In this Python tutorial, we will learn** how to use TensorFlow Placeholder in Python**. Also, we will cover the following topics.

- TensorFlow Placeholder vs variable
- TensorFlow Placeholder shape
- TensorFlow Placeholder replacement
- TensorFlow Placeholder example
- TensorFlow Placeholder name
- TensorFlow Placeholder shape none
- TensorFlow placeholder with default
- TensorFlow replace placeholder with constant
- TensorFlow feed placeholder with tensor
- TensorFlow placeholder not compatible with eager execution
- TensorFlow has no attribute ‘placeholder_with_default’
- TensorFlow initialize variable with placeholder
- TensorFlow placeholder error
- TensorFlow placeholder to numpy
- TensorFlow add placeholder to graph
- TensorFlow placeholder unkown shape
- TensorFlow check if placeholder
- TensorFlow iterate over placeholder
- TensorFlow get shape of placeholder
- TensorFlow placeholder feed_dict
- TensorFlow placeholder bool

## Python TensorFlow Placeholder

- In this section, we will discuss how to use the placeholder in Python TensorFlow.
- In TensorFlow, the placeholder is a variable that assigns data and feeds values into a computation graph. This method allows the user to provide the data for operation and generate our computation graph.
- In Python, if we want to initialize some data then we have used variables but in placeholder allow you to feed data into a computation graph.

**Syntax:**

Let’s have a look at the Syntax and understand the working of the **tf.placeholder() **function in Python TensorFlow.

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

- It consists of a few parameters
**dtype:**This parameter indicates which type of elements in the tensor.**shape:**By default, it takes none value and if you do not mention the shape in tensor then you can feed a tensor of any shape.**name:**This parameter specifies the name of the operation and it is an optional parameter.

**Example:**

Let’s take an example and check how to create a **Placeholder in Python TensorFlow**.

**Source Code:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
tens1= tf.compat.v1.placeholder(tf.float32)
tens2 = tf.compat.v1.placeholder(tf.float32)
c = tf.math.multiply(tens1, tens2)
with tf.compat.v1.Session() as val:
new_output=val.run(c, feed_dict={tens1: 7.8, tens2: 9.10})
print(new_output)
```

- In the above code, we have imported the TensorFlow library and then we have declared two placeholders and its datatype is
**tf.float32()**. - After that we performed operation by using the
**tf.math.multiply**and then created the session by importing the**tf.compat.v1.disable_eager_execution()**function. - While creating the session we have assigned the
**feed_dict**as an argument.

Here is the Screenshot of the following given code.

Also, check: TensorFlow Tensor to numpy

## TensorFlow Placeholder vs variable

- In this section we will discuss the difference between Placeholder and variable in Python TensorFlow.
- In Python TensorFlow, the variable specified the tensor whose elements can be modified by running an operation on it. While in the case of Placeholder it is used to insert the external data into the computation graph and the data will be assigned later.
- In Tensorflow if you want to add variables in graphs then you can easily call the constructor and while creating tensor we have the same datatype as the initialization value.
- In
**tf.placeholder()**we can easily store the value later in the session as**feed_dict**. If we don’t pass any value while running the session then it will generate an error.

**Syntax:**

Let’s have a look at the Syntax and understand the working of the **tf.variable()** function in Python TensorFlow.

```
tf.variable
(
initial_value=None,
trainable=None,
validate_shape=True,
caching_device=None,
name=None,
variable_def=None,
dtype=None,
import_scope=None,
constraint=None
)
```

**Example:**

Let’s take an example and check the difference between the Placeholder and variable.

**Source Code:**

```
import tensorflow as tf
#creation of variables
tens=tf.Variable([[2,3,4,5],
[34,98,67,44]])
tens2=tf.Variable([[6,36,98,45],
[23,178,278,345]])
result=tf.add(tens,tens2)
print("Addition of two variable ",result)
#Creation of placeholder
tf.compat.v1.disable_eager_execution()
new_tensor1= tf.compat.v1.placeholder(tf.int32)
new_tensor2 = tf.compat.v1.placeholder(tf.int32)
z = tf.add(new_tensor1, new_tensor2)
with tf.compat.v1.Session() as val:
new_output=val.run(z, feed_dict={new_tensor1:67, new_tensor2: 89})
print(new_output)
```

In the following given code first, we have created two variables by using the **tf.variable()** function and then operated by** tf.add() **function, To create a simple variable we don’t need to create a session. while in the case of placeholder we have to create a session by importing the **tf.compat.v1.disable_eager_execution()** function.

After creating a session we have assigned the** feed_dict **as an argument. Once you will execute this code the output displays the addition of a given tensor.

Here is the implementation of the following given code.

Read: TensorFlow get shape

## TensorFlow Placeholder Shape

- In this example we are going to pass the shape parameter in
**tf.placeholder()**function by using the Python TensorFlow. - To perform this particular task we are going to use the
**tf.compat.v1.placeholder()**function for creating the variables and within this function, we will pass the datatype and shape as an argument. - Next, we will use the
**np.zeros()**function and inside this function, we will set the same shape and then create and run the session we are going to use the**sess.run()**and assign the**feed_dict()**in it.

**Example:**

Let’s take an example and check how to get the shape value in the form of a zeros array.

**Source Code:**

```
import tensorflow as tf
import numpy as np
tf.compat.v1.disable_eager_execution()
tens1= tf.compat.v1.placeholder(tf.float32,shape=(3, 4))
tens2 = tf.compat.v1.placeholder(tf.float32,shape=(3, 4))
result = tf.square(tens1) * tf.square(tens2)
new_arr= np.zeros((3,4))
with tf.compat.v1.Session() as val:
new_output=val.run(result, feed_dict={tens1:new_arr,tens2:new_arr})
print(new_output)
```

Here is the execution of the following given code.

As you can see in the Screenshot the output displays zeros value in the shape of(3,4).

Read: Python TensorFlow reduce_sum

## TensorFlow Placeholder Example

- In this Program, we will discuss how to create a Placeholder in Python TensorFlow.
- To do this task first we will create a variable by using the
**tf.compat.v1.placeholder()**function and within this method, we will mention the**datatype, shape, and name**as an argument. - Next, we will use the square operator for multiplying the given input and in this example, the given input is one’s value in the shape of
**(3,4)**.

**Example:**

Let’s take an example and check how to use the **placeholder()** function in Python TensorFlow.

**Source Code:**

```
import tensorflow as tf
import numpy as np
tf.compat.v1.disable_eager_execution()
new_tensor= tf.compat.v1.placeholder(tf.float32,shape=(3, 4),name='tensor')
z = new_tensor *2
new_arr= np.ones((3,4))
with tf.compat.v1.Session() as val:
new_output=val.run(z, feed_dict={new_tensor:new_arr})
print(new_output)
```

Once you will execute this code the output displays the multiply of **2** by one’s value which means the input array has been multiplied by twice time.

Here is the Screenshot of the following given code.

Read: Python TensorFlow reduce_mean

## TensorFlow Placeholder name

- Here we are going to use the name parameter in
**tf.Placeholder()**function by using Python TensorFlow. - In this example, we will perform the subtract operation on the given placeholder. To perform this task we are going to use the
**tf.math.subtract()**function.

**Syntax:**

Here is the Syntax of **tf.math.subtract()** function in Python TensorFlow.

```
tf.math.subtract
(
x,
y,
name=None
)
```

**Example:**

Let’s take an example and check how will name parameter used in the tf.placeholder() function.

**Source Code:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
tens1= tf.compat.v1.placeholder(tf.int32,name='tens1')
tens2 = tf.compat.v1.placeholder(tf.int32,name='tens2')
c = tf.math.subtract(tens1, tens2)
with tf.compat.v1.Session() as val:
new_output=val.run(c, feed_dict={tens1: 87, tens2: 76})
print("Subtraction of two tensors:",new_output)
```

Here is the implementation of the following given code.

Read: Python TensorFlow random uniform

## TensorFlow Placeholder shape none

- In this section, we will discuss how to set the none value as a shape in
**tf.placeholder()**function by using Python TensorFlow. - In this example we have mention shape parameter is none in
**tf.compat.v1.placeholder()**function.

**Example:**

Let’s take an example and understand the working of the **tf.placeholder()** function in Python TensorFlow.

**Source Code:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
a= tf.compat.v1.placeholder(tf.int32,shape=None)
b= tf.compat.v1.placeholder(tf.int32,shape=None)
result=tf.math.divide(a,b)
with tf.compat.v1.Session() as val:
new_output=val.run(result, feed_dict={a: 10, b: 20})
print("Division of two tensors without any shape:",new_output)
```

Here is the Screenshot of the following given code.

Read: Python TensorFlow one_hot

## TensorFlow placeholder with default

- In this section, we will discuss how to use the
**tf.compat.v1.placeholder_with_default()**in Python TensorFlow. - When there is no fed value in the output session a placeholder with default passes through a basic operation.

**Syntax:**

Let’s have a look at the Syntax and understand the working of **tf.compat.v1.placeholder_with_default()** function

```
tf.compat.v1.placeholder_with_default
(
input,
shape,
name=None
)
```

- It consists of a few parameters
**input:**This parameter indicates to generate the default value if there is no fed value in output.**shape:**This parameter specifies the shape of the tensor.**name:**This is an optional parameter and it indicates the name of the operation.

**Example:**

Let’s take an example and check how to use the default value in Placeholder TensorFlow.

**Source Code:**

```
import tensorflow as tf
tens=tf.compat.v1.placeholder_with_default(tf.constant([[12,45,67],[76,28,181]]),[None,3])
tens2=tf.constant([[17],[24],[38]])
result= tf.matmul(tens,tens2)
print(result)
```

In the above code we have imported the TensorFlow library and then use the **tf.compat.v1.placeholder_with_default()** function and within this method, we have used the **tf.constant()** function as a default value.

Here is the implementation of the following given code.

Read: TensorFlow cross-entropy loss

## TensorFlow feed placeholder with tensor

- In this section we will discuss how to use the fe
**ed_dict in Placeholder()**function in Python TensorFlow. - Tensorflow
**feed_dict()**defines the feed dictionary in which you can pass the specific values for a placeholder. To do this task first we will create two placeholders by using the**tf.compat.v1.placeholder()**function. - Next, we will operate the values by using the
**tf.add()**function and within this function, we are going to assign the placeholders and then create the session in which we will assign the feed_dict values.

**Example:**

Let’s take an example and check how to use the **feed_dict()** in the** Placeholder() **function.

**Source Code:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
tens1= tf.compat.v1.placeholder(tf.int32)
tens2 = tf.compat.v1.placeholder(tf.int32)
c = tf.add(tens1, tens2)
with tf.compat.v1.Session() as val:
new_output=val.run(c, feed_dict={tens1: 72, tens2: 89})
print(new_output)
```

Here is the execution of the following given code

Read: TensorFlow mean squared error

## TensorFlow placeholder not compatible with eager execution

Here we are going to discuss the error TensorFlow placeholder not compatible with eager execution in Python TensorFlow. Basically this error statement comes when we did not import the **tf.compat.v1.disable_eager_execution()** function.

**Example:**

```
import tensorflow as tf
tens = tf.compat.v1.placeholder(tf.int32)
print(tens)
```

In the above code we have imported the TensorFlow library and used the **tf.compat.v1.placeholder()** for creating the placeholder.

Here is the Screenshot of the following given code

As you can see in the Screenshot the output displays the error **tf.placeholder()** is not compatible with eager execution.

Now let’s see the solution to this error

**Solution:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
tens = tf.compat.v1.placeholder(tf.int32)
print(tens)
```

In the above code we have imported the **tf.compat.v1.disable_eager_execution() **function and then use the** tf.placeholder()** function and within this function we have assigned the datatype as an argument.

Here is the implementation of the following given code

## TensorFlow has no attribute ‘placeholder_with_default’

In this section, we will discuss the error TensorFlow has no attribute ‘Placeholder_with_default in Python. Basically this error statement comes when we did not mention the** compat.v1() in placeholder_with_default()** function.

**Example:**

```
import tensorflow as tf
tens1=tf.placeholder_with_default(tf.constant([[89,178,267],[13,56,55]]),[None,3])
tens2=tf.constant([[67],[43],[26]])
new_output= tf.matmul(tens1,tens2)
print(new_output)
```

In the above code we have imported the TensorFlow library and then use the default value in the **tf.placeholder_with_default()** function and then we have operated by using the **tf.matmul() **function.

Here is the Screenshot of the following given code

As you can see in the Screenshot the output displays the error Tensorflow has no attribute **‘placeholder_with_default’**.

Now let’s see the solution to this error

Solution

```
import tensorflow as tf
tens1=tf.compat.v1.placeholder_with_default(tf.constant([[89,178,267],[13,56,55]]),[None,3])
tens2=tf.constant([[67],[43],[26]])
new_output= tf.matmul(tens1,tens2)
print(new_output)
```

In the following given code we have used the **tf.compat.v1.placeholder_with_default()** function instead of** tf.placeholder_with_default()** function.

Here is the Output of the following given code

## TensorFlow initialize variable with placeholder

- In this section, we will discuss how to initialize variable with placeholder in Python TensorFlow.
- In Python TensorFlow, we cannot initialize variables with a placeholder in Tensorflow. Instead of creating variables, we can easily pass the values in
**feed_dict()**.

## TensorFlow placeholder error

In this Program, we will discuss the error **AttributeError: module ‘TensorFlow has no attribute ‘Placeholder’**. Basically this statement error comes when we did not import the **tf.compat.v1.disable_eager_execution()** function while creating a placeholder variable.

**Example:**

```
import tensorflow as tf
tens=tf.placeholder("tf.int32")
print(tens)
```

You can refer to the below Screenshot

As you can see in the Screenshot the Output displays the **AttributeError: module ‘TensorFlow’ has no attribute ‘placeholder’**.

Now let’s see the solution to this error

**Solution:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
new_result=tf.compat.v1.placeholder(tf.int32)
print(new_result)
```

In the above code we have imported the **tf.compat.v1.disable_ea**ger_execution() function and then used the** tf.compat.v1.placeholder()**. Once you will execute this code the output displays the type of placeholder.

Here is the Screenshot of the following given code

Read: TensorFlow global average pooling

## TensorFlow placeholder to numpy

- In this section, we will discuss how to convert the placeholder to numpy in Python TensorFlow.
- To perform this particular task we are going to use the
**tf.compat.v1.placeholder()**function and assign the**datatype, shape**as an argument. - Next, we will create the numpy array by using the
**np.ones()**function and within this function, we have mentioned the shape.

**Example:**

```
import tensorflow as tf
import numpy as np
tf.compat.v1.disable_eager_execution()
new_tens= tf.compat.v1.placeholder(tf.float32,shape=(2, 2),name='tensor')
z = new_tens *2
new_arr= np.ones((2,2))
with tf.compat.v1.Session() as val:
new_output=val.run(z, feed_dict={new_tens:new_arr})
print(new_output)
print(type(new_output))
```

Here is the implementation of the following given code

Read: Module ‘tensorflow’ has no attribute ‘mul’

## TensorFlow add placeholder to graph

- In this section, we will discuss how to add a placeholder to the graph in Python TensorFlow.
- To perform this particular task we are going to use the
**tf.graph()**and this method defines the units of data that flow between operations. - In this example, we will use the
**tf.compat.v1.placeholder()**function and within this function, we have assigned the datatype and shape as an argument.

**Syntax:**

Here is the Syntax of **tf.graph()** function in Python TensorFlow

`tf.graph()`

**Example:**

Let’s take an example and check how to add a placeholder to the graph in Python TensorFlow.

**Source Code:**

```
import tensorflow as tf
import numpy as np
tf.compat.v1.disable_eager_execution()
new_graph = tf.Graph()
with new_graph.as_default():
new_tens = tf.compat.v1.placeholder(tf.int32, shape=(2, 2))
new_tens2 = tf.compat.v1.placeholder(tf.int32, shape=(2, 2))
b = tf.math.multiply(new_tens, new_tens2)
new_arr = np.zeros((2,2))
with tf.compat.v1.Session(graph=new_graph) as session:
output = session.run(b, feed_dict={new_tens: new_arr,new_tens2: new_arr})
print(output)
```

Here is the implementation of the following given code

## TensorFlow placeholder unkown shape

- In this section, we will discuss how to get the unknown shape in Placeholder in Python TensorFlow.
- In this example, we don’t know about the shape and rank of the tensor and now in this Program, we are going to use the
**tf.compat.v1.placeholder()**function and within this function, we have assigned the shape parameter as an argument.

**Example:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
tens =tf.compat.v1.placeholder(tf.int32, shape=[None,None,None])
print(tens)
```

You can refer to the below Screenshot

As you can see in the Screenshot the Output displays shape as a none value.

## TensorFlow check if placeholder

In this section, we will check if the** placeholder()** function is available in Tensor or not. To do this task we are going to use the** isinstance()** method. In Python, the **isinstance() **method will check the condition if the given object is an instance then it will be true otherwise false.

**Example:**

Let’s take an example and check whether a placeholder is available or not.

Source Code:

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
result=isinstance(tf.compat.v1.placeholder("float", []), tf.Tensor)
# if placeholder contains then returns 'true'
print(result)
```

In the following given code, we have imported the Tensorflow library and then use the **tf.compat.v1.placeholder()** function along with the **isinstance() **method. Once you will execute this code the output returns a **‘true’ **value.

Here is the execution of the following given code.

## TensorFlow iterate over placeholder

- In this section, we will discuss how to iterate over placeholders in Python TensorFlow.
- To perform this particular task we are going to use the for-loop() method while creating the session.
- In this example, we will use the
**tf.compat.v1.placeholder()**function, and then by using the for loop method we can easily iterate the placeholder values which has been assigned in**feed_dict()**.

**Example:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
tens = tf.compat.v1.placeholder(tf.int32, shape=[3])
with tf.compat.v1.Session() as val:
for i in range(3):
print(val.run(tens[i], feed_dict={tens : [34,45,76]}))
```

Here is the Screenshot of the following given code

As you can see in the Screenshot the output returns the placeholder values.

## TensorFlow get shape of placeholder

- In this Program, we will discuss how to get the shape of a placeholder in Python TensorFlow.
- In this example, we are going to use the
**tf.shape()**function and this function is used to get the shape of a given object.

**Syntax:**

Here is the Syntax of **tf.shape()** function.

```
tf.shape
(
input,
out_type=tf.dtypes.int32,
name=None
)
```

- It consists of a few parameters:
**input:**This parameter indicates the input of the tensor.**out_type:**By default it takes**tf.dtypes.int32**and it is an optional parameter.**name:**This parameter defines the name of the operation.

**Example:**

Let’s take an example and check how to extract the shape of the given object in Python TensorFlow.

**Source Code:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
tens = tf.compat.v1.placeholder(tf.int32, shape=[3])
result=tf.shape(tens)
print(result)
```

Here is the execution of the following given code.

As you can see in the Screenshot the output displays the shape of a given placeholder.

## TensorFlow placeholder feed_dict

- In this section, we will discuss how to use the
**feed_dict()**function in Python TensorFlow. - To perform this particular task we are going to use the
**tf.compat.v1.placeholder()**function and the placeholder is a variable that assigns data and feeds values into a computation graph.

**Syntax:**

Here is the Syntax of **tf.compat.v1.placeholder()** function in Python TensorFlow.

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

**Example:**

Let’s take an example and check how to use the feed_dict() in Python

**Source Code:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
input1= tf.compat.v1.placeholder(tf.int32)
input2 = tf.compat.v1.placeholder(tf.int32)
result = tf.math.multiply(input1, input2)
with tf.compat.v1.Session() as val:
new_output=val.run(result, feed_dict={input1: 67, input2: 28})
print(new_output)
```

Here is the execution of the following given code

Read: Module ‘tensorflow’ has no attribute ‘get_variable’

## TensorFlow placeholder bool

- In this section, we will discuss how to get the boolean value from Placeholder in Python TensorFlow.
- To do this task, we are going to use the isinstance method along with the
**tf.compat.v1.placeholder()**function and within this function, we will assign the datatype and**tf.tensor()**as an argument.

**Example:**

Let’s take an example and check how to get the boolean value from Placeholder.

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
new_output=isinstance(tf.compat.v1.placeholder(tf.int32, []), tf.Tensor)
print(new_output)
```

Here is the implementation of the following given code.

In this Python tutorial, we have learned **how to use TensorFlow Placeholder in Python**. Also, we have covered the following topics.

- TensorFlow Placeholder vs variable
- TensorFlow Placeholder shape
- TensorFlow Placeholder replacement
- TensorFlow Placeholder example
- TensorFlow Placeholder name
- TensorFlow Placeholder shape none
- TensorFlow placeholder with default
- TensorFlow replace placeholder with constant
- TensorFlow feed placeholder with tensor
- TensorFlow placeholder not compatible with eager execution
- TensorFlow has no attribute ‘placeholder_with_default’
- TensorFlow initialize variable with placeholder
- TensorFlow placeholder error
- TensorFlow placeholder to numpy
- TensorFlow add placeholder to graph
- TensorFlow placeholder unkown shape
- TensorFlow check if placeholder
- TensorFlow iterate over placeholder
- TensorFlow get shape of placeholder
- TensorFlow placeholder feed_dict
- TensorFlow placeholder bool

I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.