diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index 9659a84219b81bc7812d472f4fabff796d87d5f0..db9dd71c6176042d6d82579e96cf7a2279bfd6b9 100755 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -1403,7 +1403,7 @@ def conv2d(input, W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1 Args: - input (Variable): The input is 4-D Tensor with shape [N, C, H, W], the data type + input (Tensor): The input is 4-D Tensor with shape [N, C, H, W], the data type of input is float16 or float32 or float64. num_filters(int): The number of filter. It is as same as the output image channel. @@ -1456,9 +1456,9 @@ def conv2d(input, `[batch_size, input_channels, input_height, input_width]`. Returns: - A Variable holding Tensor representing the conv2d, whose data type is the - same with input. If act is None, the tensor variable storing the convolution - result, and if act is not None, the tensor variable storing convolution + A Tensor representing the conv2d, whose data type is the + same with input. If act is None, the tensor storing the convolution + result, and if act is not None, the tensor storing convolution and non-linearity activation result. Raises: @@ -1477,12 +1477,12 @@ def conv2d(input, Examples: .. code-block:: python - import paddle.fluid as fluid import paddle paddle.enable_static() - data = fluid.data(name='data', shape=[None, 3, 32, 32], dtype='float32') - conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3, act="relu") + data = paddle.static.data(name='data', shape=[None, 3, 32, 32], dtype='float32') + conv2d = paddle.static.nn.conv2d(input=data, num_filters=2, filter_size=3, act="relu") + print(conv2d.shape) # [-1, 2, 30, 30] """ check_variable_and_dtype(input, 'input', ['float16', 'float32', 'float64'], @@ -3805,7 +3805,7 @@ def conv2d_transpose(input, conv2d_transpose can compute the kernel size automatically. Args: - input(Variable): 4-D Tensor with [N, C, H, W] or [N, H, W, C] format, + input(Tensor): 4-D Tensor with [N, C, H, W] or [N, H, W, C] format, its data type is float32 or float64. num_filters(int): The number of the filter. It is as same as the output image channel. @@ -3823,15 +3823,14 @@ def conv2d_transpose(input, stride(int|tuple, optional): The stride size. It means the stride in transposed convolution. If stride is a tuple, it must contain two integers, (stride_height, stride_width). Otherwise, stride_height = stride_width = stride. Default: stride = 1. - padding(int|list|str|tuple, optional): The padding size. The padding argument effectively adds - `dilation * (kernel - 1)` amount of zero-padding on both sides of input. If `padding` is a - string, either 'VALID' or 'SAME' supported, which is the padding algorithm. - If `padding` is a tuple or list, it could be in three forms: - `[pad_height, pad_width]` or - `[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`, and - when `data_format` is `'NCHW'`, - `padding` can be in the form `[[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`. - when `data_format` is `'NHWC'`, `padding` can be in the form + padding(str|int|list|tuple, optional): The padding size. It means the number of zero-paddings + on both sides for each dimension. If `padding` is a string, either 'VALID' or + 'SAME' which is the padding algorithm. If `padding` is a tuple or list, + it could be in three forms: `[pad_height, pad_width]` or + `[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`, + and when `data_format` is `"NCHW"`, `padding` can be in the form + `[[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`. + when `data_format` is `"NHWC"`, `padding` can be in the form `[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`. Default: padding = 0. dilation(int|tuple, optional): The dilation size. It means the spacing between the kernel points. @@ -3869,11 +3868,11 @@ def conv2d_transpose(input, `[batch_size, input_channels, input_height, input_width]`. Returns: - A Variable holding Tensor representing the conv2d_transpose, whose + A Tensor representing the conv2d_transpose, whose data type is the same with input and shape is (num_batches, channels, out_h, - out_w) or (num_batches, out_h, out_w, channels). If act is None, the tensor variable + out_w) or (num_batches, out_h, out_w, channels). If act is None, the tensor storing the transposed convolution result, and if act is not None, the - tensor variable storing transposed convolution and non-linearity activation + tensor storing transposed convolution and non-linearity activation result. Raises: @@ -3892,11 +3891,12 @@ def conv2d_transpose(input, Examples: .. code-block:: python - import paddle.fluid as fluid import paddle paddle.enable_static() - data = fluid.data(name='data', shape=[None, 3, 32, 32], dtype='float32') - conv2d_transpose = fluid.layers.conv2d_transpose(input=data, num_filters=2, filter_size=3) + + data = paddle.static.data(name='data', shape=[None, 3, 32, 32], dtype='float32') + conv2d_transpose = paddle.static.nn.conv2d_transpose(input=data, num_filters=2, filter_size=3) + print(conv2d_transpose.shape) # [-1, 2, 34, 34] """ assert param_attr is not False, "param_attr should not be False in conv2d_transpose." if data_format not in ['NCHW', 'NHWC']: diff --git a/python/paddle/fluid/layers/tensor.py b/python/paddle/fluid/layers/tensor.py index 9eeff3751e8ea60e06f09e4a9e4a104b4431281e..0b70010bbee05c91fd26d15f3eae46e9df597951 100644 --- a/python/paddle/fluid/layers/tensor.py +++ b/python/paddle/fluid/layers/tensor.py @@ -203,7 +203,7 @@ def create_global_var(shape, def cast(x, dtype): """ - This OP takes in the Variable :attr:`x` with :attr:`x.dtype` and casts it + This OP takes in the Tensor :attr:`x` with :attr:`x.dtype` and casts it to the output with :attr:`dtype`. It's meaningless if the output dtype equals the input dtype, but it's fine if you do so. @@ -539,20 +539,20 @@ def assign(input, output=None): The OP copies the :attr:`input` to the :attr:`output`. Parameters: - input (Variable|numpy.ndarray): A tensor or numpy ndarray, its data type supports + input (Tensor|numpy.ndarray): A tensor or numpy ndarray, its data type supports float16, float32, float64, int32 and int64. - output (Variable, optional): A tensor. If :attr:`output` is None, a new tensor will + output (Tensor, optional): A tensor. If :attr:`output` is None, a new tensor will be created as :attr:`output`. Default: None. Returns: - Variable: A tensor with the same shape, data type and value as :attr:`input`. + Tensor: A tensor with the same shape, data type and value as :attr:`input`. Examples: .. code-block:: python import paddle import numpy as np - data = paddle.fill_constant(shape=[3, 2], value=2.5, dtype='float64') # [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]] + data = paddle.full(shape=[3, 2], fill_value=2.5, dtype='float64') # [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]] array = np.array([[1, 1], [3, 4], [1, 3]]).astype(np.int64) diff --git a/python/paddle/fluid/param_attr.py b/python/paddle/fluid/param_attr.py index 7d123e7122eeb4a76cb7936511a7bc29575669cf..516181d913e66d0d36fab1b0b64945afb96225fe 100644 --- a/python/paddle/fluid/param_attr.py +++ b/python/paddle/fluid/param_attr.py @@ -37,8 +37,8 @@ class ParamAttr(object): Note: ``gradient_clip`` of ``ParamAttr`` HAS BEEN DEPRECATED since 2.0. Please use ``need_clip`` in ``ParamAttr`` to speficiy the clip scope. - There are three clipping strategies: :ref:`api_paddle_nn_GradientClipByGlobalNorm` , - :ref:`api_fluid_clip_GradientClipByNorm` , :ref:`api_fluid_clip_GradientClipByValue` . + There are three clipping strategies: :ref:`api_paddle_nn_ClipGradByGlobalNorm` , + :ref:`api_paddle_nn_ClipGradByNorm` , :ref:`api_paddle_nn_ClipGradByValue` . Parameters: name (str, optional): The parameter's name. Default None, meaning that the name @@ -50,8 +50,8 @@ class ParamAttr(object): optimize is the global learning rates times the parameter's learning rate times the factor of learning rate scheduler. Default 1.0. regularizer (WeightDecayRegularizer, optional): Regularization strategy. There are two method: - :ref:`api_fluid_regularizer_L1Decay` , :ref:`api_fluid_regularizer_L2Decay` . If - regularizer is also set in ``optimizer`` (such as :ref:`api_fluid_optimizer_SGDOptimizer` ), + :ref:`api_paddle_regularizer_L1Decay` , :ref:`api_paddle_regularizer_L2Decay` . If + regularizer is also set in ``optimizer`` (such as :ref:`api_paddle_optimizer_SGD` ), that regularizer setting in optimizer will be ignored. Default None, meaning there is no regularization. trainable (bool): Whether this parameter is trainable. Default True. @@ -63,7 +63,6 @@ class ParamAttr(object): .. code-block:: python import paddle - paddle.enable_static() weight_attr = paddle.ParamAttr(name="weight", learning_rate=0.5,