def rhinge_loss(self, X, targets):
"""
returns reverse hinge loss of points in X and their targets
"""
preds = np.matmul(X, self.weights.T) + self.bias
res = []
for i in xrange(len(X)):
target = targets[i]
if np.argmax(preds) != target:
max_ix, max_val = getMax(preds[i], target)
loss = max_val - preds[i][target]
else:
loss = 0
res.append(loss)
return res
def untargeted_loss(self, X, Y):
"""
computes the untargeted hinge loss of (X, Y)
"""
preds = np.matmul(X, self.weights.T) + self.bias
n = len(X)
loss = []
for i in xrange(n):
y = Y[i]
others = range(10)
del others[y]
if np.argmax(preds[i]) != y:
res = 0
else:
max_val = getMax(preds[i], y)[1]
y_val = preds[i][y]
res = y_val - max_val
loss.append(res)
return np.array(loss)
def gradient_untargeted(self, X, Y):
"""
computes gradients the untargeted hinge loss of (X, Y)
"""
preds = np.matmul(X, self.weights.T) + self.bias
n = len(X)
gradient = []
for i in xrange(n):
y = Y[i]
others = range(10)
del others[y]
if np.argmax(preds[i]) != y:
res = np.zeros(self.dim)
else:
max_ix = getMax(preds[i], y)[0]
w_max = self.weights[max_ix]
w_y = self.weights[y]
res = w_y - w_max
gradient.append(res)
return np.array(gradient)
def gradient(self, X, targets):
"""
returns gradient of the reverse (targeted) hinge loss
"""
preds = np.matmul(X, self.weights.T) + self.bias
n = X.shape[0]
gradient = []
for i in xrange(n):
target = targets[i]
others = range(10)
del others[target]
if np.argmax(preds[i]) == target:
res = np.zeros(self.dim)
else:
max_ix = getMax(preds[i], target)[0]
w_max = self.weights[max_ix]
w_target = self.weights[target]
res = w_max - w_target
gradient.append(res)
return np.array(gradient)
