# -*- coding: utf-8 -*-
"""
Created on Fri Jan 13 16:57:28 2017 by emin
"""
from lasagne.layers import InputLayer, DenseLayer, ReshapeLayer, GRULayer
from LeInit import LeInit
from CustomRecurrentLayerWithFastWeights import CustomRecurrentLayerWithFastWeights
import lasagne.layers
import lasagne.nonlinearities
import lasagne.updates
import lasagne.objectives
import lasagne.init
def OrthoInitRecurrent(input_var, mask_var=None, batch_size=1, n_in=100, n_out=1, n_hid=200, init_val=0.9, out_nlin=lasagne.nonlinearities.linear):
# Input Layer
l_in = InputLayer((batch_size, None, n_in), input_var=input_var)
if mask_var==None:
l_mask=None
else:
l_mask = InputLayer((batch_size, None), input_var=mask_var)
_, seqlen, _ = l_in.input_var.shape
l_in_hid = DenseLayer(lasagne.layers.InputLayer((None, n_in)), n_hid, W=lasagne.init.GlorotNormal(0.95), nonlinearity=lasagne.nonlinearities.linear)
l_hid_hid = DenseLayer(lasagne.layers.InputLayer((None, n_hid)), n_hid, W=lasagne.init.Orthogonal(gain=init_val), nonlinearity=lasagne.nonlinearities.linear)
l_rec = lasagne.layers.CustomRecurrentLayer(l_in, l_in_hid, l_hid_hid, nonlinearity=lasagne.nonlinearities.rectify, mask_input=l_mask, grad_clipping=100)
# Output Layer
l_shp = ReshapeLayer(l_rec, (-1, n_hid))
l_dense = DenseLayer(l_shp, num_units=n_out, W=lasagne.init.GlorotNormal(0.95), nonlinearity=out_nlin)
# To reshape back to our original shape, we can use the symbolic shape variables we retrieved above.
l_out = ReshapeLayer(l_dense, (batch_size, seqlen, n_out))
return l_out, l_rec
def LeInitRecurrent(input_var, mask_var=None, batch_size=1, n_in=100, n_out=1,
n_hid=200, diag_val=0.9, offdiag_val=0.01,
out_nlin=lasagne.nonlinearities.linear):
# Input Layer
l_in = InputLayer((batch_size, None, n_in), input_var=input_var)
if mask_var==None:
l_mask=None
else:
l_mask = InputLayer((batch_size, None), input_var=mask_var)
_, seqlen, _ = l_in.input_var.shape
l_in_hid = DenseLayer(lasagne.layers.InputLayer((None, n_in)), n_hid,
W=lasagne.init.GlorotNormal(0.95),
nonlinearity=lasagne.nonlinearities.linear)
l_hid_hid = DenseLayer(lasagne.layers.InputLayer((None, n_hid)), n_hid,
W=LeInit(diag_val=diag_val, offdiag_val=offdiag_val),
nonlinearity=lasagne.nonlinearities.linear)
l_rec = lasagne.layers.CustomRecurrentLayer(l_in, l_in_hid, l_hid_hid, nonlinearity=lasagne.nonlinearities.rectify, mask_input=l_mask, grad_clipping=100)
# Output Layer
l_shp = ReshapeLayer(l_rec, (-1, n_hid))
l_dense = DenseLayer(l_shp, num_units=n_out, W=lasagne.init.GlorotNormal(0.95), nonlinearity=out_nlin)
# To reshape back to our original shape, we can use the symbolic shape variables we retrieved above.
l_out = ReshapeLayer(l_dense, (batch_size, seqlen, n_out))
return l_out, l_rec
def LeInitRecurrentWithFastWeights(input_var, mask_var=None, batch_size=1, n_in=100, n_out=1,
n_hid=200, diag_val=0.9, offdiag_val=0.01,
out_nlin=lasagne.nonlinearities.linear, gamma=0.9):
# Input Layer
l_in = InputLayer((batch_size, None, n_in), input_var=input_var)
if mask_var==None:
l_mask=None
else:
l_mask = InputLayer((batch_size, None), input_var=mask_var)
_, seqlen, _ = l_in.input_var.shape
l_in_hid = DenseLayer(lasagne.layers.InputLayer((None, n_in)), n_hid,
W=lasagne.init.GlorotNormal(0.95),
nonlinearity=lasagne.nonlinearities.linear)
l_hid_hid = DenseLayer(lasagne.layers.InputLayer((None, n_hid)), n_hid,
W=LeInit(diag_val=diag_val, offdiag_val=offdiag_val),
nonlinearity=lasagne.nonlinearities.linear)
l_rec = CustomRecurrentLayerWithFastWeights(l_in, l_in_hid, l_hid_hid,
nonlinearity=lasagne.nonlinearities.rectify,
mask_input=l_mask, grad_clipping=100, gamma=gamma)
# Output Layer
l_shp = ReshapeLayer(l_rec, (-1, n_hid))
l_dense = DenseLayer(l_shp, num_units=n_out, W=lasagne.init.GlorotNormal(0.95), nonlinearity=out_nlin)
# To reshape back to our original shape, we can use the symbolic shape variables we retrieved above.
l_out = ReshapeLayer(l_dense, (batch_size, seqlen, n_out))
return l_out, l_rec
def GRURecurrent(input_var, mask_var=None, batch_size=1, n_in=100, n_out=1, n_hid=200, diag_val=0.9, offdiag_val=0.01, out_nlin=lasagne.nonlinearities.linear):
# Input Layer
l_in = InputLayer((batch_size, None, n_in), input_var=input_var)
if mask_var==None:
l_mask = None
else:
l_mask = InputLayer((batch_size, None), input_var=mask_var)
_, seqlen, _ = l_in.input_var.shape
l_rec = GRULayer(l_in, n_hid,
resetgate=lasagne.layers.Gate(W_in=lasagne.init.GlorotNormal(0.05),
W_hid=lasagne.init.GlorotNormal(0.05),
W_cell=None, b=lasagne.init.Constant(0.)),
updategate=lasagne.layers.Gate(W_in=lasagne.init.GlorotNormal(0.05),
W_hid=lasagne.init.GlorotNormal(0.05),
W_cell=None),
hidden_update=lasagne.layers.Gate(W_in=lasagne.init.GlorotNormal(0.05),
W_hid=LeInit(diag_val=diag_val, offdiag_val=offdiag_val),
W_cell=None, nonlinearity=lasagne.nonlinearities.rectify),
hid_init = lasagne.init.Constant(0.), backwards=False, learn_init=False,
gradient_steps=-1, grad_clipping=10., unroll_scan=False, precompute_input=True, mask_input=l_mask, only_return_final=False)
# Output Layer
l_shp = ReshapeLayer(l_rec, (-1, n_hid))
l_dense = DenseLayer(l_shp, num_units=n_out, W=lasagne.init.GlorotNormal(0.05), nonlinearity=out_nlin)
# To reshape back to our original shape, we can use the symbolic shape variables we retrieved above.
l_out = ReshapeLayer(l_dense, (batch_size, seqlen, n_out))
return l_out, l_rec
