In this Python tutorial, we will learn** how to clip a Tensor by value in Python TensorFlow**. Also, we will cover the following topics.

- TensorFlow clip_by_value
- TensorFlow clip_by_value gradient
- TensorFlow clip_by_value function
- TensorFlow clip_by_value relu
- TensorFlow clip_by_value unique

## Tensorflow clip_by_value

- In this section, we will discuss how to clip a Tensor by value in Python TensorFlow.
- To perform this particular task, we are going to use the
**tf.clip_by_value()**function. - And the
**tf.clip_by_value()**is represented to clip a Tensor value to a given minimum and maximum number.

**Syntax:**

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

```
tf.clip_by_value
(
t,
clip_value_min,
clip_value_max,
name=None
)
```

- It consists of a few parameters
**t:**This parameter indicates the input tensor.**clip_value_min:**This parameter specifies the minimum clip value and it is broadcastable to the shape of tensor.**clip_value_max:**This parameter indicates the maximum clip value and it is broadcastable to the shape of tensor.- name: By default it takes none value and it specifies the name of the operation.

**Example:**

Let’s take an example and check how to clip a Tensor by value in Python TensorFlow.

**Source Code:**

```
import tensorflow as tf
tensor = tf.constant([[-5., -2., 1.], [1., 3., 5.]])
new_result = tf.clip_by_value(tensor, clip_value_min=-1, clip_value_max=1)
output=new_result.numpy()
print(output)
```

In the above code, we have imported the TensorFlow library and then created the tensor by using the** tf.constant()** function. And within this function, we have assigned the integer positive and negative values to it.

After that, we have used the **tf.clip_by_value()** function and assigned the input tensor along with minimum and maximum value as an argument.

Here is the implementation of the following given code.

Read: TensorFlow Graph – Detailed Guide

## TensorFlow clip_by_value gradient

- In this Program, we will discuss how to use the gradient clipping in Python TensorFlow.
- First, we will discuss gradient clipping and which is a function where the derivative is modified or clipped to a threshold through the network and is also used to modify the weights.
- There are two ways to execute the gradient clipping, clipping by value and clipping by the norm. In this example, we will define a minimum and maximum clip value.

**Example:**

Let’s take an example and check how to use gradient clipping in Python TensorFlow.

**Source Code:**

```
import tensorflow as tf
from tensorflow.keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255., x_test / 255.
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128,activation='relu'),
tf.keras.layers.Dense(10)
])
model.compile(
optimizer=tf.keras.optimizers.Adam(clipvalue=0.5),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],
)
```

In the following given code, we created a dataset model of **mnist.load_data()** by importing from **TensorFlow.Keras.datasets** import **mnist** package. After creating the model we have to train and test the given model.

Here is the implementation of the following given code.

Read; Python TensorFlow Placeholder

## TensorFlow clip_by_value relu

- In this section, we will discuss how to use the relu activation function in
**clip_by_value()**Tensorflow Python. - To perform this particular task we, are going to use the
**tf.keras.layers.ReLU()**function and this function will help the user to rectify the linear activation function that is relu and it is used in a convolutional neural network.

**Syntax:**

Let’s have a look at the Syntax and understand the working of** tf.Keras.layers.ReLU()** function.

```
tf.keras.layers.ReLU
(
max_value=None,
negative_slope=0.0,
threshold=0.0,
**kwargs
)
```

- It consists of a few parameter
**max_value:**By default it takes none value and it specifies the maximum activation value.**negative_slope:**By default it takes 0 value and it indicates negative slope collection.**threshold:**By default it takes 0 value and it specifies the threshold value value for threshold activation.

**Example:**

```
import tensorflow as tf
tensor = tf.keras.layers.ReLU()
new_result = tensor([-4.0, -2.0, 0.0, 0.0])
list(new_result.numpy())
```

In the following given code, we have imported the TensorFlow library and then we have used **tf.Keras.layers.ReLU()** function. Once you will execute this code the output displays the zero values.

Here is the Screenshot of the following given code.

Read: Python TensorFlow expand_dims

## TensorFlow clip_by_value function

- In this example, we will discuss how to clip a Tensor by value in Python TensorFlow.
- To perform this particular task, we are going to use the
**tf.clip_by_value()**function and this function is represented to clip a Tensor value to a given minimum and maximum number.

**Syntax:**

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

```
tf.clip_by_value
(
t,
clip_value_min,
clip_value_max,
name=None
)
```

**Example:**

```
import tensorflow as tf
tensor = tf.constant([[7, 8], [2, 1]],dtype = tf.int32)
min_val = [8, 6]
max_val = [2, 3]
new_result = tf.clip_by_value(tensor, min_val, max_val)
print(new_result)
```

In the above code, we have imported the TensorFlow library and then created the tensor by using the **tf.constant()** function. And within this function, we have assigned the integer positive and negative values to it.

After that, we have used the **tf.clip_by_value()** function and assigned the input tensor along with minimum and maximum value as an argument.

You can refer to the below Screenshot.

Read: Python TensorFlow random uniform

## TensorFlow clip_by_value unique

- In this section, we will discuss how to get the unique values from clip_by_value in Python TensorFlow.
- To perform this particular task, first, we will create a tensor by using the
**tf.constant()**function and assign the integers values to it. - Next, we will use the
**tf.clip_by_value()**function to clip a Tensor by value, and then we are going to apply the**tf.unique()**function and get the unique values from it.

**Syntax:**

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

```
tf.unique
(
x,
out_idx=tf.dtypes.int32,
name=None
)
```

**Example:**

```
import tensorflow as tf
tensor = tf.constant([-5., -2., 1.,1., 3., 5.])
new_result = tf.clip_by_value(tensor, clip_value_min=-1, clip_value_max=1)
output=new_result.numpy()
new_result=tf.unique(output)
print(new_result)
```

Here is the execution of the following given code.

Also, take a look at some more TensorFlow tutorials.

- Python TensorFlow reduce_mean
- Python TensorFlow reduce_sum
- TensorFlow cross-entropy loss
- TensorFlow global average pooling
- TensorFlow Get Variable + Examples
- TensorFlow mean squared error
- Module ‘tensorflow’ has no attribute ‘truncated_normal’
- Gradient descent optimizer TensorFlow

In this Python tutorial, we have learned** how to clip a Tensor by value in Python TensorFlow**. Also, we have covered the following topics.

- TensorFlow clip_by_value
- TensorFlow clip_by_value gradient
- TensorFlow clip_by_value function
- TensorFlow clip_by_value relu
- TensorFlow clip_by_value unique

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