TensorFlow Machine Learning Projects

Placeholders

While constants store the value at the time of defining the tensor, placeholders allow you to create empty tensors so that the values can be provided at runtime. The TensorFlow library provides the tf.placeholder() function with the following signature to create placeholders:

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

As an example, let's create two placeholders and print them:

p1 = tf.placeholder(tf.float32)p2 = tf.placeholder(tf.float32)print('p1 : ', p1)print('p2 : ', p2)

The following output shows that each placeholder has been created as a tensor:

p1 :  Tensor("Placeholder:0", dtype=float32)p2 :  Tensor("Placeholder_1:0", dtype=float32)

Let's define an operation using these placeholders:

mult_op = p1 * p2

In TensorFlow, shorthand ...