def __init__(self, num_input=256, num_hidden=512, num_output=256):
X = T.matrix()
Y = T.matrix()
eta = T.scalar()
alpha = T.scalar()
self.num_input = num_input
self.num_hidden = num_hidden
self.num_output = num_output
inputs = InputLayer(X, name="inputs")
lstm1f = LSTMLayer(num_input, num_hidden, input_layers=[inputs], name="lstm1f")
lstm1b = LSTMLayer(num_input, num_hidden, input_layers=[inputs], name="lstm1b", go_backwards=True)
fc = FullyConnectedLayer(2*num_hidden, num_output, input_layers=[lstm1f, lstm1b], name="yhat")
Y_hat = sigmoid(T.mean(fc.output(), axis=0))
self.layers = inputs, lstm1f, lstm1b, fc
params = get_params(self.layers)
caches = make_caches(params)
mean_cost = - T.mean( Y * T.log(Y_hat) + (1-Y) * T.log(1-Y_hat) )
last_step_cost = - T.mean( Y[-1] * T.log(Y_hat[-1]) + (1-Y[-1]) * T.log(1-Y_hat[-1]) )
cost = alpha * mean_cost + (1-alpha) * last_step_cost
updates = momentum(cost, params, caches, eta, clip_at=3.0)
self.train = theano.function([X, Y, eta, alpha], [cost, last_step_cost], updates=updates, allow_input_downcast=True)
self.predict=theano.function([X], [Y_hat[-1]], 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)
def output(self):
return sigmoid(T.dot(self.X, self.W_yh) + self.b_y)
def output(self):
return sigmoid(T.dot(self.X, self.W_yh) + self.b_y)