https://github.com/mohd-faizy/05p_understanding_deepfakes_with_keras_using_dcgan

Understanding Deepfakes with Keras
https://github.com/mohd-faizy/05p_understanding_deepfakes_with_keras_using_dcgan

convolutional-neural-networks dcgan deepfakes generative-adversarial-network

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Understanding Deepfakes with Keras

• Host: GitHub
• URL: https://github.com/mohd-faizy/05p_understanding_deepfakes_with_keras_using_dcgan
• Owner: mohd-faizy
• Created: 2020-07-08T16:42:42.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-11-13T21:27:16.000Z (about 4 years ago)
• Last Synced: 2024-11-13T09:47:16.684Z (3 months ago)
• Topics: convolutional-neural-networks, dcgan, deepfakes, generative-adversarial-network
• Language: Jupyter Notebook
• Homepage:
• Size: 42 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# __Understanding-Deepfakes-with-Keras-Using-DCGAN__



## __Dataset__

__MNIST__(Modified National Institute of Standards and Technology database)

```python

# Downloding the dataset

(x_train, y_train), (x_test, y_test) = tfutils.datasets.mnist.load_data(one_hot=False)

# Loading the Subsets that belong to the class zero 

# So the x_train, x_test have the images of only '0'

x_train = tfutils.datasets.mnist.load_subset([0], x_train, y_train)

x_test = tfutils.datasets.mnist.load_subset([0], x_test, y_test)

# Creating the Combined set Using the NumPy Concatenate function

x = np.concatenate([x_train, x_test], axis=0)

```

## :heavy_check_mark: Objectives

:one: Implement a Deep Convolutional Generative Adversarial Network (DCGAN).

    

:two: Train a DCGAN to synthesize realistic looking images.

By the end of this course, you will understand how to implement DCGANs, and how to train them to generate realistic synthetic images.

## Task 1: Introduction

    - Introduction to the DCGANS

## Task 2: Importing and Plotting the Data

    - Importing the MNIST Dataset

    - Creating a subset of the dataset for just one class.

    - Visualizing the subset.

## Task 3: Discriminator

    - Basic understanding of how a GAN works.

    - Creating a Discriminator Network.

    - Creating an optimizer instance.

## Task 4: Generator

    - Creating a Generator Network.

    - Generating a new image from the untrained Generator model.

## Task 5: Generative Adversarial Network (GAN)

    - Connecting the Generator and Discriminator to create a Generative Adversarial Network (GAN)

    

 

## Task 6: Training the GAN

    - Creating a training loop.

    - Creating a dynamic plot that displays generated images after each epoch.

    

 --- 

### Connect with me:

[][twitter]

[][linkedin]

[][StackExchange AI]

[twitter]: https://twitter.com/F4izy

[linkedin]: https://www.linkedin.com/in/faizy-mohd-836573122/

[StackExchange AI]: https://ai.stackexchange.com/users/36737/cypher

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