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GAN_sample.py
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GAN_sample.py
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import torch
from GAN import Generator
# Assume the trained generator model is loaded or available in memory
# generator = ... (your trained generator)
def generate_samples(generator, num_samples, label, latent_dim=100):
"""
Generate samples using the trained generator conditioned on a specific label.
Args:
generator (nn.Module): Trained generator model.
num_samples (int): Number of samples to generate.
label (int): The label to condition the generation on (-1 or 1).
latent_dim (int): Dimensionality of the latent space.
Returns:
torch.Tensor: Generated samples.
"""
# Convert label to tensor
label_tensor = torch.full((num_samples,), label, dtype=torch.long)
# Sample random noise
noise = torch.randn(num_samples, latent_dim)
# Generate samples
with torch.no_grad():
generated_samples = generator(noise, label_tensor)
return generated_samples
# Example usage
num_samples = 10 # Number of samples to generate
label = 1 # Label to condition the generation on (can be -1 or 1)
batch_size = 64
learning_rate = 0.0002
num_epochs = 100
latent_dim = 100
sample_length = 96
num_classes = 2
label_dim = 1
generator = Generator(latent_dim, sample_length, label_dim)
generator.load_state_dict(torch.load('generator_final.pth'))
generator.eval()
# Generate samples conditioned on the specified label
generated_samples = generate_samples(generator, num_samples, label)