import tensorflow as tf


# Placeholders for input data and the targets
x_input = tf.placeholder(dtype=tf.float32, shape=[batch_size, input_dim], name='Input')
x_input_l = tf.placeholder(dtype=tf.float32, shape=[batch_size, input_dim], name='Labeled_Input')
y_input = tf.placeholder(dtype=tf.float32, shape=[batch_size, n_labels], name='Labels')
x_target = tf.placeholder(dtype=tf.float32, shape=[batch_size, input_dim], name='Target')
real_distribution = tf.placeholder(dtype=tf.float32, shape=[batch_size, z_dim], name='Real_distribution')
categorial_distribution = tf.placeholder(dtype=tf.float32, shape=[batch_size, n_labels],
                                         name='Categorical_distribution')
manual_decoder_input = tf.placeholder(dtype=tf.float32, shape=[1, z_dim + n_labels], name='Decoder_input')


