def __init__(self, num_input, num_cells=50, num_output=1, lr=0.01, rho=0.95):
X = T.matrix('x')
Y = T.matrix('y')
eta = T.scalar('eta')
alpha = T.scalar('alpha')
self.num_input = num_input
self.num_output = num_output
self.num_cells = num_cells
self.eta = eta
inputs = InputLayer(X, name="inputs")
lstm = LSTMLayer(num_input, num_cells, input_layer=inputs, name="lstm")
fc = FullyConnectedLayer(num_cells, num_output, input_layer=lstm)
Y_hat = T.mean(fc.output(), axis=2)
layer = inputs, lstm, fc
self.params = get_params(layer)
self.caches = make_caches(self.params)
self.layers = layer
mean_cost = T.mean((Y - Y_hat)**2)
last_cost = T.mean((Y[-1] - Y_hat[-1])**2)
self.cost = alpha*mean_cost + (1-alpha)*last_cost
""""
self.updates = momentum(self.cost, self.params, self.caches, self.eta, clip_at=3.0)
"""
self.updates,_,_,_,_ = create_optimization_updates(self.cost, self.params, method="adadelta", lr= lr, rho=rho)
self.train = theano.function([X, Y, alpha], [self.cost, last_cost] ,\
updates=self.updates, allow_input_downcast=True)
self.costfn = theano.function([X, Y, alpha], [self.cost, last_cost],\
allow_input_downcast=True)
self.predict = theano.function([X], [Y_hat], allow_input_downcast=True)
def __init__(self, num_input=256, num_hidden=[512,512], num_output=256, clip_at=0.0, scale_norm=0.0):
X = T.fmatrix()
Y = T.imatrix()
lr = T.fscalar()
alpha = T.fscalar()
reg = T.fscalar()
dropout_prob = T.fscalar()
self.num_input = num_input
self.num_hidden = num_hidden
self.num_output = num_output
self.clip_at = clip_at
self.scale_norm = scale_norm
inputs = InputLayer(X, name='inputs')
num_prev = num_input
prev_layer = inputs
self.layers = [inputs]
if type(num_hidden) is types.IntType:
lstm = LSTMLayer(num_prev, num_hidden, input_layers=[prev_layer], name="lstm", go_backwards=False)
num_prev = num_hidden
prev_layer = lstm
self.layers.append(prev_layer)
prev_layer = DropoutLayer(prev_layer, dropout_prob=dropout_prob)
self.layers.append(prev_layer)
FC = FullyConnectedLayer(num_prev, num_output, input_layers=[prev_layer], name="yhat")
self.layers.append(FC)
Y_hat = FC.output()
# change to probilities
Y_hat = T.nnet.softmax(Y_hat)
params = get_params(self.layers)
caches = make_caches(params)
updates, grads = momentum(loss, params, lr, reg)
self.train_func = theano.function([X, Y, lr, reg, dropout_prob, alpha], loss, updates=updates, allow_input_downcast=True)
self.predict_sequence_func = theano.function([X, dropout_prob], [Y_hat], allow_input_downcast=True)
def __init__(self,
num_input,
num_cells=50,
num_output=1,
lr=0.01,
rho=0.95):
X = T.matrix('x')
Y = T.matrix('y')
eta = T.scalar('eta')
alpha = T.scalar('alpha')
self.num_input = num_input
self.num_output = num_output
self.num_cells = num_cells
self.eta = eta
inputs = InputLayer(X, name="inputs")
lstm = LSTMLayer(num_input, num_cells, input_layer=inputs, name="lstm")
fc = FullyConnectedLayer(num_cells, num_output, input_layer=lstm)
Y_hat = T.mean(fc.output(), axis=2)
layer = inputs, lstm, fc
self.params = get_params(layer)
self.caches = make_caches(self.params)
self.layers = layer
mean_cost = T.mean((Y - Y_hat)**2)
last_cost = T.mean((Y[-1] - Y_hat[-1])**2)
self.cost = alpha * mean_cost + (1 - alpha) * last_cost
""""
self.updates = momentum(self.cost, self.params, self.caches, self.eta, clip_at=3.0)
"""
self.updates, _, _, _, _ = create_optimization_updates(
self.cost, self.params, method="adadelta", lr=lr, rho=rho)
self.train = theano.function([X, Y, alpha], [self.cost, last_cost] ,\
updates=self.updates, allow_input_downcast=True)
self.costfn = theano.function([X, Y, alpha], [self.cost, last_cost],\
allow_input_downcast=True)
self.predict = theano.function([X], [Y_hat], allow_input_downcast=True)
