python-tensorflowHow do I use Python and TensorFlow Placeholders?

Python and TensorFlow Placeholders are used to feed data into a TensorFlow graph. A placeholder is a variable that we can assign data to at a later point. It allows us to create our operations and build our computation graph, without needing the data.

Example code

import tensorflow as tf

# Create a placeholder of type float 32-bit, shape is a vector of 3 elements
a = tf.placeholder(tf.float32, shape=[3])

# Create a constant of type float 32-bit, shape is a vector of 3 elements
b = tf.constant([5, 5, 5], tf.float32)

# Use the placeholder as you would a constant
c = a + b  # Short for tf.add(a, b)

with tf.Session() as sess:
    # Feed [1, 2, 3] to placeholder a via the dict {a: [1, 2, 3]}
    # fetch value of c
    print(sess.run(c, {a: [1, 2, 3]}))

Output example

[6. 7. 8.]

The code above consists of the following parts:

• import tensorflow as tf imports the TensorFlow library.
• a = tf.placeholder(tf.float32, shape=[3]) creates a placeholder of type float 32-bit, shape is a vector of 3 elements.
• b = tf.constant([5, 5, 5], tf.float32) creates a constant of type float 32-bit, shape is a vector of 3 elements.
• c = a + b uses the placeholder as you would a constant.
• print(sess.run(c, {a: [1, 2, 3]})) feeds [1, 2, 3] to placeholder a via the dict {a: [1, 2, 3]} and fetches the value of c.

Helpful links

• TensorFlow Placeholders
• TensorFlow Tutorial.

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