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changed discrim

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k 2025-09-11 17:07:15 -04:00
parent c74924ccea
commit 1a66f31048
2 changed files with 12 additions and 11 deletions
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4
model.py
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@ -25,10 +25,10 @@ class Check:
self.flat = 128 * self.h * self.w self.flat = 128 * self.h * self.w
self.e1 = nn.Conv2d(1, 64, kernel_size=3, stride=2, padding=1) self.e1 = nn.Conv2d(1, 64, kernel_size=3, stride=2, padding=1)
self.e2 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1) self.e2 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)
self.out = nn.Linear(self.flat, 2) self.out = nn.Linear(self.flat, 1)
def __call__(self, x: Tensor) -> Tensor: def __call__(self, x: Tensor) -> Tensor:
x = self.e1(x).relu() x = self.e1(x).relu()
x = self.e2(x).relu() x = self.e2(x).relu()
x = x.reshape(x.shape[0], -1) x = x.reshape(x.shape[0], -1)
return self.out(x).sigmoid() return self.out(x)#.sigmoid()

19
train.py
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@ -11,8 +11,9 @@ mlflow.set_tracking_uri("http://127.0.0.1:5000")
mlflow.start_run(experiment_id=804883409598823668) mlflow.start_run(experiment_id=804883409598823668)
#hyper #hyper
BACH_SIZE=32 BACH_SIZE=32
glr=1e-3 BATCH_SIZE=BACH_SIZE
dlr=1e-3 glr=2e-4
dlr=1e-5
epochs=100 epochs=100
@ -43,12 +44,12 @@ difOpt = nn.optim.AdamW(nn.state.get_parameters(dif), lr=dlr)
@TinyJit @TinyJit
def step_dis(x:Tensor): def step_dis(x:Tensor):
Tensor.training = True Tensor.training = True
real = Tensor([1,0]) real = Tensor.ones((BATCH_SIZE,1))
fake = Tensor([0,1]) fake = Tensor.zeros((BACH_SIZE,1))
noise = Tensor.randn(BACH_SIZE, gen.ld) noise = Tensor.randn(BACH_SIZE, gen.ld)
fake_data = gen(noise).detach() fake_data = gen(noise).detach()
fake_loss = dif(fake_data).log_softmax().nll_loss(fake) fake_loss = dif(fake_data).binary_crossentropy_logits(fake)
real_loss = dif(x).log_softmax().nll_loss(real) real_loss = dif(x).binary_crossentropy_logits(real)
loss = (fake_loss + real_loss)/2 loss = (fake_loss + real_loss)/2
loss.backward() loss.backward()
difOpt.step() difOpt.step()
@ -57,10 +58,10 @@ def step_dis(x:Tensor):
@TinyJit @TinyJit
def step_gen(): def step_gen():
Tensor.training = True Tensor.training = True
real = Tensor([1,0]) real = Tensor.ones((BATCH_SIZE,1))
noise = Tensor.randn(BACH_SIZE, gen.ld) noise = Tensor.randn(BACH_SIZE, gen.ld)
fake_data = gen(noise).detach() fake_data = gen(noise).detach()
loss = dif(fake_data).log_softmax().nll_loss(real) loss = dif(fake_data).binary_crossentropy_logits(real)
loss.backward() loss.backward()
genOpt.step() genOpt.step()
return loss.numpy() return loss.numpy()
@ -88,7 +89,7 @@ for e in range(0,epochs):
dl /= (size/BACH_SIZE) dl /= (size/BACH_SIZE)
gl /= (size/BACH_SIZE) gl /= (size/BACH_SIZE)
if e%4==0: if e%5==0:
noise = Tensor.randn(BACH_SIZE, gen.ld) noise = Tensor.randn(BACH_SIZE, gen.ld)
show.logSpec(gen(noise).numpy()[0][0],e) show.logSpec(gen(noise).numpy()[0][0],e)
#todo test on test data #todo test on test data