3.2. Initialize our paramaters

In [14]:
W = tf.Variable(tf.random_normal([dimension_of_features, dimension_of_classes])* 0.01, name='weights') # W - weights
b = tf.Variable(tf.zeros([dimension_of_classes, dimension_of_classes]), name='bias') # b - bias 

# Add summary ops to collect data
w_h = tf.summary.histogram("weights", W)
b_h = tf.summary.histogram("biases", b)