Reinforcement Learning Algorithms with Python

Placeholder

A placeholder is a tensor that is fed at runtime. Usually, placeholders are used as input for models. Every input passed to a computational graph at runtime is fed with feed_dict. feed_dict is an optional argument that allows the caller to override the value of tensors in the graph. In the following snippet, the a placeholder is overridden by [[0.1,0.2,0.3]]:

import tensorflow as tfa = tf.placeholder(shape=(1,3), dtype=tf.float32)b = tf.constant([[10,10,10]], dtype=tf.float32)c = a + bsess = tf.Session()res = sess.run(c, feed_dict={a:[[0.1,0.2,0.3]]})print(res)>> [[10.1 10.2 10.3]]

If the size of the first dimension of the input is not known during the creation of the graph, TensorFlow can take care of it. Just set it to None ...