An example of how to do conv1d ourself in Tensorflow

basic_conv1d.py

import tensorflow as tf
def conv1d(input_, output_size, width, stride):
    '''
    :param input_: A tensor of embedded tokens with shape [batch_size,max_length,embedding_size]
    :param output_size: The number of feature maps we'd like to calculate
    :param width: The filter width
    :param stride: The stride
    :return: A tensor of the concolved input with shape [batch_size,max_length,output_size]
    '''
    inputSize = input_.get_shape()[-1] # How many channels on the input (The size of our embedding for instance)

    #This is the kicker where we make our text an image of height 1
    input_ = tf.expand_dims(input_, axis=1) #  Change the shape to [batch_size,1,max_length,output_size]

    #Make sure the height of the filter is 1
    filter_ = tf.get_variable("conv_filter",shape=[1, width, inputSize, output_size])

    #Run the convolution as if this were an image
    convolved = tf.nn.conv2d(input_, filter=filter_,strides=[1, 1, stride, 1], padding="SAME")
    #Remove the extra dimension, eg make the shape [batch_size,max_length,output_size]
    result = tf.squeeze(convolved, axis=1)
    return result