.to_tensor(y_data) + x = paddle.to_tensor([True, False], dtype="bool").reshape([2, 1]) + y = paddle.to_tensor([True, False, True, False], dtype="bool").reshape([2, 2]) res = paddle.logical_xor(x, y) - print(res) # [[False, True], [ True, False]] + print(res) + # Tensor(shape=[2, 2], dtype=bool, place=Place(cpu), stop_gradient=True, + # [[False, True ], + # [True , False]]) """ if in_dygraph_mode(): return _C_ops.logical_xor(x, y) @@ -373,22 +373,20 @@ def allclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False, name=None): y = paddle.to_tensor([10000.1, 1e-08]) result1 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False, name="ignore_nan") - np_result1 = result1.numpy() # [False] + result2 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=True, name="equal_nan") - np_result2 = result2.numpy() # [False] x = paddle.to_tensor([1.0, float('nan')]) y = paddle.to_tensor([1.0, float('nan')]) result1 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False, name="ignore_nan") - np_result1 = result1.numpy() # [False] + result2 = paddle.allclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=True, name="equal_nan") - np_result2 = result2.numpy() # [True] """ @@ -966,22 +964,18 @@ def isclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False, name=None): y = paddle.to_tensor([10000.1, 1e-08]) result1 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False, name="ignore_nan") - np_result1 = result1.numpy() # [True, False] result2 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=True, name="equal_nan") - np_result2 = result2.numpy() # [True, False] x = paddle.to_tensor([1.0, float('nan')]) y = paddle.to_tensor([1.0, float('nan')]) result1 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=False, name="ignore_nan") - np_result1 = result1.numpy() # [True, False] result2 = paddle.isclose(x, y, rtol=1e-05, atol=1e-08, equal_nan=True, name="equal_nan") - np_result2 = result2.numpy() # [True, True] """ diff --git a/python/paddle/tensor/manipulation.py b/python/paddle/tensor/manipulation.py index 34390124eb7f1fc8ef27cf269a3e37857a9a63fc..b2d2e0d17cb360510367ee7b8e41c501d2487342 100644 --- a/python/paddle/tensor/manipulation.py +++ b/python/paddle/tensor/manipulation.py @@ -1229,12 +1229,9 @@ def flip(x, axis, name=None): .. code-block:: python import paddle - import numpy as np image_shape=(3, 2, 2) - x = np.arange(image_shape[0] * image_shape[1] * image_shape[2]).reshape(image_shape) - x = x.astype('float32') - img = paddle.to_tensor(x) + img = paddle.arange(image_shape[0] * image_shape[1] * image_shape[2]).reshape(image_shape) tmp = paddle.flip(img, [0,1]) print(tmp) # [[[10,11],[8, 9]], [[6, 7],[4, 5]], [[2, 3],[0, 1]]] @@ -2877,15 +2874,12 @@ def chunk(x, chunks, axis=0, name=None): Returns: list(Tensor): The list of segmented Tensors. - Example: + Examples: .. code-block:: python - import numpy as np import paddle - # x is a Tensor which shape is [3, 9, 5] - x_np = np.random.random([3, 9, 5]).astype("int32") - x = paddle.to_tensor(x_np) + x = paddle.rand([3, 9, 5]) out0, out1, out2 = paddle.chunk(x, chunks=3, axis=1) # out0.shape [3, 3, 5] @@ -4440,10 +4434,11 @@ def index_add(x, index, axis, value, name=None): index = paddle.to_tensor([0, 2], dtype="int32") value = paddle.to_tensor([[1, 1, 1], [1, 1, 1]], dtype="float32") outplace_res = paddle.index_add(input_tensor, index, 0, value) - print(outplace_res.numpy()) - # [[2 2 2] - # [1 1 1] - # [2 2 2]] + print(outplace_res) + # Tensor(shape=[3, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True, + # [[2., 2., 2.], + # [1., 1., 1.], + # [2., 2., 2.]]) """ if in_dygraph_mode(): return _C_ops.index_add(x, index, value, axis) @@ -4487,10 +4482,11 @@ def index_add_(x, index, axis, value, name=None): index = paddle.to_tensor([0, 2], dtype="int32") value = paddle.to_tensor([[1, 1], [1, 1], [1, 1]], dtype="float32") inplace_res = paddle.index_add_(input_tensor, index, 1, value) - print(inplace_res.numpy()) - # [[2, 1, 2] - # [2, 1, 2] - # [2, 1, 2]] + print(inplace_res) + # Tensor(shape=[3, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True, + # [[2., 1., 2.], + # [2., 1., 2.], + # [2., 1., 2.]]) """ return _C_ops.index_add_(x, index, value, axis) diff --git a/python/paddle/tensor/math.py b/python/paddle/tensor/math.py index b046404d83a5ab117a065589758449eb05047108..ab956167d5bc65565c23f863fc52e7039f83dd46 100644 --- a/python/paddle/tensor/math.py +++ b/python/paddle/tensor/math.py @@ -929,34 +929,37 @@ def maximum(x, y, name=None): .. code-block:: python - import numpy as np import paddle x = paddle.to_tensor([[1, 2], [7, 8]]) y = paddle.to_tensor([[3, 4], [5, 6]]) res = paddle.maximum(x, y) print(res) - # [[3, 4], - # [7, 8]] + # Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, + # [[3, 4], + # [7, 8]]) x = paddle.to_tensor([[1, 2, 3], [1, 2, 3]]) y = paddle.to_tensor([3, 0, 4]) res = paddle.maximum(x, y) print(res) - # [[3, 2, 4], - # [3, 2, 4]] + # Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, + # [[3, 2, 4], + # [3, 2, 4]]) x = paddle.to_tensor([2, 3, 5], dtype='float32') - y = paddle.to_tensor([1, np.nan, np.nan], dtype='float32') + y = paddle.to_tensor([1, float("nan"), float("nan")], dtype='float32') res = paddle.maximum(x, y) print(res) - # [ 2., nan, nan] + # Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, + # [2. , nan, nan]) - x = paddle.to_tensor([5, 3, np.inf], dtype='float32') - y = paddle.to_tensor([1, -np.inf, 5], dtype='float32') + x = paddle.to_tensor([5, 3, float("inf")], dtype='float32') + y = paddle.to_tensor([1, -float("inf"), 5], dtype='float32') res = paddle.maximum(x, y) print(res) - # [ 5., 3., inf.] + # Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, + # [5. , 3. , inf.]) """ op_type = 'elementwise_max' axis = -1 @@ -994,34 +997,37 @@ def minimum(x, y, name=None): .. code-block:: python - import numpy as np import paddle x = paddle.to_tensor([[1, 2], [7, 8]]) y = paddle.to_tensor([[3, 4], [5, 6]]) res = paddle.minimum(x, y) print(res) - # [[1, 2], - # [5, 6]] + # Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, + # [[1, 2], + # [5, 6]]) x = paddle.to_tensor([[[1, 2, 3], [1, 2, 3]]]) y = paddle.to_tensor([3, 0, 4]) res = paddle.minimum(x, y) print(res) - # [[[1, 0, 3], - # [1, 0, 3]]] + # Tensor(shape=[1, 2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, + # [[[1, 0, 3], + # [1, 0, 3]]]) x = paddle.to_tensor([2, 3, 5], dtype='float32') - y = paddle.to_tensor([1, np.nan, np.nan], dtype='float32') + y = paddle.to_tensor([1, float("nan"), float("nan")], dtype='float32') res = paddle.minimum(x, y) print(res) - # [ 1., nan, nan] + # Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, + # [1. , nan, nan]) - x = paddle.to_tensor([5, 3, np.inf], dtype='float64') - y = paddle.to_tensor([1, -np.inf, 5], dtype='float64') + x = paddle.to_tensor([5, 3, float("inf")], dtype='float64') + y = paddle.to_tensor([1, -float("inf"), 5], dtype='float64') res = paddle.minimum(x, y) print(res) - # [ 1., -inf., 5.] + # Tensor(shape=[3], dtype=float64, place=Place(cpu), stop_gradient=True, + # [ 1. , -inf., 5. ]) """ op_type = 'elementwise_min' axis = -1 @@ -1061,34 +1067,37 @@ def fmax(x, y, name=None): .. code-block:: python - import numpy as np import paddle x = paddle.to_tensor([[1, 2], [7, 8]]) y = paddle.to_tensor([[3, 4], [5, 6]]) res = paddle.fmax(x, y) print(res) - # [[3, 4], - # [7, 8]] + # Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, + # [[3, 4], + # [7, 8]]) x = paddle.to_tensor([[1, 2, 3], [1, 2, 3]]) y = paddle.to_tensor([3, 0, 4]) res = paddle.fmax(x, y) print(res) - # [[3, 2, 4], - # [3, 2, 4]] + # Tensor(shape=[2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, + # [[3, 2, 4], + # [3, 2, 4]]) x = paddle.to_tensor([2, 3, 5], dtype='float32') - y = paddle.to_tensor([1, np.nan, np.nan], dtype='float32') + y = paddle.to_tensor([1, float("nan"), float("nan")], dtype='float32') res = paddle.fmax(x, y) print(res) - # [ 2., 3., 5.] + # Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, + # [2., 3., 5.]) - x = paddle.to_tensor([5, 3, np.inf], dtype='float32') - y = paddle.to_tensor([1, -np.inf, 5], dtype='float32') + x = paddle.to_tensor([5, 3, float("inf")], dtype='float32') + y = paddle.to_tensor([1, -float("inf"), 5], dtype='float32') res = paddle.fmax(x, y) print(res) - # [ 5., 3., inf.] + # Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, + # [5. , 3. , inf.]) """ op_type = 'elementwise_fmax' axis = -1 @@ -1128,34 +1137,37 @@ def fmin(x, y, name=None): .. code-block:: python - import numpy as np import paddle x = paddle.to_tensor([[1, 2], [7, 8]]) y = paddle.to_tensor([[3, 4], [5, 6]]) res = paddle.fmin(x, y) print(res) - # [[1, 2], - # [5, 6]] + # Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, + # [[1, 2], + # [5, 6]]) x = paddle.to_tensor([[[1, 2, 3], [1, 2, 3]]]) y = paddle.to_tensor([3, 0, 4]) res = paddle.fmin(x, y) print(res) - # [[[1, 0, 3], - # [1, 0, 3]]] + # Tensor(shape=[1, 2, 3], dtype=int64, place=Place(cpu), stop_gradient=True, + # [[[1, 0, 3], + # [1, 0, 3]]]) x = paddle.to_tensor([2, 3, 5], dtype='float32') - y = paddle.to_tensor([1, np.nan, np.nan], dtype='float32') + y = paddle.to_tensor([1, float("nan"), float("nan")], dtype='float32') res = paddle.fmin(x, y) print(res) - # [ 1., 3., 5.] + # Tensor(shape=[3], dtype=float32, place=Place(cpu), stop_gradient=True, + # [1., 3., 5.]) - x = paddle.to_tensor([5, 3, np.inf], dtype='float64') - y = paddle.to_tensor([1, -np.inf, 5], dtype='float64') + x = paddle.to_tensor([5, 3, float("inf")], dtype='float64') + y = paddle.to_tensor([1, -float("inf"), 5], dtype='float64') res = paddle.fmin(x, y) print(res) - # [ 1., -inf., 5.] + # Tensor(shape=[3], dtype=float64, place=Place(cpu), stop_gradient=True, + # [ 1. , -inf., 5. ]) """ op_type = 'elementwise_fmin' axis = -1 @@ -1321,15 +1333,13 @@ def nansum(x, axis=None, dtype=None, keepdim=False, name=None): .. code-block:: python import paddle - import numpy as np # x is a Tensor with following elements: # [[nan, 0.3, 0.5, 0.9] # [0.1, 0.2, -nan, 0.7]] # Each example is followed by the corresponding output tensor. - x = np.array([[float('nan'), 0.3, 0.5, 0.9], - [0.1, 0.2, float('-nan'), 0.7]]).astype(np.float32) - x = paddle.to_tensor(x) + x = paddle.to_tensor([[float('nan'), 0.3, 0.5, 0.9], + [0.1, 0.2, float('-nan'), 0.7]],dtype="float32") out1 = paddle.nansum(x) # [2.7] out2 = paddle.nansum(x, axis=0) # [0.1, 0.5, 0.5, 1.6] out3 = paddle.nansum(x, axis=-1) # [1.7, 1.0] @@ -1339,9 +1349,8 @@ def nansum(x, axis=None, dtype=None, keepdim=False, name=None): # [[[1, nan], [3, 4]], # [[5, 6], [-nan, 8]]] # Each example is followed by the corresponding output tensor. - y = np.array([[[1, float('nan')], [3, 4]], + y = paddle.to_tensor([[[1, float('nan')], [3, 4]], [[5, 6], [float('-nan'), 8]]]) - y = paddle.to_tensor(y) out5 = paddle.nansum(y, axis=[1, 2]) # [8, 19] out6 = paddle.nansum(y, axis=[0, 1]) # [9, 18] """ @@ -4323,7 +4332,7 @@ def rad2deg(x, name=None): .. code-block:: python import paddle - import numpy as np + import math x1 = paddle.to_tensor([3.142, -3.142, 6.283, -6.283, 1.570, -1.570]) result1 = paddle.rad2deg(x1) @@ -4332,7 +4341,7 @@ def rad2deg(x, name=None): # [180.02334595, -180.02334595, 359.98937988, -359.98937988, # 9.95437622 , -89.95437622]) - x2 = paddle.to_tensor(np.pi/2) + x2 = paddle.to_tensor(math.pi/2) result2 = paddle.rad2deg(x2) print(result2) # Tensor(shape=[1], dtype=float32, place=CUDAPlace(0), stop_gradient=True, @@ -4813,18 +4822,20 @@ def angle(x, name=None): x = paddle.to_tensor([-2, -1, 0, 1]).unsqueeze(-1).astype('float32') y = paddle.to_tensor([-2, -1, 0, 1]).astype('float32') z = x + 1j * y - print(z.numpy()) - # [[-2.-2.j -2.-1.j -2.+0.j -2.+1.j] - # [-1.-2.j -1.-1.j -1.+0.j -1.+1.j] - # [ 0.-2.j 0.-1.j 0.+0.j 0.+1.j] - # [ 1.-2.j 1.-1.j 1.+0.j 1.+1.j]] + print(z) + # Tensor(shape=[4, 4], dtype=complex64, place=Place(cpu), stop_gradient=True, + # [[(-2-2j), (-2-1j), (-2+0j), (-2+1j)], + # [(-1-2j), (-1-1j), (-1+0j), (-1+1j)], + # [-2j , -1j , 0j , 1j ], + # [ (1-2j), (1-1j), (1+0j), (1+1j)]]) theta = paddle.angle(z) - print(theta.numpy()) - # [[-2.3561945 -2.6779451 3.1415927 2.6779451] - # [-2.0344439 -2.3561945 3.1415927 2.3561945] - # [-1.5707964 -1.5707964 0. 1.5707964] - # [-1.1071488 -0.7853982 0. 0.7853982]] + print(theta) + # Tensor(shape=[4, 4], dtype=float32, place=Place(cpu), stop_gradient=True, + # [[-2.35619450, -2.67794514, 3.14159274, 2.67794514], + # [-2.03444386, -2.35619450, 3.14159274, 2.35619450], + # [-1.57079637, -1.57079637, 0. , 1.57079637], + # [-1.10714877, -0.78539819, 0. , 0.78539819]]) """ if in_dygraph_mode(): @@ -4911,19 +4922,14 @@ def frac(x, name=None): .. code-block:: python import paddle - import numpy as np - - input = paddle.rand([3, 3], 'float32') - print(input.numpy()) - # [[ 1.2203873 -1.0035421 -0.35193074] - # [-0.00928353 0.58917075 -0.8407828 ] - # [-1.5131804 0.5850153 -0.17597814]] + input = paddle.to_tensor([[12.22000003, -1.02999997], + [-0.54999995, 0.66000003]]) output = paddle.frac(input) - print(output.numpy()) - # [[ 0.22038734 -0.00354207 -0.35193074] - # [-0.00928353 0.58917075 -0.8407828 ] - # [-0.5131804 0.5850153 -0.17597814]] + print(output) + # Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, + # [[ 0.22000003, -0.02999997], + # [-0.54999995, 0.66000003]]) """ op_type = 'elementwise_sub' axis = -1 diff --git a/python/paddle/tensor/stat.py b/python/paddle/tensor/stat.py index 144620f3c6ea4eb41560905d47f6e4006d1d17f6..3f4e23c2baf446a68762f7ce4e12466b2c4748b8 100644 --- a/python/paddle/tensor/stat.py +++ b/python/paddle/tensor/stat.py @@ -605,32 +605,35 @@ def quantile(x, q, axis=None, keepdim=False): Examples: .. code-block:: python - import numpy as np import paddle - x = np.arange(0, 8, dtype=np.float32).reshape(4, 2) - # [[0 1] - # [2 3] - # [4 5] - # [6 7]] - y = paddle.to_tensor(x) + y = paddle.arange(0, 8 ,dtype="float32").reshape([4, 2]) + # Tensor(shape=[4, 2], dtype=float32, place=Place(cpu), stop_gradient=True, + # [[0., 1.], + # [2., 3.], + # [4., 5.], + # [6., 7.]]) + y1 = paddle.quantile(y, q=0.5, axis=[0, 1]) - # 3.5 + # Tensor(shape=[], dtype=float64, place=Place(cpu), stop_gradient=True, + # 3.50000000) y2 = paddle.quantile(y, q=0.5, axis=1) - # [0.5 2.5 4.5 6.5] + # Tensor(shape=[4], dtype=float64, place=Place(cpu), stop_gradient=True, + # [0.50000000, 2.50000000, 4.50000000, 6.50000000]) y3 = paddle.quantile(y, q=[0.3, 0.5], axis=0) - # [[1.8 2.8] - # [3. 4. ]] + # Tensor(shape=[2, 2], dtype=float64, place=Place(cpu), stop_gradient=True, + # [[1.80000000, 2.80000000], + # [3. , 4. ]]) - x[0][0] = np.nan - y = paddle.to_tensor(x) + y[0,0] = float("nan") y4 = paddle.quantile(y, q=0.8, axis=1, keepdim=True) - # [[nan] - # [2.8] - # [4.8] - # [6.8]] + # Tensor(shape=[4, 1], dtype=float64, place=Place(cpu), stop_gradient=True, + # [[nan ], + # [2.80000000], + # [4.80000000], + # [6.80000000]]) """ return _compute_quantile(x, q, axis=axis, keepdim=keepdim, ignore_nan=False) @@ -665,35 +668,37 @@ def nanquantile(x, q, axis=None, keepdim=False): Examples: .. code-block:: python - import numpy as np import paddle - x = np.array( + x = paddle.to_tensor( [[0, 1, 2, 3, 4], - [5, 6, 7, 8, 9]], - dtype=np.float32 - ) - x[0][0] = np.nan + [5, 6, 7, 8, 9]], + dtype="float32") + x[0,0] = float("nan") - x = paddle.to_tensor(x) y1 = paddle.nanquantile(x, q=0.5, axis=[0, 1]) - # 5.0 + # Tensor(shape=[], dtype=float64, place=Place(cpu), stop_gradient=True, + # 5.) y2 = paddle.nanquantile(x, q=0.5, axis=1) - # [2.5 7. ] + # Tensor(shape=[2], dtype=float64, place=Place(cpu), stop_gradient=True, + # [2.50000000, 7. ]) y3 = paddle.nanquantile(x, q=[0.3, 0.5], axis=0) - # [[5. 2.5 3.5 4.5 5.5] - # [5. 3.5 4.5 5.5 6.5] + # Tensor(shape=[2, 5], dtype=float64, place=Place(cpu), stop_gradient=True, + # [[5. , 2.50000000, 3.50000000, 4.50000000, 5.50000000], + # [5. , 3.50000000, 4.50000000, 5.50000000, 6.50000000]]) y4 = paddle.nanquantile(x, q=0.8, axis=1, keepdim=True) - # [[3.4] - # [8.2]] + # Tensor(shape=[2, 1], dtype=float64, place=Place(cpu), stop_gradient=True, + # [[3.40000000], + # [8.20000000]]) - nan = paddle.full(shape=[2, 3], fill_value=np.nan) + nan = paddle.full(shape=[2, 3], fill_value=float("nan")) y5 = paddle.nanquantile(nan, q=0.8, axis=1, keepdim=True) - # [[nan] - # [nan]] + # Tensor(shape=[2, 1], dtype=float64, place=Place(cpu), stop_gradient=True, + # [[nan], + # [nan]]) """ return _compute_quantile(x, q, axis=axis, keepdim=keepdim, ignore_nan=True)