I am trying to do a logistic regression and my training dataset is derived from a a numpy float64 array. My code looks like,
import tensorflow as tf
graph = tf.Graph()
with graph.as_default():
examples =tf.constant(mat6) # mat6 is a numpy float64 array
t_labels = tf.constant(labels) # labels is an a numpy float64 array
W = tf.Variable(tf.truncated_normal([115713, 2]))
b = tf.Variable(tf.zeros([2]))
logits = tf.matmul(examples, W)+b
This throws an exception
TypeError: Input 'b' of 'MatMul' Op has type float32 that does not match type float64 of argument 'a'.
This probably because W and b are float32 and not float64. Is there a way to convert W and b OR create it as a float64