conv1d_max_pool.py

# (batch, 128, 9) -> (batch, 32, 18)
conv1 = tf.layers.conv1d(inputs=inputs_, filters=18, kernel_size=2, strides=1,
    padding='same', activation = tf.nn.relu)
max_pool_1 = tf.layers.max_pooling1d(inputs=conv1, pool_size=4, strides=4, padding='same')

# (batch, 32, 18) -> (batch, 8, 36)
conv2 = tf.layers.conv1d(inputs=max_pool_1, filters=36, kernel_size=2, strides=1,
padding='same', activation = tf.nn.relu)
max_pool_2 = tf.layers.max_pooling1d(inputs=conv2, pool_size=4, strides=4, padding='same')

# (batch, 8, 36) -> (batch, 2, 72)
conv3 = tf.layers.conv1d(inputs=max_pool_2, filters=72, kernel_size=2, strides=1,
padding='same', activation = tf.nn.relu)
max_pool_3 = tf.layers.max_pooling1d(inputs=conv3, pool_size=4, strides=4, padding='same')