TensorFlow Feed_dict

Let’s understand TensorFlow feed_dict in this article.

Use feed_dict in TensorFlow

We need placeholders and feed dictionaries to be able to run the same model on multiple problem sets. Our visualization must keep up with the complexity of our TensorFlow application.

TensorFlow placeholders are the same as variables anybody may specify, even during runtime, using the feed_dict parameter. TensorFlow uses the feed_dict option to feed values to these placeholders to avoid an error that requests you to feed a value for placeholders in TensorFlow.

TensorFlow placeholders are comparable to variables and may be declared using tf.placeholder.

You do not need to supply an initial value. You may specify it at runtime using the feed_dict parameter within Session.run, whereas tf.Variable requires an initial value when declared.

Every session includes fetches and feed_dict. The fetches argument indicates what we want to compute, whereas the feed dictionary offers placeholder values for that computation.

Let’s look at the syntax and how the tf.compat.v1.placeholder() method in Python TensorFlow works.

Syntax:

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

Parameters:

1. The dtype option indicates the kind of elements in the tensor.
2. Shape accepts no value by default, and if you don’t specify a shape in the tensor, you can feed any tensor.
3. Name is an optional argument that gives the operation’s name.

Let’s look at an example of using TensorFlow to generate a feed_dict in placeholder.

First, import the library which is required for the task.

Then create a disable_eager_execution that will handle the placeholder conflict with TensorFlow versions.

After that, declare a placeholder of the int32 data type.

Then perform actions on the placeholder. For that, we have used the multiply function tf.math.multiply.

We assigned the feed_dict as an argument when we created the session. The placeholder values are provided via feed_dict.

Here is the screenshot of the full code.