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,dim):
X = T.matrix()
Y = T.matrix()
eta = T.scalar()
temperature=T.scalar()
num_input = len(format(dnodex.npoi,'b'))
num_hidden = dim
num_output = len(format(dnodex.npoi,'b'))
inputs = InputLayer(X, 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 = SoftmaxLayer(num_hidden, num_output, input_layer=lstm2, name="yhat", temperature=temperature)
Y_hat = softmax.output()
self.layers = inputs, lstm1, lstm2, softmax
params = get_params(self.layers)
caches = make_caches(params)
cost = T.mean(T.nnet.categorical_crossentropy(Y_hat, Y))
updates = momentum(cost, params, caches, eta)
self.train = theano.function([X, Y, eta, temperature], cost, updates=updates, allow_input_downcast=True)
predict_updates = one_step_updates(self.layers)
self.predict_char = theano.function([X, temperature], Y_hat, updates=predict_updates, allow_input_downcast=True)
def __init__(self):
X = T.matrix()
Y = T.matrix()
eta = T.scalar()
temperature = T.scalar()
num_input = 256
num_hidden = 500
num_output = 256
inputs = InputLayer(X, name="inputs")
lstm1 = LSTMLayer(num_input,
num_hidden,
input_layer=inputs,
name="lstm1")
lstm2 = LSTMLayer(num_hidden,
num_hidden,
input_layer=lstm1,
name="lstm2")
softmax = SoftmaxLayer(num_hidden,
num_output,
input_layer=lstm2,
name="yhat",
temperature=temperature)
Y_hat = softmax.output()
self.layers = inputs, lstm1, lstm2, softmax
params = get_params(self.layers)
caches = make_caches(params)
cost = T.mean(T.nnet.categorical_crossentropy(Y_hat, Y))
updates = momentum(cost, params, caches, eta)
self.train = theano.function([X, Y, eta, temperature],
cost,
updates=updates,
allow_input_downcast=True)
predict_updates = one_step_updates(self.layers)
self.predict_char = theano.function([X, temperature],
Y_hat,
updates=predict_updates,
allow_input_downcast=True)
