import tensorflow as tf

z_dim = 10
batch_size = 100
n_epochs = 1000
learning_rate = 0.001
beta1 = 0.9
results_path = './Results/Semi_Supervised'
n_labels = 10
n_labeled = 1000

# 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')


