diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index f6b4348d250a9d99541932fcfefd386584d0d1e5..3a4f93929f51b2181482267735f9c1145622f928 100644
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -39,13 +39,16 @@ __all__ = [
     'chunk_eval',
     'sequence_conv',
     'conv2d',
+    'conv3d',
     'sequence_pool',
     'sequence_softmax',
     'softmax',
     'pool2d',
+    'pool3d',
     'batch_norm',
     'beam_search_decode',
     'conv2d_transpose',
+    'conv3d_transpose',
     'sequence_expand',
     'lstm_unit',
     'reduce_sum',
@@ -1385,13 +1388,12 @@ def conv3d(input,
 
     The convolution3D layer calculates the output based on the input, filter
     and strides, paddings, dilations, groups parameters. Input(Input) and
-    Output(Output) are in NCHW format. Where N is batch size, C is the number of
-    channels, H is the height of the feature, and W is the width of the feature.
-    The details of convolution layer, please refer UFLDL's `convolution,
-    <http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/>`_ .
-    If bias attribution and activation type are provided, bias is added to the
-    output of the convolution, and the corresponding activation function is
-    applied to the final result.
+    Output(Output) are in NCDHW format. Where N is batch size C is the number of
+    channels, D is the depth of the feature, H is the height of the feature,
+    and W is the width of the feature. Convlution3D is similar with Convlution2D
+    but adds one dimension(depth). If bias attribution and activation type are
+    provided, bias is added to the output of the convolution, and the
+    corresponding activation function is applied to the final result.
 
     For each input :math:`X`, the equation is:
 
@@ -1401,8 +1403,8 @@ def conv3d(input,
 
     In the above equation:
 
-    * :math:`X`: Input value, a tensor with NCHW format.
-    * :math:`W`: Filter value, a tensor with MCHW format.
+    * :math:`X`: Input value, a tensor with NCDHW format.
+    * :math:`W`: Filter value, a tensor with MCDHW format.
     * :math:`\\ast`: Convolution operation.
     * :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
     * :math:`\\sigma`: Activation function.
@@ -1433,16 +1435,16 @@ def conv3d(input,
             num_filters(int): The number of filter. It is as same as the output
             image channel.
         filter_size (int|tuple|None): The filter size. If filter_size is a tuple,
-            it must contain two integers, (filter_size_D, filter_size_H, filter_size_W).
+            it must contain three integers, (filter_size_D, filter_size_H, filter_size_W).
             Otherwise, the filter will be a square.
         stride (int|tuple): The stride size. If stride is a tuple, it must
-            contain two integers, (stride_D, stride_H, stride_W). Otherwise, the
+            contain three integers, (stride_D, stride_H, stride_W). Otherwise, the
             stride_D = stride_H = stride_W = stride. Default: stride = 1.
         padding (int|tuple): The padding size. If padding is a tuple, it must
-            contain two integers, (padding_D, padding_H, padding_W). Otherwise, the
+            contain three integers, (padding_D, padding_H, padding_W). Otherwise, the
             padding_D = padding_H = padding_W = padding. Default: padding = 0.
         dilation (int|tuple): The dilation size. If dilation is a tuple, it must
-            contain two integers, (dilation_D, dilation_H, dilation_W). Otherwise, the
+            contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the
             dilation_D = dilation_H = dilation_W = 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,
@@ -1528,7 +1530,7 @@ def conv3d(input,
             'use_mkldnn': use_mkldnn
         })
 
-    pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=3)
+    pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
 
     return helper.append_activation(pre_act)
 
@@ -1720,12 +1722,84 @@ def pool2d(input,
     if not isinstance(use_cudnn, bool):
         raise ValueError("use_cudnn should be True or False")
 
-    helper = LayerHelper('pool2d', **locals())
+    l_type = 'conv2d'
+
+    helper = LayerHelper(l_type, **locals())
+    dtype = helper.input_dtype()
+    pool_out = helper.create_tmp_variable(dtype)
+
+    helper.append_op(
+        type=l_type,
+        inputs={"X": input},
+        outputs={"Out": pool_out},
+        attrs={
+            "pooling_type": pool_type,
+            "ksize": pool_size,
+            "global_pooling": global_pooling,
+            "strides": pool_stride,
+            "paddings": pool_padding,
+            "use_cudnn": use_cudnn,
+            "ceil_mode": ceil_mode,
+            "use_mkldnn": use_mkldnn
+        })
+
+    return pool_out
+
+
+def pool3d(input,
+           pool_size=-1,
+           pool_type="max",
+           pool_stride=1,
+           pool_padding=0,
+           global_pooling=False,
+           use_cudnn=True,
+           ceil_mode=False,
+           use_mkldnn=False,
+           name=None):
+    """
+    This function adds the operator for pooling in 3-dimensions, using the
+    pooling configurations mentioned in input parameters.
+
+    Args:
+        input (Variable): ${input_comment}
+        pool_size (int): ${ksize_comment}
+        pool_type (str): ${pooling_type_comment}
+        pool_stride (int): stride of the pooling layer.
+        pool_padding (int): padding size.
+        global_pooling (bool): ${global_pooling_comment}
+        use_cudnn (bool): ${use_cudnn_comment}
+        ceil_mode (bool): ${ceil_mode_comment}
+        use_mkldnn (bool): ${use_mkldnn_comment}
+        name (str): A name for this layer(optional). If set None, the layer
+            will be named automatically.
+
+    Returns:
+        Variable: output of pool3d layer.
+    """
+    if pool_type not in ["max", "avg"]:
+        raise ValueError(
+            "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.",
+            str(pool_type))
+
+    if global_pooling is False and pool_size == -1:
+        raise ValueError(
+            "When the global_pooling is False, pool_size must be passed "
+            "and be a valid value. Received pool_size: " + str(pool_size))
+
+    pool_size = utils.convert_to_list(pool_size, 3, 'pool_size')
+    pool_padding = utils.convert_to_list(pool_padding, 3, 'pool_padding')
+    pool_stride = utils.convert_to_list(pool_stride, 3, 'pool_stride')
+
+    if not isinstance(use_cudnn, bool):
+        raise ValueError("use_cudnn should be True or False")
+
+    l_type = "pool3d"
+    helper = LayerHelper(l_type, **locals())
     dtype = helper.input_dtype()
     pool_out = helper.create_tmp_variable(dtype)
 
     helper.append_op(
-        type="pool2d",
+        type=l_type,
         inputs={"X": input},
         outputs={"Out": pool_out},
         attrs={
@@ -2146,6 +2220,173 @@ def conv2d_transpose(input,
     return out
 
 
+def conv3d_transpose(input,
+                     num_filters,
+                     output_size=None,
+                     filter_size=None,
+                     padding=0,
+                     stride=1,
+                     dilation=1,
+                     groups=None,
+                     param_attr=None,
+                     bias_attr=None,
+                     use_cudnn=True,
+                     act=None,
+                     name=None):
+    """
+    **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 format. Where N is batch size, C is the number of channels,
+    D is the depth of the feature, H is the height of the feature, and W
+    is the width of the feature. Parameters(dilations, strides, paddings) are
+    two elements. These two elements represent height and width, respectively.
+    The details of convolution transpose layer, please refer to the following
+    explanation and references `therein <http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf>`_.
+
+    For each input :math:`X`, the equation is:
+
+    .. math::
+
+        Out = W \\ast X
+
+    In the above equation:
+
+    * :math:`X`: Input value, a tensor with NCDHW format.
+    * :math:`W`: Filter value, a tensor with MCDHW format.
+    * :math:`\\ast` : Convolution transpose operation.
+    * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be
+                   different.
+
+    Example:
+
+        - Input:
+
+          Input shape: $(N, C_{in}, D_{in}, H_{in}, W_{in})$
+
+          Filter shape: $(C_{in}, C_{out}, D_f, H_f, W_f)$
+
+        - Output:
+
+          Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$
+
+        Where
+
+        .. math::
+
+           D_{out} &= (D_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (D_f - 1) + 1 \\\\
+           H_{out} &= (H_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (H_f - 1) + 1 \\\\
+           W_{out} &= (W_{in} - 1) * strides[2] - 2 * paddings[2] + dilations[2] * (W_f - 1) + 1
+
+    Args:
+        input(Variable): The input image with [N, C, D, H, W] format.
+        num_filters(int): The number of the filter. It is as same as the output
+            image channel.
+        output_size(int|tuple|None): 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.
+        filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
+            it must contain three integers, (filter_size_D, filter_size_H, filter_size_W).
+            Otherwise, the filter will be a square. None if use output size to
+            calculate filter_size.
+        padding(int|tuple): The padding size. If padding is a tuple, it must
+            contain three integers, (padding_D, padding_H, padding_W). Otherwise, the
+            padding_D = padding_H = padding_W = padding. Default: padding = 0.
+        stride(int|tuple): The stride size. If stride is a tuple, it must
+            contain three integers, (stride_D, stride_H, stride_W). Otherwise, the
+            stride_D = stride_H = stride_W = stride. Default: stride = 1.
+        dilation(int|tuple): The dilation size. If dilation is a tuple, it must
+            contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the
+            dilation_D = dilation_H = dilation_W = dilation. Default: dilation = 1.
+        groups(int): 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
+            first half of the input channels, while the second half of the
+            filters is only connected to the second half of the input channels.
+            Default: groups=1
+        param_attr(ParamAttr): The parameters to the Conv3d_transpose Layer.
+            Default: None
+        bias_attr(ParamAttr): Bias parameter for the Conv3d layer. Default: None
+        use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn
+            library is installed. Default: True
+        act(str): Activation type. Default: None
+        name(str|None): A name for this layer(optional). If set None, the layer
+            will be named automatically.
+
+    Returns:
+        Variable: The tensor variable storing the convolution transpose result.
+
+    Raises:
+        ValueError: If the shapes of input, filter_size, stride, padding and
+                    groups mismatch.
+
+    Examples:
+       .. code-block:: python
+
+          data = fluid.layers.data(
+              name='data', shape=[3, 12, 32, 32], dtype='float32')
+          conv2d_transpose = fluid.layers.conv3d_transpose(
+              input=data, num_filters=2, filter_size=3)
+    """
+    l_type = "conv3d_transpose"
+    helper = LayerHelper(l_type, **locals())
+    if not isinstance(input, Variable):
+        raise TypeError("Input of conv3d_transpose must be Variable")
+    input_channel = input.shape[1]
+
+    padding = utils.convert_to_list(padding, 3, 'padding')
+    stride = utils.convert_to_list(stride, 3, 'stride')
+    dilation = utils.convert_to_list(dilation, 3, 'dilation')
+
+    if not isinstance(use_cudnn, bool):
+        raise ValueError("use_cudnn should be True or False")
+
+    if filter_size is None:
+        if output_size is None:
+            raise ValueError("output_size must be set when filter_size is None")
+        if isinstance(output_size, int):
+            output_size = [output_size, output_size]
+
+        d_in = input.shape[2]
+        h_in = input.shape[3]
+        w_in = input.shape[4]
+
+        filter_size_d = (output_size[0] - (d_in - 1) * stride[0] + 2 *
+                         padding[0] - 1) / dilation[0] + 1
+        filter_size_h = (output_size[1] - (h_in - 1) * stride[1] + 2 *
+                         padding[1] - 1) / dilation[1] + 1
+        filter_size_w = (output_size[2] - (w_in - 1) * stride[2] + 2 *
+                         padding[2] - 1) / dilation[2] + 1
+        filter_size = [filter_size_d, filter_size_h, filter_size_w]
+    else:
+        filter_size = utils.convert_to_list(filter_size, 3,
+                                            'conv3d_transpose.filter_size')
+
+    groups = 1 if groups is None else groups
+    filter_shape = [input_channel, num_filters / groups] + filter_size
+    img_filter = helper.create_parameter(
+        dtype=input.dtype, shape=filter_shape, attr=helper.param_attr)
+
+    pre_bias = helper.create_tmp_variable(dtype=input.dtype)
+    helper.append_op(
+        type=l_type,
+        inputs={'Input': [input],
+                'Filter': [img_filter]},
+        outputs={'Output': pre_bias},
+        attrs={
+            'strides': stride,
+            'paddings': padding,
+            'dilations': dilation,
+            'groups': groups,
+            'use_cudnn': use_cudnn
+        })
+
+    pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
+    out = helper.append_activation(pre_act)
+    return out
+
+
 def sequence_expand(x, y, ref_level=-1, name=None):
     """Sequence Expand Layer. This layer will expand the input variable **x**
     according to specified level lod of **y**. Please note that lod level of