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print.py
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print.py
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import argparse
import random
import csv
import sys
import numpy as np
import torch
import torch.optim as optim
from PIL import Image
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
# Function to read losses from a CSV file
def read_losses(csv_filename):
losses = []
with open(csv_filename, mode='r') as file:
lines = file.readlines()
for line in lines:
l = line.split(",")
losses.append((float(l[0]), float(l[1])))
return losses
# Function to plot losses
def plot_losses(losses):
# Separate generator and discriminator losses
loss_generator, loss_discriminator = zip(*losses)
# Plotting
plt.figure(figsize=(10, 5))
plt.subplot(2, 1, 1) # Create subplot for generator loss
plt.plot(loss_generator, label='Generator Loss', color='blue')
plt.title('Generator Loss Over Time')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.subplot(2, 1, 2) # Create subplot for discriminator loss
plt.plot(loss_discriminator, label='Discriminator Loss', color='red')
plt.title('Discriminator Loss Over Time')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.tight_layout() # Adjust spacing between subplots
plt.show()
if __name__ == "__main__":
file = "gan_losses.csv"