Training RNN-GAN with Adam Optimizer Using PyTorch

Here is a simple code snippet for training a RNN-GAN with the Adam optimizer using PyTorch:

python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import numpy as np

# Define the RNN model
class RNNModel(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(RNNModel, self).__init__()
        self.hidden_size = hidden_size
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# Define the GAN model
class GANModel(nn.Module):
    def __init__(self):
        super(GANModel, self).__init__()
        self.fc1 = nn.Linear(100, 256)
        self.fc2 = nn.Linear(256, 512)
        self.fc3 = nn.Linear(512, 784)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = torch.tanh(self.fc3(x))
        return x

# Initialize the RNN and GAN models
rnn_model = RNNModel(input_size=100, hidden_size=256, output_size=784)
gan_model = GANModel()

# Set the loss functions and optimizers
criterion = nn.MSELoss()
optimizer_RNN = optim.Adam(rnn_model.parameters(), lr=0.001)
optimizer_GAN = optim.Adam(gan_model.parameters(), lr=0.001)

# Train the RNN-GAN
for epoch in range(100):
    for i in range(100):
        # Sample a batch of noise
        noise = torch.randn(100, 100)

        # Generate synthetic images
        synthetic_images = gan_model(noise)

        # Evaluate the discriminator
        discriminator.zero_grad()
        output = discriminator(synthetic_images)
        loss_D = criterion(output, torch.ones_like(output))
        loss_D.backward()
        optimizer_D.step()

        # Train the generator
        gan_model.zero_grad()
        output = discriminator(synthetic_images)
        loss_G = criterion(output, torch.ones_like(output))
        loss_G.backward()
        optimizer_G.step()

    # Print the loss at each epoch
    print(f'Epoch {epoch+1}, Loss D: {loss_D.item():.4f}, Loss G: {loss_G.item():.4f}')

This code defines a basic RNN-GAN architecture and trains it using adversarial training with the Adam optimizer. The RNN generates sequences of data, and the GAN improves the quality and diversity of the generated sequences by training a discriminator to distinguish between real and generated sequences.

can you provide a simple code snippet for training a RNN-GAN with Adam optimizer