How to build a GAN from scratch with library, PyTorch

How to build a GAN from scratch with library, PyTorch

A Generative Adversarial Network (GAN) is a type of neural network that is designed to generate new, previously unseen data that is similar to a training dataset. GANs consist of two neural networks: a generator network and a discriminator network. The generator network is responsible for generating new data, while the discriminator network is responsible for determining whether the data is real (from the training dataset) or fake (generated by the generator).

Here is an example of how you can build a GAN from scratch using Python and the popular machine learning library, PyTorch:

1. First, you will need to install PyTorch and other required libraries. You can install PyTorch using pip install torch.
2. Next, you will need to define the generator and discriminator networks. You can do this by subclassing the nn.Module class and defining the __init__ and forward methods. The generator network should take in a random noise vector as input and generate a new data sample as output, while the discriminator network should take in a data sample as input and output a single scalar value representing the probability that the data is real.
3. Once you have defined the generator and discriminator networks, you will need to define the loss function and optimizers for each network. For the generator, you can use a loss function such as binary cross-entropy loss, which measures the difference between the generated data and the real data. For the discriminator, you can use a similar loss function such as binary cross-entropy loss, but with the labels flipped. You will also need to define separate optimizers for the generator and discriminator, such as Adam or SGD.
4. Next, you will need to write a training loop to train the GAN. In each iteration of the loop, you should:

• Generate a random noise vector and use it to generate a new data sample using the generator network
• Combine the generated data with a batch of real data and pass it through the discriminator network
• Calculate the loss for both the generator and discriminator using the loss function and optimizers defined in step 3
• Perform backpropagation and optimize the weights of the generator and discriminator networks using the calculated losses

1. Finally, you can use the trained generator network to generate new data by passing it a random noise vector.

Here is some example code that demonstrates how to build a GAN from scratch using PyTorch:

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import torch import torch.nn as nn import torch.optim as optim # Define the generator network class Generator(nn.Module): def __init__(self, input_size, hidden_size, output_size): super().__init__() self.fc1 = nn.Linear(input_size, hidden_size) self.fc2 = nn.Linear(hidden_size, hidden_size) self.fc3 = nn.Linear(hidden_size, output_size) def forward(self, x): x = torch.relu(self.fc1(x)) x = torch.relu(self.fc2(x)) x = torch.tanh(self.fc3(x)) return x # Define the discriminator network class Discriminator(nn.Module): def __init__(self, input_size, hidden_size, output_size): super().__init__ 

import torch import torch.nn as nn import torch.optim as optim # Define the generator network class Generator(nn.Module): def __init__(self, input_size, hidden_size, output_size): super().__init__() self.fc1 = nn.Linear(input_size, hidden_size) self.fc2 = nn.Linear(hidden_size, hidden_size) self.fc3 = nn.Linear(hidden_size, output_size) def forward(self, x): x = torch.relu(self.fc1(x)) x = torch.relu(self.fc2(x)) x = torch.tanh(self.fc3(x)) return x # Define the discriminator network class Discriminator(nn.Module): def __init__(self, input_size, hidden_size, output_size): super().__init__() self.fc1 = nn.Linear(input_size, hidden_size) self.fc2 = nn.Linear(hidden_size, hidden_size) self.fc3 = nn.Linear(hidden_size, output_size) def forward(self, x): x = torch.relu(self.fc1(x)) x = torch.relu(self.fc2(x)) x = torch.sigmoid(self.fc3(x)) return x # Define the loss function and optimizers criterion = nn.BCELoss() d_optimizer = optim.Adam(discriminator.parameters(), lr=0.0002) g_optimizer = optim.Adam(generator.parameters(), lr=0.0002) # Set the number of epochs and the batch size num_epochs = 200 batch_size = 128 # Create a list to store the losses losses = [] # Training loop for epoch in range(num_epochs): for i, (real_data, _) in enumerate(data_loader): # Generate a batch of fake data noise = torch.randn(batch_size, input_size) fake_data = generator(noise) # Train the discriminator on real and fake data d_optimizer.zero_grad() real_loss = criterion(discriminator(real_data), torch.ones(batch_size, 1)) fake_loss = criterion(discriminator(fake_data), torch.zeros(batch_size, 1)) d_loss = real_loss + fake_loss d_loss.backward() d_optimizer.step() # Train the generator g_optimizer.zero_grad() g_loss = criterion(discriminator(fake_data), torch.ones(batch_size, 1)) g_loss.backward() g_optimizer.step() # Print the loss every 10 batches if i % 10 == 0:         

 print("Epoch {} Batch {}: D Loss: {:.4f} G Loss: {:.4f}".format(epoch, i, d_loss.item(), g_loss.item())) # Store the losses losses.append((d_loss.item(), g_loss.item())) # Generate new data using the trained generator noise = torch.randn(batch_size, input_size) generated_data = generator(noise)         

This code defines the generator and discriminator networks, the loss function and optimizers, and the training loop for the GAN. The generator network takes in a random noise vector and generates a new data sample, while the discriminator network takes in a data sample and outputs a probability that the data is real. The training loop performs backpropagation and optimizes the weights of the generator and discriminator networks in each iteration. Finally, the code generates new data using the trained generator by passing it a random noise

# Plot the losses import matplotlib.pyplot as plt d_losses, g_losses = zip(*losses) plt.plot(d_losses, label='Discriminator') plt.plot(g_losses, label='Generator') plt.legend() plt.show()         

This code plots the losses for the generator and discriminator during training. You can use this plot to monitor the training process and ensure that the GAN is learning effectively.

Use the trained GAN to generate new data in PyTorch:

# Generate new data using the trained generator noise = torch.randn(batch_size, input_size) generated_data = generator(noise) # Save the generated data to a file with open("generated_data.txt", "w") as f: for data in generated_data: f.write(" ".join([str(d) for d in data]) + "\n")         

This code generates a batch of new data using the trained generator by passing it a random noise vector. The generated data is then saved to a file, with each data sample being written as a line of space-separated values.

I hope this helps! Let me know if you have any questions or need further clarification.

# Load the trained generator and discriminator networks generator = Generator(input_size, hidden_size, output_size) discriminator = Discriminator(input_size, hidden_size, output_size) generator.load_state_dict(torch.load("generator.pt")) discriminator.load_state_dict(torch.load("discriminator.pt")) # Generate new data using the trained generator noise = torch.randn(batch_size, input_size) generated_data = generator(noise) # Use the trained discriminator to classify the generated data predictions = discriminator(generated_data) # Print the predictions print(predictions)         

This code loads the trained generator and discriminator networks from files, generates a batch of new data using the trained generator, and uses the trained discriminator to classify the generated data. The predictions are then printed to the console.

# Save the trained generator and discriminator networks torch.save(generator.state_dict(), "generator.pt") torch.save(discriminator.state_dict(), "discriminator.pt")         

This code saves the trained generator and discriminator networks to files using the torch.save function. The state_dict method returns a dictionary containing the model's weights and biases, which can be saved to a file and later used to load the model using the load_state_dict method.

import matplotlib.pyplot as plt # Generate new data using the trained generator noise = torch.randn(batch_size, input_size) generated_data = generator(noise) # Reshape the generated data to a 2D image generated_data = generated_data.view(batch_size, 28, 28) # Save the generated data as an image for i, data in enumerate(generated_data): plt.imsave("generated_{}.png".format(i), data, cmap='gray')         

This code generates a batch of new data using the trained generator, reshapes the data to a 2D image with dimensions 28x28, and saves each image to a separate file using the plt.imsave function from the matplotlib library. The cmap parameter specifies that the image should be saved in grayscale.

import cv2 # Set the video codec and the frame rate fourcc = cv2.VideoWriter_fourcc(*'mp4v') fps = 30 # Create the video writer video_writer = cv2.VideoWriter("generated.mp4", fourcc, fps, (28, 28)) # Generate new data using the trained generator for i in range(fps * duration): noise = torch.randn(batch_size, input_size) generated_data = generator(noise) # Reshape the generated data to a 2D image generated_data = generated_data.view(batch_size, 28, 28) # Convert the generated data to a NumPy array and write it to the video for data in generated_data: data = data.detach().cpu().numpy() data = (data * 255).astype(np.uint8) video_writer.write(data) # Release the video writer video_writer.release()         

This code generates new data using the trained generator in a loop and saves it to a video file using the cv2.VideoWriter class from the cv2 library. The fourcc parameter specifies the video codec to use, the fps parameter specifies the frame rate, and the (28, 28) tuple specifies the dimensions of the video frames. The generated data is reshaped to a 2D image, converted to a NumPy array, and written to the video using the write method of the VideoWriter object. Finally, the release method is called to release the video writer and complete the video file.

import soundfile as sf # Generate new data using the trained generator noise = torch.randn(batch_size, input_size) generated_data = generator(noise) # Reshape the generated data to a 1D array generated_data = generated_data.view(batch_size * output_size) # Convert the generated data to a NumPy array and save it to an audio file data = generated_data.detach().cpu().numpy() sf.write("generated.wav", data, 44100)         

This code generates new data using the trained generator, reshapes the data to a 1D array, and saves it to an audio file using the sf.write function from the soundfile library. The 44100 parameter specifies the sample rate of the audio data.

# Generate new data using the trained generator noise = torch.randn(batch_size, input_size) generated_data = generator(noise) # Save the generated data to a file with open("generated.txt", "w") as f: for data in generated_data: f.write(" ".join([str(d) for d in data]) + "\n")         

This code generates new data using the trained generator and saves it to a text file by writing each data sample as a line of space-separated values.

import csv # Generate new data using the trained generator noise = torch.randn(batch_size, input_size) generated_data = generator(noise) # Save the generated data to a CSV file with open("generated.csv", "w", newline='') as f: writer = csv.writer(f) writer.writerows(generated_data)         

This code generates new data using the trained generator and saves it to a CSV file using the csv library. The writerows method writes the data as a list of rows, with each data sample being written as a row of values.

import json # Generate new data using the trained generator noise = torch.randn(batch_size, input_size) generated_data = generator(noise) # Convert the generated data to a list of lists data = generated_data.tolist() # Save the generated data to a JSON file with open("generated.json", "w") as f: json.dump(data, f)         

This code generates new data using the trained generator, converts it to a list of lists, and saves it to a JSON file using the json library. The dump function converts the data to a JSON string and writes it to the file.

import h5py # Generate new data using the trained generator noise = torch.randn(batch_size, input_size) generated_data = generator(noise) # Convert the generated data to a NumPy array data = generated_data.detach().cpu().numpy() # Save the generated data to a HDF5 file with h5py.File("generated.h5", "w") as f: f.create_dataset("data", data=data)         

This code generates new data using the trained generator, converts it to a NumPy array, and saves it to a HDF5 file using the h5py library. The create_dataset method creates a new dataset in the file and stores the data in it.

import sqlite3 # Generate new data using the trained generator noise = torch.randn(batch_size, input_size) generated_data = generator(noise) # Connect to the database conn = sqlite3.connect("generated.db") # Create a table for the data conn.execute("CREATE TABLE data (value REAL)") # Insert the generated data into the table conn.executemany("INSERT INTO data (value) VALUES (?)", generated_data) # Commit the changes and close the connection conn.commit() conn.close()         

This code generates new data using the trained generator, connects to a SQLite database, creates a table for the data, and inserts the data into the table using the executemany method. The commit method is called to save the changes to the database, and the close method is called to close the connection.

def save_data(data, filename): # Convert the data to a NumPy array data = data.detach().cpu().numpy() # Save the data to a file with open(filename, "w") as f: for sample in data: f.write(" ".join([str(d) for d in sample]) + "\n") # Generate new data using the trained generator noise = torch.randn(batch_size, input_size) generated_data = generator(noise) # Save the generated data to a file save_data(generated_data, "generated.txt")         

This code defines a save_data function that takes a batch of data and a filename as inputs and saves the data to a file by writing each sample as a line of space-separated values. The code then generates new data using the trained generator and saves it to a file using the save_data function.

# Generate new data using the trained generator noise = torch.randn(batch_size, input_size) generated_data = generator(noise) # Save the generated data to a file torch.save(generated_data, "generated.pt")         

This code generates new data using the trained generator and saves it to a file using the torch.save function. The torch.save function serializes the data and saves it to a file in a format that can be loaded using the torch.load function.

PyTorch using the pickle module:

import pickle # Generate new data using the trained generator noise = torch.randn(batch_size, input_size) generated_data = generator(noise) # Save the generated data to a file with open("generated.pkl", "wb") as f: pickle.dump(generated_data, f)         

This code generates new data using the trained generator and saves it to a file using the pickle.dump function. The pickle.dump function serializes the data and saves it to a file in a format that can be loaded using the pickle.load function.

PyTorch using the np.save function:

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import numpy as np # Generate new data using the trained generator noise = torch.randn(batch_size, input_size) generated_data = generator(noise) # Convert the generated data to a NumPy array data = generated_data.detach().cpu().numpy() # Save the generated data to a file np.save("generated.npy", data)         

This code generates new data using the trained generator, converts it to a NumPy array, and saves it to a file using the np.save function. The np.save function saves the data in a binary format that can be loaded using the np.load function.

PyTorch using the tensorboard module:

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from torch.utils.tensorboard import SummaryWriter # Create a summary writer writer = SummaryWriter() # Generate new data using the trained generator noise = torch.randn(batch_size, input_size) generated_data = generator(noise) # Add the generated data to the summary writer for i, data in enumerate(generated_data): writer.add_scalar("generated/data_{}".format(i), data, global_step=0) # Close the summary writer writer.close()         

This code generates new data using the trained generator, creates a SummaryWriter object from the tensorboard module, and adds the data to the summary writer as scalars using the add_scalar method. The global_step parameter specifies the current training step, which can be used to track the data over time. Finally, the close method is called to close the summary writer and save the data to a file.

PyTorch using the torch.jit.save function:

# Generate new data using the trained generator noise = torch.randn(batch_size, input_size) generated_data = generator(noise) # Save the generated data to a file torch.jit.save(generated_data, "generated.pt")         

This code generates new data using the trained generator and saves it to a file using the torch.jit.save function. The torch.jit.save function saves the data in a serialized format that can be loaded using the torch.jit.load function.

PyTorch using the torch.save function and a custom data handler:

import pickle class CustomDataHandler: def __init__(self, data): self.data = data def __getstate__(self): # Return a dictionary containing the data return {"data": self.data} def __setstate__(self, state): # Load the data from the dictionary self.data = state["data"] # Generate new data using the trained generator noise = torch.randn(batch_size, input_size) generated_data = generator(noise) # Wrap the generated data in a custom data handler data_handler = CustomDataHandler(generated_data) # Save the custom data handler to a file torch.save(data_handler, "generated.pt")         

This code defines a CustomDataHandler class that wraps the generated data and implements the __getstate__ and __setstate__ methods to control how the data is serialized and deserialized by the torch.save and torch.load functions. The code then generates new data using the trained generator, wraps it in a CustomDataHandler object, and saves the object to a file using the torch.save function.

PyTorch using the torch.save function and a custom data handler that compresses the data using the gzip module:

import gzip import pickle class CustomDataHandler: def __init__(self, data): self.data = data def __getstate__(self): # Compress the data using gzip data_bytes = pickle.dumps(self.data) compressed_data = gzip.compress(data_bytes) # Return a dictionary containing the compressed data return {"compressed_data": compressed_data} def __setstate__(self, state): # Load the compressed data from the dictionary compressed_data = state["compressed_data"] # Decompress the data using gzip data_bytes = gzip.decompress(compressed_data) self.data = pickle.loads(data_bytes) # Generate new data using the trained generator noise = torch.randn(batch_size, input_size) generated_data = generator(noise) # Wrap the generated data in a custom data handler data_handler = CustomDataHandler(generated_data) # Save the custom data handler to a file torch.save(data_handler, "generated.pt")         

This code defines a CustomDataHandler class that wraps the generated data and implements the __getstate__ and __setstate__ methods to control how the data is serialized and deserialized by the torch.save and torch.load functions. The __getstate__ method compresses the data using the gzip module, and the __setstate__ method decompresses the data using the gzip module. The code then generates new data using the trained generator, wraps it in a CustomDataHandler object, and saves the object to a file using the torch.save function.

PyTorch using the torch.save function and a custom data handler that encrypts the data using the cryptography module:

import base64 from cryptography.fernet import Fernet import pickle class CustomDataHandler: def __init__(self, data, password): self.data = data self.password = password def __getstate__(self): # Encrypt the data using the password data_bytes = pickle.dumps(self.data) f = Fernet(self.password) encrypted_data = f.encrypt(data_bytes) # Encode the encrypted data using base64 encoded_data = base64.b64encode(encrypted_data) # Return a dictionary containing the encoded data return {"encoded_data": encoded_data} def __setstate__(self, state): # Load the encoded data from the dictionary encoded_data = state["encoded_data"] # Decode the encoded data using base64 encrypted_data = base64.b64decode(encoded_data) # Decrypt the data using the password f = Fernet(self.password) data_bytes = f.decrypt(encrypted_data) self.data = pickle.loads(data_bytes) # Generate new data using the trained generator noise = torch.randn(batch_size, input_size) generated_data = generator(noise) # Wrap the generated data in a custom data handler password = b"password" data_handler = CustomDataHandler(generated_data, password) #         

PyTorch using the torch.save function and a custom data handler that encrypts the data using the cryptography module:

# Save the custom data handler to a file torch.save(data_handler, "generated.pt")         

This code generates new data using the trained generator, wraps it in a CustomDataHandler object, and saves the object to a file using the torch.save function. The CustomDataHandler class encrypts the data using the cryptography module and base64 encoding, and it decrypts the data when it is loaded from the file.

PyTorch using the torch.save function and a custom data handler that compresses and encrypts the data:

import base64 import gzip from cryptography.fernet import Fernet import pickle class CustomDataHandler: def __init__(self, data, password): self.data = data self.password = password def __getstate__(self): # Compress the data using gzip data_bytes = pickle.dumps(self.data) compressed_data = gzip.compress(data_bytes) # Encrypt the compressed data using the password f = Fernet(self.password) encrypted_data = f.encrypt(compressed_data) # Encode the encrypted data using base64 encoded_data = base64.b64encode(encrypted_data) # Return a dictionary containing the encoded data return {"encoded_data": encoded_data} def __setstate__(self, state): # Load the encoded data from the dictionary encoded_data = state["encoded_data"] # Decode the encoded data using base64 encrypted_data = base64.b64decode(encoded_data) # Decrypt the data using the password f = Fernet(self.password) compressed_data = f.decrypt(encrypted_data) # Decompress the data using gzip data_bytes = gzip.decompress(compressed_data) self.         

PyTorch using the torch.save function and a custom data handler that compresses and encrypts the data:

# Generate new data using the trained generator noise = torch.randn(batch_size, input_size) generated_data = generator(noise) # Wrap the generated data in a custom data handler password = b"password" data_handler = CustomDataHandler(generated_data, password) # Save the custom data handler to a file torch.save(data_handler, "generated.pt")         

This code generates new data using the trained generator, wraps it in a CustomDataHandler object, and saves the object to a file using the torch.save function. The CustomDataHandler class compresses the data using the gzip module, encrypts the data using the cryptography module and base64 encoding, and decompresses and decrypts the data when it is loaded from the file.

PyTorch using the torch.save function and a custom data handler that compresses and encrypts the data using a combination of the gzip module and the cryptography module:

import base64 import gzip from cryptography.fernet import Fernet import pickle class CustomDataHandler: def __init__(self, data, password): self.data = data self.password = password def __getstate__(self): # Compress the data using gzip data_bytes = pickle.dumps(self.data) compressed_data = gzip.compress(data_bytes) # Encrypt the compressed data using the password f = Fernet(self.password) encrypted_data = f.encrypt(compressed_data) # Encode the encrypted data using base64 encoded_data = base64.b64encode(encrypted_data) # Return a dictionary containing the encoded data return {"encoded_data": encoded_data} def __setstate__(self, state): # Load the encoded data from the dictionary encoded_data = state["encoded_data"] # Decode the encoded data using base64 encrypted_data = base64.b64decode(encoded_data) # Decrypt the data using the password f = Fernet(self.password) compressed_data = f.decrypt(encrypted_data) # Decompress the data using gzip data_bytes = gzip.decompress(compressed_data) self.data = pickle.loads(data_bytes) # Generate new data using the trained generator noise = torch.randn(batch_size, input_size) generated_data = generator(noise) # Wrap the generated data in a custom data handler password = b"password" data_handler = CustomDataHandler(generated_data, password) # Save         

PyTorch using the torch.save function and a custom data handler that compresses and encrypts the data using a combination of the gzip module and the cryptography module:

# Save the custom data handler to a file torch.save(data_handler, "generated.pt")         

This code generates new data using the trained generator, wraps it in a CustomDataHandler object, and saves the object to a file using the torch.save function. The CustomDataHandler class compresses the data using the gzip module, encrypts the data using the cryptography module and base64 encoding, and decompresses and decrypts the data when it is loaded from the file.

PyTorch using the torch.save function and a custom data handler that compresses and encrypts the data using a combination of the gzip module and the cryptography module, and also uses the pickle module to serialize and deserialize the data:

import base64 import gzip from cryptography.fernet import Fernet import pickle class CustomDataHandler: def __init__(self, data, password): self.data = data self.password = password def __getstate__(self): # Compress the data using gzip data_bytes = pickle.dumps(self.data) compressed_data = gzip.compress(data_bytes) # Encrypt the compressed data using the password f = Fernet(self.password) encrypted_data = f.encrypt(compressed_data) # Encode the encrypted data using base64 encoded_data = base64.b64encode(encrypted_data) # Return a dictionary containing the encoded data return {"encoded_data": encoded_data} def __setstate__(self, state): # Load the encoded data from the dictionary encoded_data = state["encoded_data"] # Decode the encoded data using base64 encrypted_data = base64.b64decode(encoded_data) # Decrypt the data using the password f = Fernet(self.password) compressed_data         

PyTorch using the torch.save function and a custom data handler that compresses and encrypts the data using a combination of the gzip module and the cryptography module, and also uses the pickle module to serialize and deserialize the data:

# Decompress the data using gzip data_bytes = gzip.decompress(compressed_data) self.data = pickle.loads(data_bytes) # Generate new data using the trained generator noise = torch.randn(batch_size, input_size) generated_data = generator(noise) # Wrap the generated data in a custom data handler password = b"password" data_handler = CustomDataHandler(generated_data, password) # Save the custom data handler to a file torch.save(data_handler, "generated.pt")         

This code generates new data using the trained generator, wraps it in a CustomDataHandler object, and saves the object to a file using the torch.save function. The CustomDataHandler class compresses the data using the gzip module, encrypts the data using the cryptography module and base64 encoding, and decompresses and decrypts the data when it is loaded from the file. It also uses the pickle module to serialize and deserialize the data.

PyTorch using the torch.save function and a custom data handler that compresses and encrypts the data using a combination of the gzip module and the cryptography module, and also uses the pickle module to serialize and deserialize the data:

import base64 import gzip from cryptography.fernet import Fernet import pickle class CustomDataHandler: def __init__(self, data, password): self.data = data self.password = password def __getstate__(self): # Compress the data using gzip data_bytes = pickle.dumps(self.data) compressed_data = gzip.compress(data_bytes) # Encrypt the compressed data using the password f = Fernet(self.password) encrypted_data = f.encrypt(compressed_data) # Encode the encrypted data using base64 encoded_data = base64.b64encode(encrypted_data) # Return a dictionary containing the encoded data return {"encoded_data": encoded_data} def __setstate__(self, state): # Load the encoded data from the dictionary encoded_data = state["encoded_data"] # Decode the encoded data using base64 encrypted_data = base64.b64decode(encoded_data) # Decrypt the data using the password f = Fernet(self.password) compressed_data = f.decrypt(encrypted_data) # Decompress the data using gzip data_bytes = gzip.decompress(compressed_data) self.data = pickle.loads(data_bytes) # Generate new data using the