(x,
           num_filters,
           filter_length,
           name,
           dilation=1,
           causal=True,
           kernel_initializer=tf.uniform_unit_scaling_initializer(1.0),
           biases_initializer=tf.constant_initializer(0.0),
           is_training=True)
return y
Fast 1D convolution that supports causal padding and dilation.

  Args:
    x: The [mb, time, channels] float tensor that we convolve.
    num_filters: The number of filter maps in the convolution.
    filter_length: The integer length of the filter.
    name: The name of the scope for the variables.
    dilation: The amount of dilation.
    causal: Whether or not this is a causal convolution.
    kernel_initializer: The kernel initialization function.
    biases_initializer: The biases initialization function.
    is_training: Whether or not ot use traininable variables.

  Returns:
    y: The output of the 1D convolution.
Fast 1D convolution that supports causal padding and dilation.
Fast 1D convolution that supports causal padding and dilation .
def conv1d(x,
           num_filters,
           filter_length,
           name,
           dilation=1,
           causal=True,
           kernel_initializer=tf.uniform_unit_scaling_initializer(1.0),
           biases_initializer=tf.constant_initializer(0.0),
           is_training=True):
  """Fast 1D convolution that supports causal padding and dilation.

  Args:
    x: The [mb, time, channels] float tensor that we convolve.
    num_filters: The number of filter maps in the convolution.
    filter_length: The integer length of the filter.
    name: The name of the scope for the variables.
    dilation: The amount of dilation.
    causal: Whether or not this is a causal convolution.
    kernel_initializer: The kernel initialization function.
    biases_initializer: The biases initialization function.
    is_training: Whether or not ot use traininable variables.

  Returns:
    y: The output of the 1D convolution.
  """
  batch_size, length, num_input_channels = x.get_shape().as_list()
  assert length % dilation == 0

  kernel_shape = [1, filter_length, num_input_channels, num_filters]
  strides = [1, 1, 1, 1]
  biases_shape = [num_filters]
  padding = 'VALID' if causal else 'SAME'

  with tf.variable_scope(name):
    weights = tf.get_variable(
        'W', shape=kernel_shape, initializer=kernel_initializer,
        trainable=is_training)
    biases = tf.get_variable(
        'biases', shape=biases_shape, initializer=biases_initializer,
        trainable=is_training)

  x_ttb = time_to_batch(x, dilation)
  if filter_length > 1 and causal:
    x_ttb = tf.pad(x_ttb, [[0, 0], [filter_length - 1, 0], [0, 0]])

  x_ttb_shape = x_ttb.get_shape().as_list()
  x_4d = tf.reshape(x_ttb, [x_ttb_shape[0], 1,
                            x_ttb_shape[1], num_input_channels])
  y = tf.nn.conv2d(x_4d, weights, strides, padding=padding)
  y = tf.nn.bias_add(y, biases)
  y_shape = y.get_shape().as_list()
  y = tf.reshape(y, [y_shape[0], y_shape[2], num_filters])
  y = batch_to_time(y, dilation)
  y.set_shape([batch_size, length, num_filters])
  return y
def conv1d ( x , num_filters , filter_length , name , dilation = 1 , causal = True , kernel_initializer = tf . uniform_unit_scaling_initializer ( 1.0 ) , biases_initializer = tf . constant_initializer ( 0.0 ) , is_training = True ) : batch_size , length , num_input_channels = x . get_shape ( ) . as_list ( ) assert length % dilation == 0 kernel_shape = [ 1 , filter_length , num_input_channels , num_filters ] strides = [ 1 , 1 , 1 , 1 ] biases_shape = [ num_filters ] padding = 'VALID' if causal else 'SAME' with tf . variable_scope ( name ) : weights = tf . get_variable ( 'W' , shape = kernel_shape , initializer = kernel_initializer , trainable = is_training ) biases = tf . get_variable ( 'biases' , shape = biases_shape , initializer = biases_initializer , trainable = is_training ) x_ttb = time_to_batch ( x , dilation ) if filter_length > 1 and causal : x_ttb = tf . pad ( x_ttb , [ [ 0 , 0 ] , [ filter_length - 1 , 0 ] , [ 0 , 0 ] ] ) x_ttb_shape = x_ttb . get_shape ( ) . as_list ( ) x_4d = tf . reshape ( x_ttb , [ x_ttb_shape [ 0 ] , 1 , x_ttb_shape [ 1 ] , num_input_channels ] ) y = tf . nn . conv2d ( x_4d , weights , strides , padding = padding ) y = tf . nn . bias_add ( y , biases ) y_shape = y . get_shape ( ) . as_list ( ) y = tf . reshape ( y , [ y_shape [ 0 ] , y_shape [ 2 ] , num_filters ] ) y = batch_to_time ( y , dilation ) y . set_shape ( [ batch_size , length , num_filters ] ) return y