Note ** :

  1. X = Dimension of features is known while number of instances is left undefined
  2. Y = Dimension of classes is known, while number of instances is left undefined
In [13]:
# Create the graph for the linear model
# Placeholders for inputs (X) and outputs(Y)
# data and target are symbolic variable which will contain input data
data = tf.placeholder(dtype=tf.float32, shape=[None, dimension_of_features], name='X_placeholder')
target = tf.placeholder(dtype=tf.float32, shape=[None, dimension_of_classes], name='Y_placeholder')