Subhaditya Mukherjee SubhadityaMukherjee

netD = Discriminator(ngpu).to(device)
if (device.type == 'cuda') and (ngpu > 1):
 netD = nn.DataParallel(netD, list(range(ngpu)))
netD.apply(weights_init)
print(netD)

criterion = nn.BCELoss()
fixed_noise = torch.randn(64, nz, 1, 1, device=device)
real_label = 1
fake_label = 0
optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))
optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))

img_list = []
G_losses = []
D_losses = []
iters = 0
for epoch in range(num_epochs):

    for i, data in enumerate(dataloader, 0):
    	# Part 1
        netD.zero_grad()

plt.figure(figsize=(10,5))
plt.title("Generator and Discriminator Loss During Training")
plt.plot(G_losses,label="G")
plt.plot(D_losses,label="D")
plt.xlabel("iterations")
plt.ylabel("Loss")
plt.legend()
plt.show()

#%%capture
fig = plt.figure(figsize=(8,8))
plt.axis("off")
ims = [[plt.imshow(np.transpose(i,(1,2,0)), animated=True)] for i in img_list]
ani = animation.ArtistAnimation(fig, ims, interval=1000, repeat_delay=1000, blit=True)
ani.save("anim.mp4")
HTML(ani.to_jshtml())

A bit about me

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Implementations Link
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import pandas as pd
import numpy as np
import seaborn as sns

df = pd.read_csv("speed-dating-experiment/Speed Dating Data.csv", encoding="ISO-8859-1")
df.head(10) #shows the first 10 rows
df.shape # gives rows and columns

col_0	count
match

	to_remove = []

	for a in df.columns:
	if df[a].isna().sum() > 4000:
	to_remove.append(a)

	df = df.drop(to_remove, axis=1)

	df.shape

	df2['match'].shape
	pd.crosstab(index=df2["match"], columns="count")