placeholder tensorflow 2.0 code example

Example 1: tf.placeholder()

Inserts a placeholder for a tensor that will be always fed.
A placeholder is simply a variable that we will assign data to 
at a later date. It allows us to create our operations and build 
our computation graph, without needing the data. In TensorFlow
terminology, we then feed data into the graph through these 
placeholders.

tf.compat.v1.placeholder(
    dtype, shape=None, name=None
)

Important: This tensor will produce an error if evaluated. 
Its value must be fed using the feed_dict optional argument 
to Session.run(), Tensor.eval(), or Operation.run().

Example:
x = tf.compat.v1.placeholder(tf.float32, shape=(1024, 1024))
y = tf.matmul(x, x)
with tf.compat.v1.Session() as sess:
  print(sess.run(y))  # ERROR: will fail because x was not fed.
  rand_array = np.random.rand(1024, 1024)
  print(sess.run(y, feed_dict={x: rand_array}))  # Will succeed.

Example 2: tensorflow placeholder

import tensorflow as tf

x = tf.placeholder("float", [None, 3])
y = x * 2

with tf.Session() as session:
    x_data = [[1, 2, 3],
              [4, 5, 6],]
    result = session.run(y, feed_dict={x: x_data})
    print(result)

Example 3: tensorflow placeholder

import tensorflow as tf

x = tf.placeholder("float", None)
y = x * 2

with tf.Session() as session:
    result = session.run(y, feed_dict={x: [1, 2, 3]})
    print(result)