[release] simplified conv1d

Author: zsdonghao

docs/modules/layers.rst

@@ -288,8 +288,10 @@ Layer list
    AtrousConv2dLayer
    SeparableConv2dLayer
 
+   Conv1d
    Conv2d
    DeConv2d
+   
    MaxPool1d
    MeanPool1d
    MaxPool2d
@@ -464,6 +466,10 @@ For users don't familiar with TensorFlow, the following simplified functions may
 We will provide more simplified functions later, but if you are good at TensorFlow, the professional
 APIs may better for you.
 
+1D Convolutional layer
+^^^^^^^^^^^^^^^^^^^^^^^
+.. autofunction:: Conv1d
+
 2D Convolutional layer
 ^^^^^^^^^^^^^^^^^^^^^^^
 .. autofunction:: Conv2d

tensorlayer/layers.py

@@ -1791,6 +1791,39 @@ def deconv2d_bilinear_upsampling_initializer(shape):
     return bilinear_weights_init
 
 ## Convolutional layer (Simplified)
+def Conv1d(net, n_filter=32, filter_size=5, stride=1, act=None,
+        padding='SAME', use_cudnn_on_gpu=None,data_format=None,
+        W_init = tf.truncated_normal_initializer(stddev=0.02),
+        b_init = tf.constant_initializer(value=0.0),
+        W_init_args = {}, b_init_args = {}, name ='conv1d',):
+    """Wrapper for :class:`Conv1dLayer`, if you don't understand how to use :class:`Conv1dLayer`, this function may be easier.
+
+    Parameters
+    ----------
+    net : TensorLayer layer.
+    n_filter : number of filter.
+    filter_size : an int.
+    stride : an int.
+    act : None or activation function.
+    others : see :class:`Conv1dLayer`.
+    """
+    if act is None:
+        act = tf.identity
+    net = Conv1dLayer(layer = net,
+            act = act,
+            shape = [filter_size, int(net.outputs.get_shape()[-1]), n_filter],
+            stride = stride,
+            padding = padding,
+            use_cudnn_on_gpu = use_cudnn_on_gpu,
+            data_format = data_format,
+            W_init = W_init,
+            b_init = b_init,
+            W_init_args = W_init_args,
+            b_init_args = b_init_args,
+            name = name,
+        )
+    return net
+
 def Conv2d(net, n_filter=32, filter_size=(3, 3), strides=(1, 1), act = None,
         padding='SAME', W_init = tf.truncated_normal_initializer(stddev=0.02), b_init = tf.constant_initializer(value=0.0),
         W_init_args = {}, b_init_args = {}, name ='conv2d',):