import tensorflow as tf
import numpy as np
rule_tag = tf.constant([[7],[5],[5],[5],[7]])
preds = tf.constant([[0.9],[1.5],[1.1]])
print(rule_tag)
eq = tf.equal(tf.constant(7, dtype=tf.int32), rule_tag)
indices = tf.reduce_max(tf.where(eq, tf.ones_like(rule_tag), tf.zeros_like(rule_tag)), axis=1)
indices2 = tf.stack([tf.range(tf.shape(indices)[0]), tf.cast(indices, tf.int32)], axis=1)
preds_add = tf.concat([preds, tf.zeros([tf.shape(preds)[0],1])], axis=1)
sess = tf.Session()
print(preds)
print(preds_add)
print(sess.run(preds_add))
# Learns best fit is W: [0.1], b: [0.3]# your code goes here
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
Tensor("Const:0", shape=(5, 1), dtype=int32)
Tensor("Const_1:0", shape=(3, 1), dtype=float32)
Tensor("concat:0", shape=(3, 2), dtype=float32)
[[0.9 0. ]
[1.5 0. ]
[1.1 0. ]]