def f(name, h, width, n_out=None): n_out = n_out or int(h.get_shape()[3]) with tf.variable_scope(name): h = tf.nn.relu(Z.conv2d("l_1", h, width)) h = tf.nn.relu(Z.conv2d("l_2", h, width, filter_size=[1, 1])) h = Z.conv2d_zeros("l_last", h, n_out) return h
def f_resnet(name, h, width, n_out=None): n_out = n_out or int(h.get_shape()[3]) with tf.variable_scope(name): h = tf.nn.relu(Z.conv2d("l_1", h, width)) h = Z.conv2d_zeros("l_2", h, n_out) return h