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Fix discriminator balancing in mnist_gan example (#1332)

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This commit is contained in:
Giles Bathgate
2023-07-23 21:43:05 +02:00
committed by GitHub
parent aa05495620
commit c4238b4ea0
1 changed files with 5 additions and 5 deletions
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10
examples/mnist_gan.py
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@@ -31,7 +31,8 @@ class LinearDisc:
self.l4 = Tensor.scaled_uniform(256, 2)
def forward(self, x):
x = x.dot(self.l1).leakyrelu(0.2).dropout(0.3)
# balance the discriminator inputs with const bias (.add(1))
x = x.dot(self.l1).add(1).leakyrelu(0.2).dropout(0.3)
x = x.dot(self.l2).leakyrelu(0.2).dropout(0.3)
x = x.dot(self.l3).leakyrelu(0.2).dropout(0.3)
x = x.dot(self.l4).log_softmax()
@@ -39,13 +40,12 @@ class LinearDisc:
def make_batch(images):
sample = np.random.randint(0, len(images), size=(batch_size))
image_b = images[sample].reshape(-1, 28*28).astype(np.float32) / 255.0
image_b = (image_b - 0.5) / 0.5
image_b = images[sample].reshape(-1, 28*28).astype(np.float32) / 127.5 - 1.0
return Tensor(image_b)
def make_labels(bs, val):
def make_labels(bs, col, val=-2.0):
y = np.zeros((bs, 2), np.float32)
y[range(bs), [val] * bs] = -2.0 # Can we do label smoothin? i.e -2.0 changed to -1.98789.
y[range(bs), [col] * bs] = val # Can we do label smoothing? i.e -2.0 changed to -1.98789.
return Tensor(y)
def train_discriminator(optimizer, data_real, data_fake):