def conv_layer(x,
hidden_size,
kernel_size,
stride,
pooling_window,
dropout_rate,
dilation_rate,
name="conv"):
"""Single conv layer with relu, optional pooling, and dropout."""
with tf.variable_scope(name):
out = x
out = common_layers.conv1d_block(out,
hidden_size,
[(dilation_rate, kernel_size)],
strides=stride,
first_relu=False,
padding="same")
out = tf.nn.relu(out)
if pooling_window:
out = tf.layers.max_pooling1d(out,
pooling_window,
pooling_window,
padding="same")
out = tf.layers.dropout(out, dropout_rate)
return out
def conv_layer(x,
hidden_size,
kernel_size,
stride,
pooling_window,
dropout_rate,
dilation_rate,
name="conv"):
with tf.variable_scope(name):
out = x
out = common_layers.conv1d_block(
out,
hidden_size, [(dilation_rate, kernel_size)],
strides=stride,
first_relu=False,
padding="same")
out = tf.nn.relu(out)
if pooling_window:
out = tf.layers.max_pooling1d(
out, pooling_window, pooling_window, padding="same")
out = tf.layers.dropout(out, dropout_rate)
return out