diff --git a/mindspore/ops/operations/array_ops.py b/mindspore/ops/operations/array_ops.py index 3a39ce7e10..d76e85f627 100644 --- a/mindspore/ops/operations/array_ops.py +++ b/mindspore/ops/operations/array_ops.py @@ -1344,7 +1344,8 @@ class Argmin(PrimitiveWithInfer): Examples: >>> input_x = Tensor(np.array([2.0, 3.1, 1.2]), mindspore.float32) >>> index = P.Argmin()(input_x) - >>> assert index == Tensor(2, mindspore.int64) + >>> print(index) + 2 """ @prim_attr_register diff --git a/mindspore/ops/operations/comm_ops.py b/mindspore/ops/operations/comm_ops.py index 76dbb49f16..9ce3a69f0e 100644 --- a/mindspore/ops/operations/comm_ops.py +++ b/mindspore/ops/operations/comm_ops.py @@ -363,6 +363,7 @@ class Broadcast(PrimitiveWithInfer): >>> from mindspore.communication import init >>> import mindspore.nn as nn >>> import mindspore.ops.operations as P + >>> import numpy as np >>> >>> init() >>> class Net(nn.Cell): diff --git a/mindspore/ops/operations/debug_ops.py b/mindspore/ops/operations/debug_ops.py index cf2c4424ac..0213a1669a 100644 --- a/mindspore/ops/operations/debug_ops.py +++ b/mindspore/ops/operations/debug_ops.py @@ -358,12 +358,12 @@ class Assert(PrimitiveWithInfer): >>> class AssertDemo(nn.Cell): >>> def __init__(self): >>> super(AssertDemo, self).__init__() - >>> self.assert = P.Assert(summarize=10) + >>> self.assert1 = P.Assert(summarize=10) >>> self.add = P.TensorAdd() >>> >>> def construct(self, x, y): >>> data = self.add(x, y) - >>> self.assert(True, [data]) + >>> self.assert1(True, [data]) >>> return data """ diff --git a/mindspore/ops/operations/math_ops.py b/mindspore/ops/operations/math_ops.py index a37d3b0293..ab9d0665b4 100644 --- a/mindspore/ops/operations/math_ops.py +++ b/mindspore/ops/operations/math_ops.py @@ -182,7 +182,9 @@ class AssignAdd(PrimitiveWithInfer): >>> >>> net = Net() >>> value = Tensor(np.ones([1]).astype(np.int64)*100) - >>> net(value) + >>> output = net(value) + >>> print(output) + Parameter (name=global_step) """ __mindspore_signature__ = ( sig.make_sig('x', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T), @@ -232,7 +234,9 @@ class AssignSub(PrimitiveWithInfer): >>> >>> net = Net() >>> value = Tensor(np.ones([1]).astype(np.int32)*100) - >>> net(value) + >>> output = net(value) + >>> print(output) + Parameter (name=global_step) """ __mindspore_signature__ = ( @@ -3347,8 +3351,9 @@ class Atan(PrimitiveWithInfer): >>> tan = P.Tan() >>> output_y = tan(input_x) >>> atan = P.Atan() - >>> atan(output_y) - [[1.047, 07850001]] + >>> output = atan(output_y) + >>> print(output) + [[1.047, 0.7850001]] """ @prim_attr_register @@ -3473,7 +3478,7 @@ class BitwiseAnd(_BitwiseBinaryOp): - **input_x2** (Tensor) - The input tensor with same type as the `input_x1`. Outputs: - - **y** (Tensor) - The same type as the `input_x1`. + Tensor, has the same type as the `input_x1`. Examples: >>> input_x1 = Tensor(np.array([0, 0, 1, -1, 1, 1, 1]), mstype.int16) @@ -3499,7 +3504,7 @@ class BitwiseOr(_BitwiseBinaryOp): - **input_x2** (Tensor) - The input tensor with same type as the `input_x1`. Outputs: - - **y** (Tensor) - The same type as the `input_x1`. + Tensor, has the same type as the `input_x1`. Examples: >>> input_x1 = Tensor(np.array([0, 0, 1, -1, 1, 1, 1]), mstype.int16) @@ -3525,7 +3530,7 @@ class BitwiseXor(_BitwiseBinaryOp): - **input_x2** (Tensor) - The input tensor with same type as the `input_x1`. Outputs: - - **y** (Tensor) - The same type as the `input_x1`. + Tensor, has the same type as the `input_x1`. Examples: >>> input_x1 = Tensor(np.array([0, 0, 1, -1, 1, 1, 1]), mstype.int16) @@ -3541,10 +3546,11 @@ class BesselI0e(PrimitiveWithInfer): Computes BesselI0e of input element-wise. Inputs: - - **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`. + - **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`. Data type must be float16 or + float32. Outputs: - Tensor, has the same shape as `input_x`. Data type must be float16 or float32. + Tensor, has the same shape as `input_x`. Examples: >>> bessel_i0e = P.BesselI0e() @@ -3570,10 +3576,11 @@ class BesselI1e(PrimitiveWithInfer): Computes BesselI1e of input element-wise. Inputs: - - **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`. + - **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`. Data type must be float16 or + float32. Outputs: - Tensor, has the same shape as `input_x`. Data type must be float16 or float32. + Tensor, has the same shape as `input_x`. Examples: >>> bessel_i1e = P.BesselI1e() diff --git a/mindspore/ops/operations/nn_ops.py b/mindspore/ops/operations/nn_ops.py index 29113818c7..802d353497 100644 --- a/mindspore/ops/operations/nn_ops.py +++ b/mindspore/ops/operations/nn_ops.py @@ -789,9 +789,12 @@ class BNTrainingReduce(PrimitiveWithInfer): - **square_sum** (Tensor) - A 1-D Tensor with float32 data type. Tensor of shape :math:`(C,)`. Examples: - >>> input_x = Tensor(np.ones([128, 64, 32, 64]), mindspore.float32) + >>> input_x = Tensor(np.ones([128, 3, 32, 3]), mindspore.float32) >>> bn_training_reduce = P.BNTrainingReduce() >>> output = bn_training_reduce(input_x) + >>> print(output) + ([1.22880000e+04, 1.22880000e+04, 1.22880000e+04], + [1.22880000e+04, 1.22880000e+04, 1.22880000e+04]) """ @prim_attr_register @@ -843,15 +846,30 @@ class BNTrainingUpdate(PrimitiveWithInfer): Has the same shape as `variance`. Examples: - >>> input_x = Tensor(np.ones([128, 64, 32, 64]), mindspore.float32) - >>> sum = Tensor(np.ones([64]), mindspore.float32) - >>> square_sum = Tensor(np.ones([64]), mindspore.float32) - >>> scale = Tensor(np.ones([64]), mindspore.float32) - >>> offset = Tensor(np.ones([64]), mindspore.float32) - >>> mean = Tensor(np.ones([64]), mindspore.float32) - >>> variance = Tensor(np.ones([64]), mindspore.float32) + >>> input_x = Tensor(np.ones([1, 2, 2, 2]), mindspore.float32) + >>> sum = Tensor(np.ones([2]), mindspore.float32) + >>> square_sum = Tensor(np.ones([2]), mindspore.float32) + >>> scale = Tensor(np.ones([2]), mindspore.float32) + >>> offset = Tensor(np.ones([2]), mindspore.float32) + >>> mean = Tensor(np.ones([2]), mindspore.float32) + >>> variance = Tensor(np.ones([2]), mindspore.float32) >>> bn_training_update = P.BNTrainingUpdate() >>> output = bn_training_update(input_x, sum, square_sum, scale, offset, mean, variance) + >>> print(output) + ([[[[2.73200464e+00, 2.73200464e+00], + [2.73200464e+00, 2.73200464e+00]], + [[2.73200464e+00, 2.73200464e+00], + [2.73200464e+00, 2.73200464e+00]]]], + [[[[2.73200464e+00, 2.73200464e+00], + [2.73200464e+00, 2.73200464e+00]], + [[2.73200464e+00, 2.73200464e+00], + [2.73200464e+00, 2.73200464e+00]]]], + [[[[2.73200464e+00, 2.73200464e+00], + [2.73200464e+00, 2.73200464e+00]], + [[2.73200464e+00, 2.73200464e+00], + [2.73200464e+00, 2.73200464e+00]]]], + [2.50000000e-01, 2.50000000e-01], + [1.87500000e-01, 1.87500000e-01]) """ @prim_attr_register @@ -928,13 +946,20 @@ class BatchNorm(PrimitiveWithInfer): - **reserve_space_2** (Tensor) - Tensor of shape :math:`(C,)`. Examples: - >>> input_x = Tensor(np.ones([32, 64]), mindspore.float32) - >>> scale = Tensor(np.ones([64]), mindspore.float32) - >>> bias = Tensor(np.ones([64]), mindspore.float32) - >>> mean = Tensor(np.ones([64]), mindspore.float32) - >>> variance = Tensor(np.ones([64]), mindspore.float32) + >>> input_x = Tensor(np.ones([2, 2]), mindspore.float32) + >>> scale = Tensor(np.ones([2]), mindspore.float32) + >>> bias = Tensor(np.ones([2]), mindspore.float32) + >>> mean = Tensor(np.ones([2]), mindspore.float32) + >>> variance = Tensor(np.ones([2]), mindspore.float32) >>> batch_norm = P.BatchNorm() >>> output = batch_norm(input_x, scale, bias, mean, variance) + >>> print(output) + ([[1.0, 1.0], + [1.0, 1.0]], + [1.0, 1.0], + [1.0, 1.0], + [1.0, 1.0], + [1.0, 1.0]) """ @prim_attr_register @@ -1704,7 +1729,10 @@ class BiasAdd(PrimitiveWithInfer): >>> input_x = Tensor(np.arange(6).reshape((2, 3)), mindspore.float32) >>> bias = Tensor(np.random.random(3).reshape((3,)), mindspore.float32) >>> bias_add = P.BiasAdd() - >>> bias_add(input_x, bias) + >>> output = bias_add(input_x, bias) + >>> print(output) + [[0.4662124 1.2493685 2.3611782] + [3.4662123 4.2493687 5.3611784]] """ @prim_attr_register @@ -4805,7 +4833,7 @@ class ApplyPowerSign(PrimitiveWithInfer): >>> self.beta = 0.9 >>> def construct(self, grad): >>> out = self.apply_power_sign(self.var, self.m, self.lr, self.logbase, - self.sign_decay, self.beta, grad) + >>> self.sign_decay, self.beta, grad) >>> return out >>> net = Net() >>> grad = Tensor(np.random.rand(3, 3).astype(np.float32)) @@ -5582,7 +5610,7 @@ class BasicLSTMCell(PrimitiveWithInfer): LSTM layer except the last layer. Default 1.0. The range of dropout is [0.0, 1.0]. forget_bias (float): Add forget bias to forget gate biases in order to decrease former scale. Default: 1.0. state_is_tuple (bool): If true, the state is a tuple of 2 tensors, containing h and c; If false, the state is - a tensor and it needs to be split first. Default: True. + a tensor and it needs to be split first. Default: True. activation (str): Activation. Default: "tanh". Only "tanh" is currently supported. Inputs: @@ -5614,12 +5642,20 @@ class BasicLSTMCell(PrimitiveWithInfer): Examples: >>> x = Tensor(np.random.rand(1, 32).astype(np.float16)) - >>> h = Tensor(np.random.rand(1, 64).astype(np.float16)) - >>> c = Tensor(np.random.rand(1, 64).astype(np.float16)) - >>> w = Tensor(np.random.rand(96, 256).astype(np.float16)) - >>> b = Tensor(np.random.rand(256, ).astype(np.float16)) + >>> h = Tensor(np.random.rand(1, 2).astype(np.float16)) + >>> c = Tensor(np.random.rand(1, 2).astype(np.float16)) + >>> w = Tensor(np.random.rand(34, 8).astype(np.float16)) + >>> b = Tensor(np.random.rand(8, ).astype(np.float16)) >>> lstm = P.BasicLSTMCell(keep_prob=1.0, forget_bias=1.0, state_is_tuple=True, activation='tanh') - >>> lstm(x, h, c, w, b) + >>> output = lstm(x, h, c, w, b) + >>> print(output) + ([[9.5459e-01, 9.2725e-01]], + [[1.0000e+00, 1.0000e+00]], + [[1.0000e+00, 1.0000e+00]], + [[1.0000e+00, 1.0000e+00]], + [[9.9951e-01, 1.0000e+00]], + [[9.5459e-01, 9.2773e-01]], + [[0.0000e+00, 0.0000e+00]]) """ @prim_attr_register diff --git a/mindspore/ops/operations/other_ops.py b/mindspore/ops/operations/other_ops.py index b5599c51be..25c39f1572 100644 --- a/mindspore/ops/operations/other_ops.py +++ b/mindspore/ops/operations/other_ops.py @@ -48,7 +48,9 @@ class Assign(PrimitiveWithCheck): >>> return self.y >>> x = Tensor([2.0], mindspore.float32) >>> net = Net() - >>> net(x) + >>> output = net(x) + >>> print(output) + Parameter (name=y) """ __mindspore_signature__ = ( sig.make_sig('variable', sig.sig_rw.RW_WRITE, dtype=sig.sig_dtype.T), diff --git a/mindspore/ops/operations/random_ops.py b/mindspore/ops/operations/random_ops.py index ad4fdee2c1..4c628446bb 100644 --- a/mindspore/ops/operations/random_ops.py +++ b/mindspore/ops/operations/random_ops.py @@ -134,11 +134,14 @@ class Gamma(PrimitiveWithInfer): The dtype is float32. Examples: - >>> shape = (4, 16) + >>> shape = (2, 2) >>> alpha = Tensor(1.0, mstype.float32) >>> beta = Tensor(1.0, mstype.float32) >>> gamma = P.Gamma(seed=3) - >>> output = Gamma(shape, alpha, beta) + >>> output = gamma(shape, alpha, beta) + >>> print(output) + [[0.21962446 0.33740655] + [1.0859369 0.25875127]] """ @prim_attr_register