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AlessandroMondin/logistic_regression.py

Last active May 10, 2022 17:04
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logistic_regression.py
def logistic_regression(self, X, W, b):
if self.library == "tf":
# flatten_X has shape (batch_size, W*H*Channels) --> in our case (64, 32*32*3)
flatten_X = tf.reshape(X, (-1, W.shape[0]))
out = self.softmax(tf.matmul(flatten_X, W) + b)
else:
flatten_X = X.reshape((-1, W.shape[0]))
out = self.softmax(torch.matmul(flatten_X,W) + b)
return out
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