diff --git a/python/paddle/distributed/fleet/dataset/dataset.py b/python/paddle/distributed/fleet/dataset/dataset.py index ed06a0db6843f10a0ef2d685b95a32cd0183bd53..907a099f0e8943d8419c175bc66ffff92ce0b96e 100755 --- a/python/paddle/distributed/fleet/dataset/dataset.py +++ b/python/paddle/distributed/fleet/dataset/dataset.py @@ -1493,7 +1493,7 @@ class BoxPSDataset(InMemoryDataset): filelist = ["a.txt", "b.txt"] dataset.set_filelist(filelist) dataset.load_into_memory() - """ + """ self._prepare_to_run() self.boxps.load_into_memory() diff --git a/python/paddle/incubate/nn/layer/fused_transformer.py b/python/paddle/incubate/nn/layer/fused_transformer.py index ba14ac5b8652990fa08539f168f3dbd610e66275..3af26db37a029fa227fdca53e3b62ebf9259a1a2 100644 --- a/python/paddle/incubate/nn/layer/fused_transformer.py +++ b/python/paddle/incubate/nn/layer/fused_transformer.py @@ -684,7 +684,7 @@ class FusedTransformerEncoderLayer(Layer): .. code-block:: python - # required: gpu + # required: gpu import paddle from paddle.incubate.nn import FusedTransformerEncoderLayer diff --git a/python/paddle/incubate/sparse/unary.py b/python/paddle/incubate/sparse/unary.py index 621e31bc3e83440eb9e82a9ba10eb268b339b75f..bb18a5715479fb401135c0a7612c65082b5a40bc 100644 --- a/python/paddle/incubate/sparse/unary.py +++ b/python/paddle/incubate/sparse/unary.py @@ -511,7 +511,7 @@ def coalesce(x): #[[0, 1], [1, 2]] print(sp_x.values()) #[3.0, 3.0] - """ + """ return _C_ops.sparse_coalesce(x) diff --git a/python/paddle/nn/functional/common.py b/python/paddle/nn/functional/common.py index 007f2ee185314a6ae5217b6b1684cdec20ebf71c..34818152f9a59130f52920c45b989855174b072f 100644 --- a/python/paddle/nn/functional/common.py +++ b/python/paddle/nn/functional/common.py @@ -347,23 +347,23 @@ def interpolate(x, Examples: .. code-block:: python - import paddle - import paddle.nn.functional as F - - input_data = paddle.randn(shape=(2,3,6,10)).astype(paddle.float32) - output_1 = F.interpolate(x=input_data, size=[12,12]) - print(output_1.shape) - # [2L, 3L, 12L, 12L] - - # given scale - output_2 = F.interpolate(x=input_data, scale_factor=[2,1]) - print(output_2.shape) - # [2L, 3L, 12L, 10L] - - # bilinear interp - output_3 = F.interpolate(x=input_data, scale_factor=[2,1], mode="bilinear") - print(output_2.shape) - # [2L, 3L, 12L, 10L] + import paddle + import paddle.nn.functional as F + + input_data = paddle.randn(shape=(2,3,6,10)).astype(paddle.float32) + output_1 = F.interpolate(x=input_data, size=[12,12]) + print(output_1.shape) + # [2L, 3L, 12L, 12L] + + # given scale + output_2 = F.interpolate(x=input_data, scale_factor=[2,1]) + print(output_2.shape) + # [2L, 3L, 12L, 10L] + + # bilinear interp + output_3 = F.interpolate(x=input_data, scale_factor=[2,1], mode="bilinear") + print(output_2.shape) + # [2L, 3L, 12L, 10L] """ data_format = data_format.upper() resample = mode.upper() @@ -818,17 +818,17 @@ def upsample(x, or 5-D Tensor of the shape (num_batches, channels, out_d, out_h, out_w) or (num_batches, out_d, out_h, out_w, channels). Examples: - .. code-block:: python + .. code-block:: python - import paddle - import paddle.nn as nn + import paddle + import paddle.nn as nn - input_data = paddle.randn(shape=(2,3,6,10)).astype(paddle.float32) - upsample_out = paddle.nn.Upsample(size=[12,12]) + input_data = paddle.randn(shape=(2,3,6,10)).astype(paddle.float32) + upsample_out = paddle.nn.Upsample(size=[12,12]) - output = upsample_out(x=input_data) - print(output.shape) - # [2L, 3L, 12L, 12L] + output = upsample_out(x=input_data) + print(output.shape) + # [2L, 3L, 12L, 12L] """ return interpolate(x, size, scale_factor, mode, align_corners, align_mode, @@ -842,30 +842,30 @@ def bilinear(x1, x2, weight, bias=None, name=None): See :ref:`api_nn_Bilinear` for details and output shape. Parameters: - x1 (Tensor): the first input tensor, it's data type should be float32, float64. - x2 (Tensor): the second input tensor, it's data type should be float32, float64. - weight (Parameter): The learnable weights of this layer, shape is [out_features, in1_features, in2_features]. - bias (Parameter, optional): The learnable bias(Bias) of this layer, shape is [1, out_features]. If it is set to None, no bias will be added to the output units. The default value is None. - name (str, optional): The default value is None. Normally there is no need for user - to set this property. For more information, please refer to :ref:`api_guide_Name`. Default: None. + x1 (Tensor): the first input tensor, it's data type should be float32, float64. + x2 (Tensor): the second input tensor, it's data type should be float32, float64. + weight (Parameter): The learnable weights of this layer, shape is [out_features, in1_features, in2_features]. + bias (Parameter, optional): The learnable bias(Bias) of this layer, shape is [1, out_features]. If it is set to None, no bias will be added to the output units. The default value is None. + name (str, optional): The default value is None. Normally there is no need for user + to set this property. For more information, please refer to :ref:`api_guide_Name`. Default: None. Returns: - Tensor: A 2-D Tensor of shape [batch_size, out_features]. + Tensor: A 2-D Tensor of shape [batch_size, out_features]. Examples: - .. code-block:: python + .. code-block:: python - import paddle - import paddle.nn.functional as F + import paddle + import paddle.nn.functional as F - x1 = paddle.randn((5, 5)).astype(paddle.float32) - x2 = paddle.randn((5, 4)).astype(paddle.float32) - w = paddle.randn((1000, 5, 4)).astype(paddle.float32) - b = paddle.randn((1, 1000)).astype(paddle.float32) + x1 = paddle.randn((5, 5)).astype(paddle.float32) + x2 = paddle.randn((5, 4)).astype(paddle.float32) + w = paddle.randn((1000, 5, 4)).astype(paddle.float32) + b = paddle.randn((1, 1000)).astype(paddle.float32) - result = F.bilinear(x1, x2, w, b) - print(result.shape) - # [5, 1000] + result = F.bilinear(x1, x2, w, b) + print(result.shape) + # [5, 1000] """ if in_dygraph_mode(): @@ -1008,38 +1008,38 @@ def dropout(x, .. code-block:: python - import paddle - - x = paddle.to_tensor([[1,2,3], [4,5,6]]).astype(paddle.float32) - y_train = paddle.nn.functional.dropout(x, 0.5) - y_test = paddle.nn.functional.dropout(x, 0.5, training=False) - y_0 = paddle.nn.functional.dropout(x, axis=0) - y_1 = paddle.nn.functional.dropout(x, axis=1) - y_01 = paddle.nn.functional.dropout(x, axis=[0,1]) - print(x) - # Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, - # [[1., 2., 3.], - # [4., 5., 6.]]) - print(y_train) - # Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, - # [[2. , 0. , 6. ], - # [8. , 0. , 12.]]) - print(y_test) - # Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, - # [[1., 2., 3.], - # [4., 5., 6.]]) - print(y_0) - # Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, - # [[0. , 0. , 0. ], - # [8. , 10., 12.]]) - print(y_1) - # Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, - # [[2. , 0. , 6. ], - # [8. , 0. , 12.]]) - print(y_01) - # Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, - # [[0. , 0. , 0. ], - # [8. , 0. , 12.]]) + import paddle + + x = paddle.to_tensor([[1,2,3], [4,5,6]]).astype(paddle.float32) + y_train = paddle.nn.functional.dropout(x, 0.5) + y_test = paddle.nn.functional.dropout(x, 0.5, training=False) + y_0 = paddle.nn.functional.dropout(x, axis=0) + y_1 = paddle.nn.functional.dropout(x, axis=1) + y_01 = paddle.nn.functional.dropout(x, axis=[0,1]) + print(x) + # Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, + # [[1., 2., 3.], + # [4., 5., 6.]]) + print(y_train) + # Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, + # [[2. , 0. , 6. ], + # [8. , 0. , 12.]]) + print(y_test) + # Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, + # [[1., 2., 3.], + # [4., 5., 6.]]) + print(y_0) + # Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, + # [[0. , 0. , 0. ], + # [8. , 10., 12.]]) + print(y_1) + # Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, + # [[2. , 0. , 6. ], + # [8. , 0. , 12.]]) + print(y_01) + # Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, + # [[0. , 0. , 0. ], + # [8. , 0. , 12.]]) """ if not isinstance(p, (float, int, Variable)): @@ -1239,14 +1239,14 @@ def dropout3d(x, p=0.5, training=True, data_format='NCDHW', name=None): Examples: .. code-block:: python - import paddle + import paddle - x = paddle.randn(shape=(2, 3, 4, 5, 6)).astype(paddle.float32) - y_train = paddle.nn.functional.dropout3d(x) #train - y_test = paddle.nn.functional.dropout3d(x, training=False) #test - print(x[0,0,:,:,:]) - print(y_train[0,0,:,:,:]) # may all 0 - print(y_test[0,0,:,:,:]) + x = paddle.randn(shape=(2, 3, 4, 5, 6)).astype(paddle.float32) + y_train = paddle.nn.functional.dropout3d(x) #train + y_test = paddle.nn.functional.dropout3d(x, training=False) #test + print(x[0,0,:,:,:]) + print(y_train[0,0,:,:,:]) # may all 0 + print(y_test[0,0,:,:,:]) """ @@ -1287,19 +1287,19 @@ def alpha_dropout(x, p=0.5, training=True, name=None): Examples: .. code-block:: python - import paddle - - x = paddle.to_tensor([[-1, 1], [-1, 1]]).astype(paddle.float32) - y_train = paddle.nn.functional.alpha_dropout(x, 0.5) - y_test = paddle.nn.functional.alpha_dropout(x, 0.5, training=False) - print(y_train) - # Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, - # [[-0.10721093, -0.77919382], - # [-0.10721093, 1.66559887]]) (randomly) - print(y_test) - # Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, - # [[-1., 1.], - # [-1., 1.]]) + import paddle + + x = paddle.to_tensor([[-1, 1], [-1, 1]]).astype(paddle.float32) + y_train = paddle.nn.functional.alpha_dropout(x, 0.5) + y_test = paddle.nn.functional.alpha_dropout(x, 0.5, training=False) + print(y_train) + # Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, + # [[-0.10721093, -0.77919382], + # [-0.10721093, 1.66559887]]) (randomly) + print(y_test) + # Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, + # [[-1., 1.], + # [-1., 1.]]) """ if not isinstance(p, (float, int)): raise TypeError("p argument should be a float or int") diff --git a/python/paddle/nn/functional/loss.py b/python/paddle/nn/functional/loss.py index 8d0d64c7c616bfda404f4a1d91e6a85da2ce6495..31022690da39bbbd5ba0015d4765c0f852175afa 100755 --- a/python/paddle/nn/functional/loss.py +++ b/python/paddle/nn/functional/loss.py @@ -472,10 +472,9 @@ def edit_distance(input, NOTE: This Api is different from fluid.metrics.EditDistance Returns: - Tuple: - - distance(Tensor): edit distance result, its data type is float32, and its shape is (batch_size, 1). - sequence_num(Tensor): sequence number, its data type is float32, and its shape is (1,). + Tuple: + distance(Tensor): edit distance result, its data type is float32, and its shape is (batch_size, 1). + sequence_num(Tensor): sequence number, its data type is float32, and its shape is (1,). Examples: .. code-block:: python @@ -2959,29 +2958,29 @@ def multi_label_soft_margin_loss(input, name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. - Shape: - input: N-D Tensor, the shape is [N, \*], N is batch size and `\*` means number of classes, available dtype is float32, float64. The sum operationoperates over all the elements. - label: N-D Tensor, same shape as the input. - weight:N-D Tensor, the shape is [N,1] - output: scalar. If :attr:`reduction` is ``'none'``, then same shape as the input. + Shape: + input: N-D Tensor, the shape is [N, \*], N is batch size and `\*` means number of classes, available dtype is float32, float64. The sum operationoperates over all the elements. + label: N-D Tensor, same shape as the input. + weight:N-D Tensor, the shape is [N,1] + output: scalar. If :attr:`reduction` is ``'none'``, then same shape as the input. - Returns: - Tensor, The tensor variable storing the multi_label_soft_margin_loss of input and label. + Returns: + Tensor, The tensor variable storing the multi_label_soft_margin_loss of input and label. - Examples: - .. code-block:: python + Examples: + .. code-block:: python - import paddle - import paddle.nn.functional as F - input = paddle.to_tensor([[1, -2, 3], [0, -1, 2], [1, 0, 1]], dtype=paddle.float32) - # label elements in {1., -1.} - label = paddle.to_tensor([[-1, 1, -1], [1, 1, 1], [1, -1, 1]], dtype=paddle.float32) - loss = F.multi_label_soft_margin_loss(input, label, reduction='none') - print(loss) - # Tensor([3.49625897, 0.71111226, 0.43989015]) - loss = F.multi_label_soft_margin_loss(input, label, reduction='mean') - print(loss) - # Tensor([1.54908717]) + import paddle + import paddle.nn.functional as F + input = paddle.to_tensor([[1, -2, 3], [0, -1, 2], [1, 0, 1]], dtype=paddle.float32) + # label elements in {1., -1.} + label = paddle.to_tensor([[-1, 1, -1], [1, 1, 1], [1, -1, 1]], dtype=paddle.float32) + loss = F.multi_label_soft_margin_loss(input, label, reduction='none') + print(loss) + # Tensor([3.49625897, 0.71111226, 0.43989015]) + loss = F.multi_label_soft_margin_loss(input, label, reduction='mean') + print(loss) + # Tensor([1.54908717]) """ if reduction not in ['sum', 'mean', 'none']: raise ValueError( @@ -3266,8 +3265,8 @@ def triplet_margin_with_distance_loss(input, distance_function (callable, optional): Quantifies the distance between two tensors. if not specified, 2 norm functions will be used. - margin (float, optional):Default: :math:`1`.A nonnegative margin representing the minimum difference - between the positive and negative distances required for the loss to be 0. + margin (float, optional): A nonnegative margin representing the minimum difference + between the positive and negative distances required for the loss to be 0. Default value is :math:`1`. swap (bool, optional):The distance swap changes the negative distance to the swap distance (distance between positive samples and negative samples) if swap distance smaller than negative distance. Default: ``False``. diff --git a/python/paddle/nn/layer/loss.py b/python/paddle/nn/layer/loss.py index 7b1415c1a5018ebf91780cc0e74c42e65ff8b247..711174c8a8c6c661f1eb20dd6cf2bc2e2b0da37b 100644 --- a/python/paddle/nn/layer/loss.py +++ b/python/paddle/nn/layer/loss.py @@ -1219,7 +1219,7 @@ class MultiLabelSoftMarginLoss(Layer): :math:`y` and :math:`x` must have the same size. Parameters: - weight (Tensor,optional): a manual rescaling weight given to each class. + weight (Tensor,optional): a manual rescaling weight given to each class. If given, has to be a Tensor of size C and the data type is float32, float64. Default is ``'None'`` . reduction (str, optional): Indicate how to average the loss by batch_size, @@ -1482,7 +1482,7 @@ class TripletMarginWithDistanceLoss(Layer): where the default `distance_function` .. math:: - d(x_i, y_i) = \left\lVert {\bf x}_i - {\bf y}_i \right\rVert_2 + d(x_i, y_i) = \left\lVert {\bf x}_i - {\bf y}_i \right\rVert_2 or user can define their own distance function. `margin` is a nonnegative margin representing the minimum difference between the positive and negative distances that is required for the loss to be 0. If `swap` is true, it will compare distance of (input, negative) with @@ -1510,15 +1510,15 @@ class TripletMarginWithDistanceLoss(Layer): Shapes: input (Tensor):Input tensor, the data type is float32 or float64. - the shape is [N, \*], N is batch size and `\*` means any number of additional dimensions, available dtype is float32, float64. + the shape is [N, \*], N is batch size and `\*` means any number of additional dimensions, available dtype is float32, float64. positive (Tensor):Positive tensor, the data type is float32 or float64. - The shape of label is the same as the shape of input. + The shape of label is the same as the shape of input. negative (Tensor):Negative tensor, the data type is float32 or float64. - The shape of label is the same as the shape of input. + The shape of label is the same as the shape of input. - output(Tensor): The tensor variable storing the triplet_margin_with_distance_loss of input and positive and negative. + output(Tensor): The tensor variable storing the triplet_margin_with_distance_loss of input and positive and negative. Return： A callable object of TripletMarginWithDistanceLoss diff --git a/python/paddle/nn/layer/norm.py b/python/paddle/nn/layer/norm.py index 46b4f6adefd6776a5097f369f11c7e9de8ccf6a7..fe553fd2741453310badc3460cd3be5fecb5ec8b 100644 --- a/python/paddle/nn/layer/norm.py +++ b/python/paddle/nn/layer/norm.py @@ -134,15 +134,15 @@ Where `H` means height of feature map, `W` means width of feature map. numerical stability. Default is 1e-5. momentum(float, optional): The value used for the moving_mean and moving_var computation. Default: 0.9. weight_attr(ParamAttr|bool, optional): The parameter attribute for Parameter `scale` - of instance_norm. If it is set to None or one attribute of ParamAttr, instance_norm - will create ParamAttr as weight_attr, the name of scale can be set in ParamAttr. - If the Initializer of the weight_attr is not set, the parameter is initialized - one. If it is set to False, will not create weight_attr. Default: None. + of instance_norm. If it is set to None or one attribute of ParamAttr, instance_norm + will create ParamAttr as weight_attr, the name of scale can be set in ParamAttr. + If the Initializer of the weight_attr is not set, the parameter is initialized + one. If it is set to False, will not create weight_attr. Default: None. bias_attr(ParamAttr|bool, optional): The parameter attribute for the bias of instance_norm. - If it is set to None or one attribute of ParamAttr, instance_norm - will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr. - If the Initializer of the bias_attr is not set, the bias is initialized zero. - If it is set to False, will not create bias_attr. Default: None. + If it is set to None or one attribute of ParamAttr, instance_norm + will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr. + If the Initializer of the bias_attr is not set, the bias is initialized zero. + If it is set to False, will not create bias_attr. Default: None. data_format(str, optional): Specify the input data format, may be "NC", "NCL". Default "NCL". name(str, optional): Name for the InstanceNorm, default is None. For more information, please refer to :ref:`api_guide_Name`.. @@ -202,15 +202,15 @@ Where `H` means height of feature map, `W` means width of feature map. numerical stability. Default is 1e-5. momentum(float, optional): The value used for the moving_mean and moving_var computation. Default: 0.9. weight_attr(ParamAttr|bool, optional): The parameter attribute for Parameter `scale` - of instance_norm. If it is set to None or one attribute of ParamAttr, instance_norm - will create ParamAttr as weight_attr, the name of scale can be set in ParamAttr. - If the Initializer of the weight_attr is not set, the parameter is initialized - one. If it is set to False, will not create weight_attr. Default: None. + of instance_norm. If it is set to None or one attribute of ParamAttr, instance_norm + will create ParamAttr as weight_attr, the name of scale can be set in ParamAttr. + If the Initializer of the weight_attr is not set, the parameter is initialized + one. If it is set to False, will not create weight_attr. Default: None. bias_attr(ParamAttr|bool, optional): The parameter attribute for the bias of instance_norm. - If it is set to None or one attribute of ParamAttr, instance_norm - will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr. - If the Initializer of the bias_attr is not set, the bias is initialized zero. - If it is set to False, will not create bias_attr. Default: None. + If it is set to None or one attribute of ParamAttr, instance_norm + will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr. + If the Initializer of the bias_attr is not set, the bias is initialized zero. + ` If it is set to False, will not create bias_attr. Default: None. data_format(str, optional): Specify the input data format, could be "NCHW". Default: NCHW. name(str, optional): Name for the InstanceNorm, default is None. For more information, please refer to :ref:`api_guide_Name`.. @@ -226,13 +226,13 @@ Where `H` means height of feature map, `W` means width of feature map. .. code-block:: python - import paddle + import paddle - x = paddle.rand((2, 2, 2, 3)) - instance_norm = paddle.nn.InstanceNorm2D(2) - instance_norm_out = instance_norm(x) + x = paddle.rand((2, 2, 2, 3)) + instance_norm = paddle.nn.InstanceNorm2D(2) + instance_norm_out = instance_norm(x) - print(instance_norm_out) + print(instance_norm_out) """ def _check_input_dim(self, input): @@ -268,15 +268,15 @@ Where `H` means height of feature map, `W` means width of feature map. numerical stability. Default is 1e-5. momentum(float, optional): The value used for the moving_mean and moving_var computation. Default: 0.9. weight_attr(ParamAttr|bool, optional): The parameter attribute for Parameter `scale` - of instance_norm. If it is set to None or one attribute of ParamAttr, instance_norm - will create ParamAttr as weight_attr, the name of scale can be set in ParamAttr. - If the Initializer of the weight_attr is not set, the parameter is initialized - one. If it is set to False, will not create weight_attr. Default: None. + of instance_norm. If it is set to None or one attribute of ParamAttr, instance_norm + will create ParamAttr as weight_attr, the name of scale can be set in ParamAttr. + If the Initializer of the weight_attr is not set, the parameter is initialized + one. If it is set to False, will not create weight_attr. Default: None. bias_attr(ParamAttr|bool, optional): The parameter attribute for the bias of instance_norm. - If it is set to None or one attribute of ParamAttr, instance_norm - will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr. - If the Initializer of the bias_attr is not set, the bias is initialized zero. - If it is set to False, will not create bias_attr. Default: None. + If it is set to None or one attribute of ParamAttr, instance_norm + will create ParamAttr as bias_attr, the name of bias can be set in ParamAttr. + If the Initializer of the bias_attr is not set, the bias is initialized zero. + If it is set to False, will not create bias_attr. Default: None. data_format(str, optional): Specify the input data format, could be "NCDHW". Default: NCDHW. name(str, optional): Name for the InstanceNorm, default is None. For more information, please refer to :ref:`api_guide_Name`.. @@ -292,13 +292,13 @@ Where `H` means height of feature map, `W` means width of feature map. .. code-block:: python - import paddle + import paddle - x = paddle.rand((2, 2, 2, 2, 3)) - instance_norm = paddle.nn.InstanceNorm3D(2) - instance_norm_out = instance_norm(x) + x = paddle.rand((2, 2, 2, 2, 3)) + instance_norm = paddle.nn.InstanceNorm3D(2) + instance_norm_out = instance_norm(x) - print(instance_norm_out.numpy) + print(instance_norm_out.numpy) """ def _check_input_dim(self, input): @@ -318,13 +318,13 @@ class GroupNorm(Layer): num_groups(int): The number of groups that divided from channels. num_channels(int): The number of channels of input. epsilon(float, optional): The small value added to the variance to prevent - division by zero. Default: 1e-05. + division by zero. Default: 1e-05. weight_attr(ParamAttr|bool, optional): The parameter attribute for the learnable - scale :math:`g`. If it is set to False, no scale will be added to the output units. - If it is set to None, the bias is initialized one. Default: None. + scale :math:`g`. If it is set to False, no scale will be added to the output units. + If it is set to None, the bias is initialized one. Default: None. bias_attr(ParamAttr|bool, optional): The parameter attribute for the learnable - bias :math:`b`. If it is set to False, no bias will be added to the output units. - If it is set to None, the bias is initialized zero. Default: None. + bias :math:`b`. If it is set to False, no bias will be added to the output units. + If it is set to None, the bias is initialized zero. Default: None. data_format(str, optional): Specify the input data format. Only NCHW is supported. Default: NCHW. name(str, optional): Name for the GroupNorm, default is None. For more information, please refer to :ref:`api_guide_Name`.. @@ -338,17 +338,17 @@ class GroupNorm(Layer): Examples: .. code-block:: python - import paddle - import numpy as np + import paddle + import numpy as np - paddle.disable_static() - np.random.seed(123) - x_data = np.random.random(size=(2, 6, 2, 2)).astype('float32') - x = paddle.to_tensor(x_data) - group_norm = paddle.nn.GroupNorm(num_channels=6, num_groups=6) - group_norm_out = group_norm(x) + paddle.disable_static() + np.random.seed(123) + x_data = np.random.random(size=(2, 6, 2, 2)).astype('float32') + x = paddle.to_tensor(x_data) + group_norm = paddle.nn.GroupNorm(num_channels=6, num_groups=6) + group_norm_out = group_norm(x) - print(group_norm_out.numpy()) + print(group_norm_out.numpy()) """ def __init__(self, diff --git a/python/paddle/optimizer/adagrad.py b/python/paddle/optimizer/adagrad.py index 99de4243e52717d1b70cdfae187208637051e5e5..180af110ac44edd7c8cae30418dbc8743846027d 100644 --- a/python/paddle/optimizer/adagrad.py +++ b/python/paddle/optimizer/adagrad.py @@ -45,19 +45,19 @@ class Adagrad(Optimizer): It can be a float value or a ``Variable`` with a float type. epsilon (float, optional): A small float value for numerical stability. The default value is 1e-06. - parameters (list|tuple, optional): List/Tuple of ``Tensor`` to update to minimize ``loss``. \ - This parameter is required in dygraph mode. And you can specify different options for \ - different parameter groups such as the learning rate, weight decay, etc, \ - then the parameters are list of dict. Note that the learning_rate in paramter groups \ - represents the scale of base learning_rate. \ - The default value is None in static mode, at this time all parameters will be updated. - weight_decay (float|WeightDecayRegularizer, optional): The strategy of regularization. \ - It canbe a float value as coeff of L2 regularization or \ - :ref:`api_paddle_regularizer_L1Decay`, :ref:`api_paddle_regularizer_L2Decay`. - If a parameter has set regularizer using :ref:`api_paddle_fluid_param_attr_aramAttr` already, \ - the regularization setting here in optimizer will be ignored for this parameter. \ - Otherwise, the regularization setting here in optimizer will take effect. \ - Default None, meaning there is no regularization. + parameters (list|tuple, optional): List/Tuple of ``Tensor`` to update to minimize ``loss``. + This parameter is required in dygraph mode. And you can specify different options for + different parameter groups such as the learning rate, weight decay, etc, + then the parameters are list of dict. Note that the learning_rate in paramter groups + represents the scale of base learning_rate. + The default value is None in static mode, at this time all parameters will be updated. + weight_decay (float|WeightDecayRegularizer, optional): The strategy of regularization. + It canbe a float value as coeff of L2 regularization or + :ref:`api_paddle_regularizer_L1Decay`, :ref:`api_paddle_regularizer_L2Decay`. + If a parameter has set regularizer using :ref:`api_paddle_fluid_param_attr_aramAttr` already, + the regularization setting here in optimizer will be ignored for this parameter. + Otherwise, the regularization setting here in optimizer will take effect. + Default None, meaning there is no regularization. grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of some derived class of ``GradientClipBase`` . There are three cliping strategies, ClipGradByGlobalNorm, ClipGradByNorm and ClipGradByValue. Default None, diff --git a/python/paddle/optimizer/adam.py b/python/paddle/optimizer/adam.py index 26d082690b7b7e2c7652f6178f6c40ff0ced6f95..4f8122121b62c0b960ef79ca02b6d5befea7e237 100644 --- a/python/paddle/optimizer/adam.py +++ b/python/paddle/optimizer/adam.py @@ -67,19 +67,19 @@ class Adam(Optimizer): epsilon (float|Tensor, optional): A small float value for numerical stability. It should be a float number or a Tensor with shape [1] and data type as float32. The default value is 1e-08. - parameters (list|tuple, optional): List/Tuple of ``Tensor`` to update to minimize ``loss``. \ - This parameter is required in dygraph mode. And you can specify different options for \ - different parameter groups such as the learning rate, weight decay, etc, \ - then the parameters are list of dict. Note that the learning_rate in paramter groups \ - represents the scale of base learning_rate. \ - The default value is None in static mode, at this time all parameters will be updated. - weight_decay (float|WeightDecayRegularizer, optional): The strategy of regularization. \ - It canbe a float value as coeff of L2 regularization or \ - :ref:`api_fluid_regularizer_L1Decay`, :ref:`api_fluid_regularizer_L2Decay`. - If a parameter has set regularizer using :ref:`api_fluid_ParamAttr` already, \ - the regularization setting here in optimizer will be ignored for this parameter. \ - Otherwise, the regularization setting here in optimizer will take effect. \ - Default None, meaning there is no regularization. + parameters (list|tuple, optional): List/Tuple of ``Tensor`` to update to minimize ``loss``. + This parameter is required in dygraph mode. And you can specify different options for + different parameter groups such as the learning rate, weight decay, etc, + then the parameters are list of dict. Note that the learning_rate in paramter groups + represents the scale of base learning_rate. + The default value is None in static mode, at this time all parameters will be updated. + weight_decay (float|WeightDecayRegularizer, optional): The strategy of regularization. + It canbe a float value as coeff of L2 regularization or + :ref:`api_fluid_regularizer_L1Decay`, :ref:`api_fluid_regularizer_L2Decay`. + If a parameter has set regularizer using :ref:`api_fluid_ParamAttr` already, + the regularization setting here in optimizer will be ignored for this parameter. + Otherwise, the regularization setting here in optimizer will take effect. + Default None, meaning there is no regularization. grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of some derived class of ``GradientClipBase`` . There are three cliping strategies ( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` , diff --git a/python/paddle/optimizer/adamax.py b/python/paddle/optimizer/adamax.py index 03f766e646da94342ee0b2fbeabb66a6f5f53a6a..86b4ed97d1c1a60d94fae78c24cbb413c2c47e51 100644 --- a/python/paddle/optimizer/adamax.py +++ b/python/paddle/optimizer/adamax.py @@ -57,19 +57,19 @@ class Adamax(Optimizer): The default value is 0.999. epsilon (float, optional): A small float value for numerical stability. The default value is 1e-08. - parameters (list|tuple, optional): List/Tuple of ``Tensor`` to update to minimize ``loss``. \ - This parameter is required in dygraph mode. And you can specify different options for \ - different parameter groups such as the learning rate, weight decay, etc, \ - then the parameters are list of dict. Note that the learning_rate in paramter groups \ - represents the scale of base learning_rate. \ - The default value is None in static mode, at this time all parameters will be updated. - weight_decay (float|WeightDecayRegularizer, optional): The strategy of regularization. \ - It canbe a float value as coeff of L2 regularization or \ - :ref:`api_fluid_regularizer_L1Decay`, :ref:`api_fluid_regularizer_L2Decay`. - If a parameter has set regularizer using :ref:`api_fluid_ParamAttr` already, \ - the regularization setting here in optimizer will be ignored for this parameter. \ - Otherwise, the regularization setting here in optimizer will take effect. \ - Default None, meaning there is no regularization. + parameters (list|tuple, optional): List/Tuple of ``Tensor`` to update to minimize ``loss``. + This parameter is required in dygraph mode. And you can specify different options for + different parameter groups such as the learning rate, weight decay, etc, + then the parameters are list of dict. Note that the learning_rate in paramter groups + represents the scale of base learning_rate. + The default value is None in static mode, at this time all parameters will be updated. + weight_decay (float|WeightDecayRegularizer, optional): The strategy of regularization. + It canbe a float value as coeff of L2 regularization or + :ref:`api_fluid_regularizer_L1Decay`, :ref:`api_fluid_regularizer_L2Decay`. + If a parameter has set regularizer using :ref:`api_fluid_ParamAttr` already, + the regularization setting here in optimizer will be ignored for this parameter. + Otherwise, the regularization setting here in optimizer will take effect. + Default None, meaning there is no regularization. grad_clip (GradientClipBase, optional): Gradient cliping strategy, it's an instance of some derived class of ``GradientClipBase`` . There are three cliping strategies ( :ref:`api_fluid_clip_GradientClipByGlobalNorm` , :ref:`api_fluid_clip_GradientClipByNorm` , diff --git a/python/paddle/optimizer/adamw.py b/python/paddle/optimizer/adamw.py index 4c13b8f78974b8e1ed62fea3153193b58e4aa687..1c0dbb3134c809b299824906f265e48b34018f49 100644 --- a/python/paddle/optimizer/adamw.py +++ b/python/paddle/optimizer/adamw.py @@ -54,12 +54,12 @@ class AdamW(Optimizer): Args: learning_rate (float|LRScheduler, optional): The learning rate used to update ``Parameter``. It can be a float value or a LRScheduler. The default value is 0.001. - parameters (list|tuple, optional): List/Tuple of ``Tensor`` names to update to minimize ``loss``. \ - This parameter is required in dygraph mode. And you can specify different options for \ - different parameter groups such as the learning rate, weight decay, etc, \ - then the parameters are list of dict. Note that the learning_rate in paramter groups \ - represents the scale of base learning_rate. \ - The default value is None in static mode, at this time all parameters will be updated. + parameters (list|tuple, optional): List/Tuple of ``Tensor`` names to update to minimize ``loss``. + This parameter is required in dygraph mode. And you can specify different options for + different parameter groups such as the learning rate, weight decay, etc, + then the parameters are list of dict. Note that the learning_rate in paramter groups + represents the scale of base learning_rate. + The default value is None in static mode, at this time all parameters will be updated. beta1 (float|Tensor, optional): The exponential decay rate for the 1st moment estimates. It should be a float number or a Tensor with shape [1] and data type as float32. The default value is 0.9. diff --git a/python/paddle/tensor/attribute.py b/python/paddle/tensor/attribute.py index d480f24a68b680914c3b969a39664607a1ba19a5..79b7e2dc9d9dab4d0954a53c0ddc53b96566cb76 100644 --- a/python/paddle/tensor/attribute.py +++ b/python/paddle/tensor/attribute.py @@ -63,12 +63,6 @@ def rank(input): def shape(input): """ - :alias_main: paddle.shape - :alias: paddle.shape,paddle.tensor.shape,paddle.tensor.attribute.shape - :old_api: paddle.fluid.layers.shape - - **Shape Layer** - Get the shape of the input. .. code-block:: text diff --git a/python/paddle/tensor/manipulation.py b/python/paddle/tensor/manipulation.py index e0076c8964ddac6ba28a558ec0c9a6db5beee9cd..8f72efbfffe6a6272f1f1a717989cf25d0b89b6f 100644 --- a/python/paddle/tensor/manipulation.py +++ b/python/paddle/tensor/manipulation.py @@ -416,12 +416,6 @@ def transpose(x, perm, name=None): def unstack(x, axis=0, num=None): """ - :alias_main: paddle.unstack - :alias: paddle.unstack,paddle.tensor.unstack,paddle.tensor.manipulation.unstack - :old_api: paddle.fluid.layers.unstack - - **UnStack Layer** - This layer unstacks input Tensor :code:`x` into several Tensors along :code:`axis`. If :code:`axis` < 0, it would be replaced with :code:`axis+rank(x)`. @@ -1693,12 +1687,12 @@ def stack(x, axis=0, name=None): # [[3., 4.]], # [[5., 6.]]] - out = paddle.stack([x1, x2, x3], axis=-2) - print(out.shape) # [1, 3, 2] - print(out) - # [[[1., 2.], - # [3., 4.], - # [5., 6.]]] + out = paddle.stack([x1, x2, x3], axis=-2) + print(out.shape) # [1, 3, 2] + print(out) + # [[[1., 2.], + # [3., 4.], + # [5., 6.]]] """ axis = 0 if axis is None else axis @@ -2663,7 +2657,7 @@ def scatter(x, index, updates, overwrite=True, name=None): overwrite (bool): The mode that updating the output when there are same indices. If True, use the overwrite mode to update the output of the same index, - if False, use the accumulate mode to update the output of the same index.Default value is True. + if False, use the accumulate mode to update the output of the same index.Default value is True. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . diff --git a/python/paddle/text/datasets/conll05.py b/python/paddle/text/datasets/conll05.py index 119ce9fea51e160a453e62153b00305bb3abc619..862b0d0a4cbd3e3d39729badadf278e07823ba63 100644 --- a/python/paddle/text/datasets/conll05.py +++ b/python/paddle/text/datasets/conll05.py @@ -302,10 +302,10 @@ class Conll05st(Dataset): .. code-block:: python - from paddle.text.datasets import Conll05st + from paddle.text.datasets import Conll05st - conll05st = Conll05st() - word_dict, predicate_dict, label_dict = conll05st.get_dict() + conll05st = Conll05st() + word_dict, predicate_dict, label_dict = conll05st.get_dict() """ return self.word_dict, self.predicate_dict, self.label_dict @@ -317,9 +317,9 @@ class Conll05st(Dataset): .. code-block:: python - from paddle.text.datasets import Conll05st + from paddle.text.datasets import Conll05st - conll05st = Conll05st() - emb_file = conll05st.get_embedding() + conll05st = Conll05st() + emb_file = conll05st.get_embedding() """ return self.emb_file