TensorFlow while loop where the prediction is the next iteration's input

Question

I am trying to get a while loop working in TensorFlow, but am struggling to understand the body parameter of tf.while_loop.

In my code, I have a neural network defined as follows:

input_placeholder = tf.placeholder(dtype=np.float32, shape=[None, 2])

hidden_layer = tf.layers.dense(inputs=input_placeholder, units=10, activation=tf.nn.relu)
prediction_op = tf.layers.dense(inputs=hidden_layer, units=2)

I then want to run this prediction operation 10 times, feeding the prediction from one iteration as the input into the next iteration. My code for this is as follows:

num_steps = 10
condition = lambda step_num, input_placeholder: tf.less(step_num, num_steps)
_, final_prediction = tf.while_loop(condition, body, [step_num, latest_prediction])

What I am confused about is what to use for the body parameter. I need the body to take the prediction of the current iteration, and set this as the input for the next iteration.

Can I just use prediction_op for body? If so, how should I tell the while loop to update the value of input_placeholder after each call of prediction_op? Or should I do this a different way?

Thanks!

hampi · Accepted Answer · 2018-11-29 15:23:18Z

1

You could have the body definition like this:

def body(step_num, latest_prediction):
    hidden_layer = tf.layers.dense(inputs=latest_prediction, units=10, activation=tf.nn.relu)
    prediction_op = tf.layers.dense(inputs=hidden_layer, units=2)
    return step_num+1, prediction_op

and you can modify the while_loop as:

step_num = 0
input_placeholder = tf.placeholder(dtype=np.float32, shape=[None, 2])
condition = lambda step_num, input_placeholder: tf.less(step_num, num_steps)
_, final_prediction = tf.while_loop(condition, body, [step_num, input_placeholder])

answered Nov 29, 2018 at 15:23

hampi

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