TensorFlow Clip_by_value

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

Table of Contents

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.

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

Bijay Kumar

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.

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