def lossAndGrads(self, batchAndTargets, computeGrads=True):
nb = len(batchAndTargets)
ctxtoks = [ctxtok for ctxtok,tgt in batchAndTargets]
tgts = [tgt for ctxtok,tgt in batchAndTargets]
context = self.wvec[ctxtoks,:].reshape((nb,-1))
z = np.dot(context, self.Wz)
a = relu(z)
scores = np.dot(a, self.Wa)
preds = np.argmax(scores, axis=1)
loss, probs, dscores = softmaxLossAndGrads(scores, tgts)
if not computeGrads:
return preds, probs, loss
dWa = np.dot(a.T, dscores)
da = np.dot(dscores, self.Wa.T)
dz = drelu(da, a)
dWz = np.dot(context.T, dz)
dcontext = np.dot(dz, self.Wz.T).reshape((nb,-1,self.wdim))
dwvec = np.zeros_like(self.wvec)
for b,ctxtok in enumerate(ctxtoks):
for w,tok in enumerate(ctxtok):
dwvec[tok] += dcontext[b,w]
grads = {}
grads['Wa'] = dWa + self.reg * self.Wa
grads['Wz'] = dWz + self.reg * self.Wz
grads['wvec'] = dwvec + self.reg * self.wvec * (dwvec != 0)
return preds, probs, loss, grads
def lossAndGrads(self, batchAndTargets, computeGrads=True):
nb = len(batchAndTargets)
ctxtoks = [ctxtok for ctxtok, tgt in batchAndTargets]
tgts = [tgt for ctxtok, tgt in batchAndTargets]
context = self.wvec[ctxtoks, :].reshape((nb, -1))
z = np.dot(context, self.Wz)
a = relu(z)
scores = np.dot(a, self.Wa)
preds = np.argmax(scores, axis=1)
loss, probs, dscores = softmaxLossAndGrads(scores, tgts)
if not computeGrads:
return preds, probs, loss
dWa = np.dot(a.T, dscores)
da = np.dot(dscores, self.Wa.T)
dz = drelu(da, a)
dWz = np.dot(context.T, dz)
dcontext = np.dot(dz, self.Wz.T).reshape((nb, -1, self.wdim))
dwvec = np.zeros_like(self.wvec)
for b, ctxtok in enumerate(ctxtoks):
for w, tok in enumerate(ctxtok):
dwvec[tok] += dcontext[b, w]
grads = {}
grads['Wa'] = dWa + self.reg * self.Wa
grads['Wz'] = dWz + self.reg * self.Wz
grads['wvec'] = dwvec + self.reg * self.wvec * (dwvec != 0)
return preds, probs, loss, grads
def lossAndGrads(self, batchAndTargets, computeGrads=True):
nb = len(batchAndTargets)
ctxtoks = [ctxtok for ctxtok,tgt in batchAndTargets]
tgts = [tgt for ctxtok,tgt in batchAndTargets]
context = np.transpose(self.wvec[ctxtoks,:], (0,2,1))
attnvec = context.dot(self.attn)
scores = np.dot(attnvec, self.W)
preds = np.argmax(scores, axis=1)
loss, probs, dscores = softmaxLossAndGrads(scores, tgts)
if not computeGrads:
return preds, probs, loss
dW = np.dot(attnvec.T, dscores)
dattnvec = np.dot(dscores, self.W.T)
dattn = np.tensordot(context, dattnvec, axes=([0,1],[0,1]))
dcontext = dattnvec[...,None]*self.attn
dwvec = np.zeros_like(self.wvec)
for b,ctxtok in enumerate(ctxtoks):
for w,tok in enumerate(ctxtok):
dwvec[tok] += dcontext[b,:,w]
grads = {}
grads['W'] = dW + self.reg * self.W
grads['attn'] = dattn + self.reg * self.attn
grads['wvec'] = dwvec + self.reg * self.wvec * (dwvec != 0)
return preds, probs, loss, grads
def lossAndGrads(self, batchAndTargets, computeGrads=True):
nb = len(batchAndTargets)
ctxtoks = [ctxtok for ctxtok, tgt in batchAndTargets]
tgts = [tgt for ctxtok, tgt in batchAndTargets]
context = np.transpose(self.wvec[ctxtoks, :], (0, 2, 1))
attnvec = context.dot(self.attn)
scores = np.dot(attnvec, self.W)
preds = np.argmax(scores, axis=1)
loss, probs, dscores = softmaxLossAndGrads(scores, tgts)
if not computeGrads:
return preds, probs, loss
dW = np.dot(attnvec.T, dscores)
dattnvec = np.dot(dscores, self.W.T)
dattn = np.tensordot(context, dattnvec, axes=([0, 1], [0, 1]))
dcontext = dattnvec[..., None] * self.attn
dwvec = np.zeros_like(self.wvec)
for b, ctxtok in enumerate(ctxtoks):
for w, tok in enumerate(ctxtok):
dwvec[tok] += dcontext[b, :, w]
grads = {}
grads['W'] = dW + self.reg * self.W
grads['attn'] = dattn + self.reg * self.attn
grads['wvec'] = dwvec + self.reg * self.wvec * (dwvec != 0)
return preds, probs, loss, grads
