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“1ee9037fce5651b0fdea60f30f733c3bb9962c06”上不存在“develop/doc/api/v2/config/activation.html”
未验证 提交 d4199901 编写于 作者: L lvmengsi 提交者： GitHub
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refine en doc (#20088) (#20215) 

test=release/1.6, test=document_fix

* update en doc
上级 061cb34f
隐藏空白更改
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Showing with 178 addition and 133 deletion
paddle/fluid/API.spec
浏览文件 @ d4199901
......@@ -134,14 +134,14 @@ paddle.fluid.layers.dynamic_gru (ArgSpec(args=['input', 'size', 'param_attr', 'b
paddle.fluid.layers.gru_unit (ArgSpec(args=['input', 'hidden', 'size', 'param_attr', 'bias_attr', 'activation', 'gate_activation', 'origin_mode'], varargs=None, keywords=None, defaults=(None, None, 'tanh', 'sigmoid', False)), ('document', '33974b9bfa69f2f1eb85e6f956dff04e'))
paddle.fluid.layers.linear_chain_crf (ArgSpec(args=['input', 'label', 'param_attr', 'length'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'bc7a0fd2bb2b35dfd2f54947320e78fa'))
paddle.fluid.layers.crf_decoding (ArgSpec(args=['input', 'param_attr', 'label', 'length'], varargs=None, keywords=None, defaults=(None, None)), ('document', '933b7e268c4ffa3d5c3ef953a5ee9f0b'))
paddle.fluid.layers.cos_sim (ArgSpec(args=['X', 'Y'], varargs=None, keywords=None, defaults=None), ('document', '8e6ce424cf9e261ef32ee229c06a6e66'))
paddle.fluid.layers.cos_sim (ArgSpec(args=['X', 'Y'], varargs=None, keywords=None, defaults=None), ('document', '07bb25484c98d529fbe67338422724af'))
paddle.fluid.layers.cross_entropy (ArgSpec(args=['input', 'label', 'soft_label', 'ignore_index'], varargs=None, keywords=None, defaults=(False, -100)), ('document', '789a141e97fd0b37241f630935936d08'))
paddle.fluid.layers.bpr_loss (ArgSpec(args=['input', 'label', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6263dfdeb6c670fa0922c9cbc8fb1bf4'))
paddle.fluid.layers.square_error_cost (ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None), ('document', 'bbb9e708bab250359864fefbdf48e9d9'))
paddle.fluid.layers.chunk_eval (ArgSpec(args=['input', 'label', 'chunk_scheme', 'num_chunk_types', 'excluded_chunk_types', 'seq_length'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'b02844e0ad4bd713c5fe6802aa13219c'))
paddle.fluid.layers.sequence_conv (ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'padding_start', 'bias_attr', 'param_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(3, 1, True, None, None, None, None, None)), ('document', '2bf23e7884c380c3b27f2709aa322cb9'))
paddle.fluid.layers.conv2d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name', 'data_format'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None, 'NCHW')), ('document', 'b8da17862ba02b5297a37d2edd571d76'))
paddle.fluid.layers.conv3d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name', 'data_format'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None, 'NCDHW')), ('document', '73a15322d460ef9aa90d4d237b0bc5d5'))
paddle.fluid.layers.conv2d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name', 'data_format'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None, 'NCHW')), ('document', 'b9be3712a46e196c7329eed52ed91d05'))
paddle.fluid.layers.conv3d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name', 'data_format'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None, 'NCDHW')), ('document', 'a7e4573745c40b8b1d726709f209b6e4'))
paddle.fluid.layers.sequence_pool (ArgSpec(args=['input', 'pool_type', 'is_test', 'pad_value'], varargs=None, keywords=None, defaults=(False, 0.0)), ('document', 'e90a93251c52dc4e6fb34fb3991b3f82'))
paddle.fluid.layers.sequence_softmax (ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', 'eaa9d0bbd3d4e017c8bc4ecdac483711'))
paddle.fluid.layers.softmax (ArgSpec(args=['input', 'use_cudnn', 'name', 'axis'], varargs=None, keywords=None, defaults=(False, None, -1)), ('document', 'cee673c79e3ff4582656a24e04f841e5'))
......@@ -149,12 +149,12 @@ paddle.fluid.layers.pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'po
paddle.fluid.layers.pool3d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name', 'exclusive', 'data_format'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None, True, 'NCDHW')), ('document', 'db0035a3132b1dfb12e53c57591fb9f6'))
paddle.fluid.layers.adaptive_pool2d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None)), ('document', '52343203de40afe29607397e13aaf0d2'))
paddle.fluid.layers.adaptive_pool3d (ArgSpec(args=['input', 'pool_size', 'pool_type', 'require_index', 'name'], varargs=None, keywords=None, defaults=('max', False, None)), ('document', '55db6ae7275fb9678a6814aebab81a9c'))
paddle.fluid.layers.batch_norm (ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu', 'use_global_stats'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, None, None, None, False, False, False)), ('document', '9e5a9f4f6d82d34a33d9ca632379cbcc'))
paddle.fluid.layers.instance_norm (ArgSpec(args=['input', 'epsilon', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(1e-05, None, None, None)), ('document', '02972097e089629efdb0ed9404fd36ae'))
paddle.fluid.layers.batch_norm (ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu', 'use_global_stats'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, None, None, None, False, False, False)), ('document', 'b88a2a2d5de3e6d845d134782fb54857'))
paddle.fluid.layers.instance_norm (ArgSpec(args=['input', 'epsilon', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(1e-05, None, None, None)), ('document', '5e2d18e85599ede7e71b06ed64d0f69e'))
paddle.fluid.layers.data_norm (ArgSpec(args=['input', 'act', 'epsilon', 'param_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var'], varargs=None, keywords=None, defaults=(None, 1e-05, None, 'NCHW', False, None, None, None, False)), ('document', '2460b30fb87037555208fa8ac6fc1787'))
paddle.fluid.layers.beam_search_decode (ArgSpec(args=['ids', 'scores', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '83e08f21af41ac8bac37aeab1f86fdd0'))
paddle.fluid.layers.conv2d_transpose (ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name', 'data_format'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None, 'NCHW')), ('document', '9391d75358b6cba0cc5d22a01a223420'))
paddle.fluid.layers.conv3d_transpose (ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name', 'data_format'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None, 'NCDHW')), ('document', '74bce3cd4224e6ff133d54508dc7f150'))
paddle.fluid.layers.conv2d_transpose (ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name', 'data_format'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None, 'NCHW')), ('document', '0ca6c549ac2b63096bdc7832a08b4431'))
paddle.fluid.layers.conv3d_transpose (ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name', 'data_format'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None, 'NCDHW')), ('document', '709d7ca3a94f52a253d15b06aafb1bd0'))
paddle.fluid.layers.sequence_expand (ArgSpec(args=['x', 'y', 'ref_level', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '10e122eb755c2bd1f78ef2332b28f1a0'))
paddle.fluid.layers.sequence_expand_as (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '858c432e7cbd8bb952cc2eb555457d50'))
paddle.fluid.layers.sequence_pad (ArgSpec(args=['x', 'pad_value', 'maxlen', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'df08b9c499ab3a90f95d08ab5b6c6c62'))
......@@ -170,7 +170,7 @@ paddle.fluid.layers.reduce_any (ArgSpec(args=['input', 'dim', 'keep_dim', 'name'
paddle.fluid.layers.sequence_first_step (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', 'f2dfd65b859de9844e7261e7a4503f63'))
paddle.fluid.layers.sequence_last_step (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '1af2e3a887e4f914f9d6650406186ab6'))
paddle.fluid.layers.sequence_slice (ArgSpec(args=['input', 'offset', 'length', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '39fbc5437be389f6c0c769f82fc1fba2'))
paddle.fluid.layers.dropout (ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name', 'dropout_implementation'], varargs=None, keywords=None, defaults=(False, None, None, 'downgrade_in_infer')), ('document', '558d13133596209190df9a624264f28f'))
paddle.fluid.layers.dropout (ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name', 'dropout_implementation'], varargs=None, keywords=None, defaults=(False, None, None, 'downgrade_in_infer')), ('document', '4fd396b6cf16bb4ef2a56d695d0ad941'))
paddle.fluid.layers.split (ArgSpec(args=['input', 'num_or_sections', 'dim', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '78cf3a7323d1a7697658242e13f63759'))
paddle.fluid.layers.ctc_greedy_decoder (ArgSpec(args=['input', 'blank', 'input_length', 'padding_value', 'name'], varargs=None, keywords=None, defaults=(None, 0, None)), ('document', '9abb7bb8d267e017620a39a146dc47ea'))
paddle.fluid.layers.edit_distance (ArgSpec(args=['input', 'label', 'normalized', 'ignored_tokens', 'input_length', 'label_length'], varargs=None, keywords=None, defaults=(True, None, None, None)), ('document', '77cbfb28cd2fc589f589c7013c5086cd'))
......@@ -297,7 +297,7 @@ paddle.fluid.layers.prroi_pool (ArgSpec(args=['input', 'rois', 'output_channels'
paddle.fluid.layers.teacher_student_sigmoid_loss (ArgSpec(args=['input', 'label', 'soft_max_up_bound', 'soft_max_lower_bound'], varargs=None, keywords=None, defaults=(15.0, -15.0)), ('document', '07cb0d95a646dba1b9cc7cdce89e59f0'))
paddle.fluid.layers.huber_loss (ArgSpec(args=['input', 'label', 'delta'], varargs=None, keywords=None, defaults=None), ('document', '11bb8e62cc9256958eff3991fe4834da'))
paddle.fluid.layers.kldiv_loss (ArgSpec(args=['x', 'target', 'reduction', 'name'], varargs=None, keywords=None, defaults=('mean', None)), ('document', '18bc95c62d3300456c3c7da5278b47bb'))
paddle.fluid.layers.npair_loss (ArgSpec(args=['anchor', 'positive', 'labels', 'l2_reg'], varargs=None, keywords=None, defaults=(0.002,)), ('document', '6b6ee1170fe20a79cf0631a1f49b0df2'))
paddle.fluid.layers.npair_loss (ArgSpec(args=['anchor', 'positive', 'labels', 'l2_reg'], varargs=None, keywords=None, defaults=(0.002,)), ('document', 'a41a93253c937697e900e19af172490d'))
paddle.fluid.layers.pixel_shuffle (ArgSpec(args=['x', 'upscale_factor'], varargs=None, keywords=None, defaults=None), ('document', '7e5cac851fd9bad344230e1044b6a565'))
paddle.fluid.layers.fsp_matrix (ArgSpec(args=['x', 'y'], varargs=None, keywords=None, defaults=None), ('document', '20992b20d19c2e5983f366150827b4a6'))
paddle.fluid.layers.continuous_value_model (ArgSpec(args=['input', 'cvm', 'use_cvm'], varargs=None, keywords=None, defaults=(True,)), ('document', 'c03490ffaa1b78258747157c313db4cd'))
......@@ -907,7 +907,7 @@ paddle.fluid.nets.simple_img_conv_pool (ArgSpec(args=['input', 'num_filters', 'f
paddle.fluid.nets.sequence_conv_pool (ArgSpec(args=['input', 'num_filters', 'filter_size', 'param_attr', 'act', 'pool_type', 'bias_attr'], varargs=None, keywords=None, defaults=(None, 'sigmoid', 'max', None)), ('document', 'd6a1e527b53f5cc15594fee307dfc5cf'))
paddle.fluid.nets.glu (ArgSpec(args=['input', 'dim'], varargs=None, keywords=None, defaults=(-1,)), ('document', 'b87bacfc70dd3477ed25ef14aa01389a'))
paddle.fluid.nets.scaled_dot_product_attention (ArgSpec(args=['queries', 'keys', 'values', 'num_heads', 'dropout_rate'], varargs=None, keywords=None, defaults=(1, 0.0)), ('document', 'b1a07a0000eb9103e3a143ca8c13de5b'))
paddle.fluid.nets.img_conv_group (ArgSpec(args=['input', 'conv_num_filter', 'pool_size', 'conv_padding', 'conv_filter_size', 'conv_act', 'param_attr', 'conv_with_batchnorm', 'conv_batchnorm_drop_rate', 'pool_stride', 'pool_type', 'use_cudnn'], varargs=None, keywords=None, defaults=(1, 3, None, None, False, 0.0, 1, 'max', True)), ('document', '4913d846264f17112bf7bc04273388cc'))
paddle.fluid.nets.img_conv_group (ArgSpec(args=['input', 'conv_num_filter', 'pool_size', 'conv_padding', 'conv_filter_size', 'conv_act', 'param_attr', 'conv_with_batchnorm', 'conv_batchnorm_drop_rate', 'pool_stride', 'pool_type', 'use_cudnn'], varargs=None, keywords=None, defaults=(1, 3, None, None, False, 0.0, 1, 'max', True)), ('document', '591a48aa9d871896aa8548c977c4c120'))
paddle.fluid.optimizer.SGDOptimizer ('paddle.fluid.optimizer.SGDOptimizer', ('document', 'c3c8dd3193d991adf8bda505560371d6'))
paddle.fluid.optimizer.SGDOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'regularization', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.SGDOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', '80ea99c9af7ef5fac7e57fb302103610'))
......
paddle/fluid/operators/cos_sim_op.cc
浏览文件 @ d4199901
......@@ -73,8 +73,12 @@ class CosSimOp : public framework::OperatorWithKernel {
class CosSimOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "The 1st input of cos_sim op.");
AddInput("Y", "The 2nd input of cos_sim op.");
AddInput("X",
"The 1st input of cos_sim op, LoDTensor with shape ``[N_1, N_2, "
"..., N_k]``, the data type is float32.");
AddInput("Y",
"The 2nd input of cos_sim op, Tensor with shape ``[N_1 or 1, N_2, "
"..., N_k]``, the data type is float32.");
AddOutput("Out", "The output of cos_sim op.");
AddOutput("XNorm",
"Norm of the first input, reduced along the 1st "
......
python/paddle/fluid/layers/nn.py
浏览文件 @ d4199901
......@@ -1595,7 +1595,7 @@ def cos_sim(X, Y):
Y (Variable): ${y_comment}.
Returns:
Variable: the output of cosine(X, Y).
A Variable holding LoDTensor representing the output of cosine(X, Y).
Examples:
.. code-block:: python
......@@ -1637,13 +1637,13 @@ def dropout(x,
dropout op can be removed from the program to make the program more efficient.
Args:
x (Variable): The input tensor variable.
x (Variable): The input tensor variable. The data type is float16 or float32 or float64.
dropout_prob (float): Probability of setting units to zero.
is_test (bool): A flag indicating whether it is in test phrase or not.
seed (int): A Python integer used to create random seeds. If this
parameter is set to None, a random seed is used.
NOTE: If an integer seed is given, always the same output
units will be dropped. DO NOT use a fixed seed in training.
units will be dropped. DO NOT use a fixed seed in training.Default: None.
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
dropout_implementation(string): ['downgrade_in_infer'(default)|'upscale_in_train']
......@@ -1665,7 +1665,7 @@ def dropout(x,
Returns:
Variable: A tensor variable is the shape with `x`.
A Variable holding Tensor representing the dropout, has same shape and data type with `x`.
Examples:
......@@ -2358,31 +2358,32 @@ def conv2d(input,
H_{out}&= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\\\
W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1
Note:
padding mode is 'SAME' and 'VALID' can reference this link<https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleGAN/network/base_network.py#L181>`_
Args:
input (Variable): The input image with [N, C, H, W] or [N, H, W, C] format.
input (Variable): 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.
filter_size (int|tuple): The filter size. If filter_size
is a tuple, it must contain two integers, (filter_size_height,
filter_size_width). Otherwise, filter_size_height = filter_\
size_width = filter_size.
stride (int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_height, stride_width). Otherwise,
stride_height = stride_width = stride. Default: stride = 1.
padding (string|int|list|tuple): The padding size. If `padding` is a string, either 'VALID' or
filter_size_width). Otherwise, filter_size_height = filter_size_width =\
filter_size.
stride (int|tuple): The stride size. It means the stride in 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 (string|int|list|tuple): The padding size. It means the number of zero-paddings
on both sides for each dimention.If `padding` is a string, either 'VALID' or
'SAME' which is the padding algorithm. If padding size 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]]`.
`[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"`, `pool_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): The dilation size. If dilation is a tuple, it must
contain two integers, (dilation_height, dilation_width). Otherwise,
dilation_height = dilation_width = dilation. Default: dilation = 1.
dilation (int|tuple): The dilation size. It means the spacing between the kernel
points. If dilation is a tuple, it must contain two integers, (dilation_height,
dilation_width). Otherwise, dilation_height = dilation_width = dilation.
Default: dilation = 1.
groups (int): The groups number of the Conv2d Layer. According to grouped
convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
the first half of the filters is only connected to the first half
......@@ -2402,19 +2403,18 @@ def conv2d(input,
library is installed. Default: True
act (str): Activation type, if it is set to None, activation is not appended.
Default: None
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically. Default: None.
name(str|None): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
data_format (str): The data format of the input and output data. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`.
Returns:
Variable: The tensor variable storing the convolution and \
non-linearity activation result.
Raises:
ValueError: If the shapes of input, filter_size, stride, padding and
groups mismatch.
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
and non-linearity activation result.
Examples:
.. code-block:: python
......@@ -2575,8 +2575,6 @@ def conv3d(input,
name=None,
data_format="NCDHW"):
"""
**Convlution3D Layer**
The convolution3D layer calculates the output based on the input, filter
and strides, paddings, dilations, groups parameters. Input(Input) and
Output(Output) are in NCDHW or NDHWC format. Where N is batch size C is the number of
......@@ -2621,17 +2619,19 @@ def conv3d(input,
W_{out}&= \\frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{strides[2]} + 1
Args:
input (Variable): The input image with [N, C, D, H, W] or [N, D, H, W, C]format.
input (Variable): The input is 5-D Tensor with shape [N, C, D, 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.
filter_size (int|tuple): The filter size. If filter_size is a tuple,
it must contain three integers, (filter_size_depth, filter_size_height,
filter_size_width). Otherwise, filter_size_depth = filter_size_height = \
filter_size_width = filter_size.
stride (int|tuple): The stride size. If stride is a tuple, it must
contain three integers, (stride_depth, stride_height, stride_width). Otherwise,
stride_depth = stride_height = stride_width = stride. Default: stride = 1.
padding (string|int|list|tuple): The padding size. f `padding` is a string, either 'VALID' or
stride (int|tuple): The stride size. It means the stride in convolution. If stride is a
tuple, it must contain three integers, (stride_depth, stride_height, stride_width).
Otherwise, stride_depth = stride_height = stride_width = stride. Default: stride = 1.
padding (string|int|list|tuple): The padding size. It means the number of zero-paddings
on both sides for each dimention. If `padding` is a string, either 'VALID' or
'SAME' which is the padding algorithm. If padding size is a tuple or list,
it could be in three forms: `[pad_depth, pad_height, pad_width]` or
`[pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`,
......@@ -2640,9 +2640,10 @@ def conv3d(input,
when `data_format` is `"NDHWC"`, `pool_padding` can be in the form
`[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
Default: padding = 0.
dilation (int|tuple): The dilation size. If dilation is a tuple, it must
contain three integers, (dilation_depth, dilation_height, dilation_width). Otherwise,
dilation_depth = dilation_height = dilation_width = dilation. Default: dilation = 1.
dilation (int|tuple): The dilation size. It means the spacing between the kernel points.
If dilation is a tuple, it must contain three integers, (dilation_depth, dilation_height,
dilation_width). Otherwise, dilation_depth = dilation_height = dilation_width = dilation.
Default: dilation = 1.
groups (int): The groups number of the Conv3d Layer. According to grouped
convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
the first half of the filters is only connected to the first half
......@@ -2662,19 +2663,18 @@ def conv3d(input,
library is installed. Default: True
act (str): Activation type, if it is set to None, activation is not appended.
Default: None.
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically. Default: None.
name(str|None): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
data_format (str): The data format of the input and output data. An optional string from: `"NCDHW"`, `"NDHWC"`.
The default is `"NCDHW"`. When it is `"NCDHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_depth, input_height, input_width]`.
Returns:
Variable: The tensor variable storing the convolution and \
non-linearity activation result.
Raises:
ValueError: If the shapes of input, filter_size, stride, padding and
groups mismatch.
A Variable holding Tensor representing the conv3d, 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 and non-linearity activation result.
Examples:
.. code-block:: python
......@@ -3683,7 +3683,7 @@ def batch_norm(input,
"""
**Batch Normalization Layer**
Can be used as a normalizer function for conv2d and fully_connected operations.
Can be used as a normalizer function for convolution or fully_connected operations.
The required data format for this layer is one of the following:
1. NHWC `[batch, in_height, in_width, in_channels]`
......@@ -3706,10 +3706,11 @@ def batch_norm(input,
\\sigma_{\\beta}^{2} + \\epsilon}} \\qquad &//\ normalize \\\\
y_i &\\gets \\gamma \\hat{x_i} + \\beta \\qquad &//\ scale\ and\ shift
moving\_mean = moving\_mean * momentum + mini-batch\_mean * (1. - momentum)
moving\_var = moving\_var * momentum + mini-batch\_var * (1. - momentum)
moving_mean and moving_var is global mean and global variance.
moving\_mean = moving\_mean * momentum + mini-batch\_mean * (1. - momentum) \\\\
moving\_var = moving\_var * momentum + mini-batch\_var * (1. - momentum)
moving_mean is global mean and moving_var is global variance.
When use_global_stats = True, the :math:`\\mu_{\\beta}`
and :math:`\\sigma_{\\beta}^{2}` are not the statistics of one mini-batch.
......@@ -3728,7 +3729,8 @@ def batch_norm(input,
sync_batch_norm automatically.
Args:
input(variable): The rank of input variable can be 2, 3, 4, 5.
input(variable): The rank of input variable can be 2, 3, 4, 5. The data type
is float16 or float32 or float64.
act(string, Default None): Activation type, linear|relu|prelu|...
is_test (bool, Default False): A flag indicating whether it is in
test phrase or not.
......@@ -3749,14 +3751,14 @@ def batch_norm(input,
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.
Default: None.
data_layout(string, default NCHW): NCHW|NHWC
data_layout(str, default NCHW): the data_layout of input, is NCHW or NHWC.
in_place(bool, Default False): Make the input and output of batch norm reuse memory.
name(string, Default None): A name for this layer(optional). If set None, the layer
will be named automatically.
moving_mean_name(string, Default None): The name of moving_mean which store the global Mean. If it
name(str|None): For detailed information, please refer to :ref:`api_guide_Name`.
Usually name is no need to set and None by default.
moving_mean_name(str, Default None): The name of moving_mean which store the global Mean. If it
is set to None, batch_norm will save global mean with a random name, otherwise, batch_norm
will save global mean with the string.
moving_variance_name(string, Default None): The name of the moving_variance which store the global Variance.
moving_variance_name(str, Default None): The name of the moving_variance which store the global Variance.
If it is set to None, batch_norm will save global variance with a random name, otherwise, batch_norm
will save global variance with the string.
do_model_average_for_mean_and_var(bool, Default False): Do model average for mean and variance or not.
......@@ -3768,7 +3770,8 @@ def batch_norm(input,
and variance are also used during train period.
Returns:
Variable: A tensor variable which is the result after applying batch normalization on the input.
A Variable holding Tensor which is the result after applying batch normalization on the input,
has same shape and data type with input.
Examples:
......@@ -3890,7 +3893,7 @@ def instance_norm(input,
"""
**Instance Normalization Layer**
Can be used as a normalizer function for conv2d and fully_connected operations.
Can be used as a normalizer function for convolution or fully_connected operations.
The required data format for this layer is one of the following:
DataLayout: NCHW `[batch, in_channels, in_height, in_width]`
......@@ -3904,19 +3907,19 @@ def instance_norm(input,
.. math::
\\mu_{\\beta} &\\gets \\frac{1}{HW} \\sum_{i=1}^{HW} x_i \\qquad &//\\
\\ mean of one feature map in mini-batch \\\\
\\ mean\ of\ one\ feature\ map\ in\ mini-batch \\\\
\\sigma_{\\beta}^{2} &\\gets \\frac{1}{HW} \\sum_{i=1}^{HW}(x_i - \\
\\mu_{\\beta})^2 \\qquad &//\ variance of one feature map in mini-batch \\\\
\\mu_{\\beta})^2 \\qquad &//\ variance\ of\ one\ feature\ map\ in\ mini-batch \\\\
\\hat{x_i} &\\gets \\frac{x_i - \\mu_\\beta} {\\sqrt{\\
\\sigma_{\\beta}^{2} + \\epsilon}} \\qquad &//\ normalize \\\\
y_i &\\gets \\gamma \\hat{x_i} + \\beta \\qquad &//\ scale\ and\ shift
\\hat{x_i} &\\gets \\frac{x_i - \\mu_\\beta} {\\sqrt{\\
\\sigma_{\\beta}^{2} + \\epsilon}} \\\\
y_i &\\gets \\gamma \\hat{x_i} + \\beta
Note:
`H` means height of feature map, `W` means width of feature map.
Args:
input(variable): The rank of input variable can be 2, 3, 4, 5.
input(variable): The rank of input variable can be 2, 3, 4, 5.
The data type is float32 or float64.
epsilon(float, Default 1e-05): A value added to the denominator for
numerical stability. Default is 1e-5.
param_attr(ParamAttr|None): The parameter attribute for Parameter `scale`
......@@ -3933,7 +3936,8 @@ def instance_norm(input,
will be named automatically.
Returns:
Variable: A tensor variable which is the result after applying instance normalization on the input.
A Variable holding Tensor which is the result after applying instance normalization on the input,
has same shape and data type with input.
Examples:
......@@ -4427,8 +4431,6 @@ def conv2d_transpose(input,
name=None,
data_format='NCHW'):
"""
**Convlution2D transpose layer**
The convolution2D transpose layer calculates the output based on the input,
filter, and dilations, strides, paddings. Input(Input) and output(Output)
are in NCHW or NHWC format. Where N is batch size, C is the number of channels,
......@@ -4477,10 +4479,11 @@ def conv2d_transpose(input,
H_{out} &\in [ H^\prime_{out}, H^\prime_{out} + strides[0] ] \\\\
W_{out} &\in [ W^\prime_{out}, W^\prime_{out} + strides[1] ]
padding mode is 'SAME' and 'VALID' can reference this link<https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleGAN/network/base_network.py#L181>`_
Note:
if output_size is None, :math:`H_{out} = H^\prime_{out}, W_{out} = W^\prime_{out}`;
The conv2d_transpose can be seen as the backward of the conv2d. For conv2d,
when stride > 1, conv2d maps multiple input shape to the same output shape,
so for conv2d_transpose, when stride > 1, input shape maps multiple output shape.
If output_size is None, :math:`H_{out} = H^\prime_{out}, W_{out} = W^\prime_{out}`;
else, the :math:`H_{out}` of the output size must between :math:`H^\prime_{out}`
and :math:`H^\prime_{out} + strides[0]`, and the :math:`W_{out}` of the output size must
between :math:`W^\prime_{out}` and :math:`W^\prime_{out} + strides[1]`,
......@@ -4494,13 +4497,19 @@ def conv2d_transpose(input,
output_size(int|tuple, optional): The output image size. If output size is a
tuple, it must contain two integers, (image_height, image_width). None if use
filter_size, padding, and stride to calculate output_size.
if output_size and filter_size are specified at the same time, They
should follow the formula above. Default: None.
If output_size and filter_size are specified at the same time, They
should follow the formula above. Default: None. output_size and filter_size
should not be None at the same time.
filter_size(int|tuple, optional): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_height, filter_size_width).
Otherwise, filter_size_height = filter_size_width = filter_size. None if
use output size to calculate filter_size. Default: None.
padding(int|list|str|tuple, optional):The padding size. If `padding` is a
use output size to calculate filter_size. Default: None. filter_size and
output_size should not be None at the same time.
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
......@@ -4510,12 +4519,13 @@ def conv2d_transpose(input,
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.
stride(int|tuple, optional): The stride size. If stride is a tuple, it must
contain two integers, (stride_height, stride_width). Otherwise,
stride_height = stride_width = stride. Default: stride = 1.
dilation(int|tuple, optional): The dilation size. If dilation is a tuple, it must
contain two integers, (dilation_height, dilation_width). Otherwise,
dilation_height = dilation_width = dilation. Default: dilation = 1.
dilation(int|tuple, optional): The dilation size. It means the spacing between the kernel points.
If dilation is a tuple, it must contain two integers, (dilation_height, dilation_width).
Otherwise, dilation_height = dilation_width = dilation. Default: dilation = 1.
filter_size(int|tuple, optional): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_height, filter_size_width).
Otherwise, filter_size_height = filter_size_width = filter_size. None if
use output size to calculate filter_size. Default: None.
groups(int, optional): The groups number of the Conv2d transpose layer. Inspired by
grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
when group=2, the first half of the filters is only connected to the
......@@ -4535,18 +4545,23 @@ def conv2d_transpose(input,
library is installed. Default: True.
act (str, optional): Activation type, if it is set to None, activation is not appended.
Default: None.
name(str, optional): A name for this layer(optional). If set None, the layer
will be named automatically. Default: True.
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
data_format(str, optional): The data format of the input and output data. An optional string
from: `"NCHW"`, `"NHWC"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. Default: 'NCHW'.
Returns:
Variable: A 4-D Tensor of the shape (num_batches, channels, out_h, out_w) or
(num_batches, out_h, out_w, channels).
A Variable holding 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
storing the transposed convolution result, and if act is not None, the
tensor variable storing transposed convolution and non-linearity activation
result.
Raises:
ValueError: If the shapes of input, filter_size, stride, padding and
ValueError: If the shapes of output, input, filter_size, stride, padding and
groups mismatch.
Examples:
......@@ -4688,8 +4703,6 @@ def conv3d_transpose(input,
name=None,
data_format='NCDHW'):
"""
**Convlution3D transpose layer**
The convolution3D transpose layer calculates the output based on the input,
filter, and dilations, strides, paddings. Input(Input) and output(Output)
are in NCDHW or NDHWC format. Where N is batch size, C is the number of channels,
......@@ -4733,26 +4746,43 @@ def conv3d_transpose(input,
.. math::
D_{out} &= (D_{in} - 1) * strides[0] - pad_depth_front - pad_depth_back + dilations[0] * (D_f - 1) + 1 \\\\
H_{out} &= (H_{in} - 1) * strides[1] - pad_height_top - pad_height_bottom + dilations[1] * (H_f - 1) + 1 \\\\
W_{out} &= (W_{in} - 1) * strides[2] - pad_width_left - pad_width_right + dilations[2] * (W_f - 1) + 1
Padding mode is 'SAME' and 'VALID' can reference this
link<https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleGAN/network/base_network.py#L181>`_
D^\prime_{out} &= (D_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (D_f - 1) + 1 \\\\
H^\prime_{out} &= (H_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (H_f - 1) + 1 \\\\
W^\prime_{out} &= (W_{in} - 1) * strides[2] - 2 * paddings[2] + dilations[2] * (W_f - 1) + 1 \\\\
D_{out} &\in [ D^\prime_{out}, D^\prime_{out} + strides[0] ] \\\\
H_{out} &\in [ H^\prime_{out}, H^\prime_{out} + strides[1] ] \\\\
W_{out} &\in [ W^\prime_{out}, W^\prime_{out} + strides[2] ]
Args:
input(Variable): A 5-D Tensor with [N, C, H, W] or [N, H, W, C] format. Its data type is float32 or float64.
Note:
The conv3d_transpose can be seen as the backward of the conv3d. For conv3d,
when stride > 1, conv3d maps multiple input shape to the same output shape,
so for conv3d_transpose, when stride > 1, input shape maps multiple output shape.
If output_size is None, :math:`H_{out} = H^\prime_{out}, :math:`H_{out} = \
H^\prime_{out}, W_{out} = W^\prime_{out}`; else, the :math:`D_{out}` of the output
size must between :math:`D^\prime_{out}` and :math:`D^\prime_{out} + strides[0]`,
the :math:`H_{out}` of the output size must between :math:`H^\prime_{out}`
and :math:`H^\prime_{out} + strides[1]`, and the :math:`W_{out}` of the output size must
between :math:`W^\prime_{out}` and :math:`W^\prime_{out} + strides[2]`,
conv3d_transpose can compute the kernel size automatically.
Args:
input(Variable): The input is 5-D Tensor with shape [N, C, D, H, W] or [N, D, H, W, C], the data type
of input is float32 or float64.
num_filters(int): The number of the filter. It is as same as the output
image channel.
output_size(int|tuple, optional): The output image size. If output size is a
tuple, it must contain three integers, (image_D, image_H, image_W). This
parameter only works when filter_size is None.
tuple, it must contain three integers, (image_depth, image_height, image_width). This
parameter only works when filter_size is None. If output_size and filter_size are
specified at the same time, They should follow the formula above. Default: None.
Output_size and filter_size should not be None at the same time.
filter_size(int|tuple, optional): The filter size. If filter_size is a tuple,
it must contain three integers, (filter_size_depth, filter_size_height, \
it must contain three integers, (filter_size_depth, filter_size_height,
filter_size_width). Otherwise, filter_size_depth = filter_size_height = \
filter_size_width = filter_size. None if use output size to
calculate filter_size.
padding(int|list|str|tuple, optional): The padding size. if `padding` is a string,
calculate filter_size. Default: None. filter_size and output_size should not be
None at the same time.
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_depth, pad_height, pad_width]` or
`[pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`,
......@@ -4761,12 +4791,14 @@ def conv3d_transpose(input,
when `data_format` is `'NDHWC'`, `padding` can be in the form
`[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
Default: padding = 0.
stride(int|tuple, optional): The stride size. If stride is a tuple, it must
contain three integers, (stride_depth, stride_height, stride_width). Otherwise,
stride_depth = stride_height = stride_width = stride. Default: stride = 1.
dilation(int|tuple, optional): The dilation size. If dilation is a tuple, it must
contain three integers, (dilation_depth, dilation_height, dilation_width). Otherwise,
dilation_depth = dilation_height = dilation_width = dilation. Default: dilation = 1.
stride(int|tuple, optional): The stride size. It means the stride in transposed convolution.
If stride is a tuple, it must contain three integers, (stride_depth, stride_height,
stride_width). Otherwise, stride_depth = stride_height = stride_width = stride.
Default: stride = 1.
dilation(int|tuple, optional): The dilation size. It means the spacing between the kernel points.
If dilation is a tuple, it must contain three integers, (dilation_depth, dilation_height,
dilation_width). Otherwise, dilation_depth = dilation_height = dilation_width = dilation.
Default: dilation = 1.
groups(int, optional): The groups number of the Conv3d transpose layer. Inspired by
grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
when group=2, the first half of the filters is only connected to the
......@@ -4786,18 +4818,22 @@ def conv3d_transpose(input,
library is installed. Default: True
act (str, optional): Activation type, if it is set to None, activation is not appended.
Default: None.
name(str, optional): A name for this layer(optional). If set None, the layer
will be named automatically.
name(str, optional): For detailed information, please refer
to :ref:`api_guide_Name`. Usually name is no need to set and
None by default.
data_format(str, optional):The data format of the input and output data. An optional string from: `"NCHW"`, `"NHWC"`.
When it is `"NCHW"`, the data is stored in the order of: `[batch_size, input_channels, input_height, input_width]`.
Default: 'NCDHW'.
Returns:
A 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).
A Variable holding Tensor representing the conv3d_transpose, whose data
type is the same with input and shape is (num_batches, channels, out_d, out_h,
out_w) or (num_batches, out_d, out_h, out_w, channels). If act is None, the tensor
variable storing the transposed convolution result, and if act is not None, the tensor
variable storing transposed convolution and non-linearity activation result.
Raises:
ValueError: If the shapes of input, filter_size, stride, padding and
ValueError: If the shapes of output, input, filter_size, stride, padding and
groups mismatch.
Examples:
......@@ -14437,20 +14473,25 @@ def npair_loss(anchor, positive, labels, l2_reg=0.002):
'''
**Npair Loss Layer**
Read `Improved Deep Metric Learning with Multi class N pair Loss Objective <http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf>`_ .
Read `Improved Deep Metric Learning with Multi class N pair Loss Objective\
<http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/\
papers/nips16_npairmetriclearning.pdf>`_ .
Npair loss requires paired data. Npair loss has two parts: the first part is L2
regularizer on the embedding vector; the second part is cross entropy loss which
takes the similarity matrix of anchor and positive as logits.
Args:
anchor(Variable): embedding vector for the anchor image. shape=[batch_size, embedding_dims]
positive(Variable): embedding vector for the positive image. shape=[batch_size, embedding_dims]
labels(Variable): 1-D tensor. shape=[batch_size]
l2_reg(float32): L2 regularization term on embedding vector, default: 0.002
anchor(Variable): embedding vector for the anchor image. shape=[batch_size, embedding_dims],
the data type is float32 or float64.
positive(Variable): embedding vector for the positive image. shape=[batch_size, embedding_dims],
the data type is float32 or float64.
labels(Variable): 1-D tensor. shape=[batch_size], the data type is float32 or float64 or int64.
l2_reg(float32): L2 regularization term on embedding vector, default: 0.002.
Returns:
npair loss(Variable): return npair loss, shape=[1]
A Variable holding Tensor representing the npair loss, the data type is the same as
anchor, the shape is [1].
Examples:
.. code-block:: python
......
python/paddle/fluid/nets.py
浏览文件 @ d4199901
......@@ -152,11 +152,11 @@ def img_conv_group(input,
result to Pool2d.
Args:
input (Variable): The input image with [N, C, H, W] format.
input (Variable): The input is 4-D Tensor with shape [N, C, H, W], the data type of input is float32 or float64.
conv_num_filter(list|tuple): Indicates the numbers of filter of this group.
pool_size (int|list|tuple): The pooling size of Pool2d Layer. If pool_size
is a list or tuple, it must contain two integers, (pool_size_H, pool_size_W).
Otherwise, the pool_size_H = pool_size_W = pool_size.
is a list or tuple, it must contain two integers, (pool_size_height, pool_size_width).
Otherwise, the pool_size_height = pool_size_width = pool_size.
conv_padding (int|list|tuple): The padding size of the Conv2d Layer. If padding is
a list or tuple, its length must be equal to the length of conv_num_filter.
Otherwise the conv_padding of all Conv2d Layers are the same. Default 1.
......@@ -184,8 +184,8 @@ def img_conv_group(input,
library is installed. Default: True
Return:
Variable: The final result after serial computation using Convolution2d,
BatchNorm, DropOut, and Pool2d.
The final result after serial computation using Convolution2d,
BatchNorm, DropOut, and Pool2d.
Examples:
.. code-block:: python
......
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