def __init__(self, dnodex,inputdim,dim):
X=T.ivector()
Y=T.ivector()
Z=T.lscalar()
eta = T.scalar()
temperature=T.scalar()
self.dnodex=dnodex
num_input = inputdim
dnodex.umatrix=theano.shared(floatX(np.random.randn(*(self.dnodex.nuser,inputdim, inputdim))))
dnodex.pmatrix=theano.shared(floatX(np.random.randn(*(self.dnodex.npoi,inputdim))))
dnodex.p_l2_norm=(dnodex.pmatrix**2).sum()
dnodex.u_l2_norm=(dnodex.umatrix**2).sum()
num_hidden = dim
num_output = inputdim
inputs = InputPLayer(dnodex.pmatrix[X,:], dnodex.umatrix[Z,:,:], name="inputs")
lstm1 = LSTMLayer(num_input, num_hidden, input_layer=inputs, name="lstm1")
lstm2 = LSTMLayer(num_hidden, num_hidden, input_layer=lstm1, name="lstm2")
lstm3 = LSTMLayer(num_hidden, num_hidden, input_layer=lstm2, name="lstm3")
softmax = SoftmaxPLayer(num_hidden, num_output, dnodex.umatrix[Z,:,:], input_layer=lstm3, name="yhat", temperature=temperature)
Y_hat = softmax.output()
self.layers = inputs, lstm1,lstm2,lstm3,softmax
params = get_params(self.layers)
#caches = make_caches(params)
cost = T.mean(T.nnet.categorical_crossentropy(Y_hat, T.dot(dnodex.pmatrix[Y,:],dnodex.umatrix[Z,:,:])))+eta*dnodex.p_l2_norm+eta*dnodex.u_l2_norm
updates = PerSGD(cost,params,eta,X,Z,dnodex)#momentum(cost, params, caches, eta)
self.train = theano.function([X,Y,Z, eta, temperature], cost, updates=updates, allow_input_downcast=True)
predict_updates = one_step_updates(self.layers)
self.predict_char = theano.function([X, Z, temperature], Y_hat, updates=predict_updates, allow_input_downcast=True)
def __init__(self, dnodex,inputdim,dim):
X=T.ivector()
Y=T.ivector()
Z=T.lscalar()
NP=T.ivector()
lambd = T.scalar()
eta = T.scalar()
temperature=T.scalar()
num_input = inputdim
self.umatrix=theano.shared(floatX(np.random.rand(dnodex.nuser,inputdim, inputdim)))
self.pmatrix=theano.shared(floatX(np.random.rand(dnodex.npoi,inputdim)))
self.p_l2_norm=(self.pmatrix**2).sum()
self.u_l2_norm=(self.umatrix**2).sum()
num_hidden = dim
num_output = inputdim
inputs = InputPLayer(self.pmatrix[X,:], self.umatrix[Z,:,:], name="inputs")
lstm1 = LSTMLayer(num_input, num_hidden, input_layer=inputs, name="lstm1")
#lstm2 = LSTMLayer(num_hidden, num_hidden, input_layer=lstm1, name="lstm2")
#lstm3 = LSTMLayer(num_hidden, num_hidden, input_layer=lstm2, name="lstm3")
softmax = SoftmaxPLayer(num_hidden, num_output, self.umatrix[Z,:,:], input_layer=lstm1, name="yhat", temperature=temperature)
Y_hat = softmax.output()
self.layers = inputs, lstm1,softmax
params = get_params(self.layers)
#caches = make_caches(params)
tmp_u=T.mean(T.dot(self.pmatrix[X,:],self.umatrix[Z,:,:]),axis=0)
tr=T.dot(tmp_u,(self.pmatrix[X,:]-self.pmatrix[NP,:]).transpose())
pfp_loss1=sigmoid(tr)
pfp_loss=pfp_loss1*(T.ones_like(pfp_loss1)-pfp_loss1)
tmp_u1=T.reshape(T.repeat(tmp_u,X.shape[0]),(inputdim,X.shape[0])).T
pfp_lossv=T.reshape(T.repeat(pfp_loss,inputdim),(inputdim,X.shape[0])).T
cost = lambd*10*T.mean(T.nnet.categorical_crossentropy(Y_hat, T.dot(self.pmatrix[Y,:],self.umatrix[Z,:,:])))+lambd*self.p_l2_norm+lambd*self.u_l2_norm
# updates = PerSGD(cost,params,eta,X,Z,dnodex)#momentum(cost, params, caches, eta)
updates = []
grads = T.grad(cost=cost, wrt=params)
updates.append([self.pmatrix,T.set_subtensor(self.pmatrix[X,:],self.pmatrix[X,:]-eta*grads[0])])
updates.append([self.umatrix,T.set_subtensor(self.umatrix[Z,:,:],self.umatrix[Z,:,:]-eta*grads[1])])
for p,g in zip(params[2:], grads[2:]):
updates.append([p, p - eta * g])
rlist=T.argsort(T.dot(tmp_u,self.pmatrix.T))[::-1]
n_updates=[(self.pmatrix, T.set_subtensor(self.pmatrix[NP,:],self.pmatrix[NP,:]-eta*pfp_lossv*tmp_u1-eta*lambd*self.pmatrix[NP,:]))]
p_updates=[(self.pmatrix, T.set_subtensor(self.pmatrix[X,:],self.pmatrix[X,:]+eta*pfp_lossv*tmp_u1-eta*lambd*self.pmatrix[X,:])),(self.umatrix, T.set_subtensor(self.umatrix[Z,:,:],self.umatrix[Z,:,:]+eta*T.mean(pfp_loss)*(T.reshape(tmp_u,(tmp_u.shape[0],1))*T.mean(self.pmatrix[X,:]-self.pmatrix[NP,:],axis=0)))-eta*lambd*self.umatrix[Z,:,:])]
self.train = theano.function([X,Y,Z, eta, lambd, temperature], cost, updates=updates, allow_input_downcast=True)
self.trainpos=theano.function([X,NP,Z,eta, lambd],tmp_u, updates=p_updates,allow_input_downcast=True)
self.trainneg=theano.function([X,NP,Z,eta, lambd],T.mean(pfp_loss), updates=n_updates,allow_input_downcast=True)
self.predict_pfp = theano.function([X,Z], rlist, allow_input_downcast=True)
