def __init__(self,
hs,
x,
mask,
seqmask,
x_dim,
outputs_info,
args,
suffix=''):
self.recdrop = args.recdrop
self.W_concat, self.b_concat = _linear_params(args.rnn_dim * 2,
args.rnn_dim,
'concat%s' % suffix)
self.W_att1, self.b_att1 = _linear_params(args.rnn_dim, args.rnn_dim,
'att1%s' % suffix)
self.W_att2, self.b_att2 = _linear_params(args.rnn_dim, args.rnn_dim,
'att2%s' % suffix)
self.hs = hs # e.g. from encoder
self.phi_hs = T.tanh(T.dot(self.hs, self.W_att1) + self.b_att1)
super(GRULayerAttention, self).__init__(x,
mask,
seqmask,
x_dim,
outputs_info,
args,
suffix=suffix)
def __init__(self, hs, x, mask, seqmask, x_dim, outputs_info, args, suffix=""):
self.recdrop = args.recdrop
self.W_concat, self.b_concat = _linear_params(args.rnn_dim * 2, args.rnn_dim, "concat%s" % suffix)
self.W_att1, self.b_att1 = _linear_params(args.rnn_dim, args.rnn_dim, "att1%s" % suffix)
self.W_att2, self.b_att2 = _linear_params(args.rnn_dim, args.rnn_dim, "att2%s" % suffix)
self.hs = hs # e.g. from encoder
self.phi_hs = T.tanh(T.dot(self.hs, self.W_att1) + self.b_att1)
super(GRULayerAttention, self).__init__(x, mask, seqmask, x_dim, outputs_info, args, suffix=suffix)
def __init__(self, x, mask, seqmask, x_dim, outputs_info, args, suffix=''):
# NOTE if want to stack should equal hdim
self.xdim = x_dim
self.hdim = args.rnn_dim
self.recdrop = args.recdrop
self.stocdrop = args.stocdrop
self.batch_norm = args.batch_norm
if args.ortho:
W = np.concatenate([norm_init(self.xdim, self.hdim, scale=0.01)] *
4,
axis=1)
U = np.concatenate([ortho_init(self.hdim, self.hdim, scale=0.05)] *
4,
axis=1)
b = np.zeros((4 * self.hdim, )).astype(floatX)
self.W = theano.shared(W, name='W%s' % suffix)
self.b = theano.shared(b, name='b%s' % suffix)
self.U = theano.shared(U, name='U%s' % suffix)
else:
self.W, self.b = _linear_params(self.xdim, 4 * self.hdim,
'W%s' % suffix)
self.U = _linear_params(self.hdim,
4 * self.hdim,
'U%s' % suffix,
bias=False)
self.params = [self.W, self.b, self.U]
initial_gamma = 0.1
if self.batch_norm:
self.gamma_inputs = theano.shared(
initial_gamma * np.ones(4 * self.hdim, ).astype('float32'))
self.gamma_hiddens = theano.shared(
initial_gamma * np.ones(4 * self.hdim, ).astype('float32'))
self.gamma_outputs = theano.shared(
initial_gamma * np.ones(self.hdim, ).astype('float32'))
self.beta_outputs = theano.shared(
np.zeros(self.hdim, ).astype('float32'))
self.params += [
self.gamma_inputs, self.gamma_hiddens, self.gamma_outputs,
self.beta_outputs
]
rval, updates = theano.scan(self._step,
sequences=[x, seqmask],
outputs_info=outputs_info)
# out should be of dim (sequence length, batch size, hidden size)
self.out = rval[0] * mask[:, :, None]
self.cell = rval[1] * mask[:, :, None]
def __init__(self, inp, n_in, n_out):
# initialize w/ zeros
self.W, self.b = _linear_params(n_in, n_out, 'sm')
E = T.dot(inp, self.W) + self.b
# time, batch, cat (None just keeps dimension)
E = T.exp(E - T.max(E, axis=2, keepdims=True))
pmf = E / T.sum(E, axis=2, keepdims=True)
self.p_y_given_x = pmf
self.y_pred = T.argmax(self.p_y_given_x, axis=1)
self.out = self.p_y_given_x
# parameters of the model
self.params = [self.W, self.b]
def __init__(self, inp, n_in, n_out):
# initialize w/ zeros
self.W, self.b = _linear_params(n_in, n_out, "sm")
E = T.dot(inp, self.W) + self.b
# time, batch, cat (None just keeps dimension)
E = T.exp(E - T.max(E, axis=2, keepdims=True))
pmf = E / T.sum(E, axis=2, keepdims=True)
self.p_y_given_x = pmf
self.y_pred = T.argmax(self.p_y_given_x, axis=1)
self.out = self.p_y_given_x
# parameters of the model
self.params = [self.W, self.b]
def __init__(self, x, mask, seqmask, x_dim, outputs_info, args, suffix=""):
# NOTE if want to stack should equal hdim
self.xdim = x_dim
self.hdim = args.rnn_dim
self.recdrop = args.recdrop
self.stocdrop = args.stocdrop
self.batch_norm = args.batch_norm
if args.ortho:
W = np.concatenate([norm_init(self.xdim, self.hdim, scale=0.01)] * 4, axis=1)
U = np.concatenate([ortho_init(self.hdim, self.hdim, scale=0.05)] * 4, axis=1)
b = np.zeros((4 * self.hdim,)).astype(floatX)
self.W = theano.shared(W, name="W%s" % suffix)
self.b = theano.shared(b, name="b%s" % suffix)
self.U = theano.shared(U, name="U%s" % suffix)
else:
self.W, self.b = _linear_params(self.xdim, 4 * self.hdim, "W%s" % suffix)
self.U = _linear_params(self.hdim, 4 * self.hdim, "U%s" % suffix, bias=False)
self.params = [self.W, self.b, self.U]
initial_gamma = 0.1
if self.batch_norm:
self.gamma_inputs = theano.shared(initial_gamma * np.ones(4 * self.hdim).astype("float32"))
self.gamma_hiddens = theano.shared(initial_gamma * np.ones(4 * self.hdim).astype("float32"))
self.gamma_outputs = theano.shared(initial_gamma * np.ones(self.hdim).astype("float32"))
self.beta_outputs = theano.shared(np.zeros(self.hdim).astype("float32"))
self.params += [self.gamma_inputs, self.gamma_hiddens, self.gamma_outputs, self.beta_outputs]
rval, updates = theano.scan(self._step, sequences=[x, seqmask], outputs_info=outputs_info)
# out should be of dim (sequence length, batch size, hidden size)
self.out = rval[0] * mask[:, :, None]
self.cell = rval[1] * mask[:, :, None]
def __init__(self, x, dim, suffix=""):
# NOTE if want to stack should equal hdim
self.W, self.b = _linear_params(dim * 2, dim, "ds%s" % suffix)
# x.shape = [seq_len, batch_size, hdim]
# x1.shape = [batch_size, seq_len / 2, hdim * 2]
x1 = x.dimshuffle([1, 0, 2]).reshape([x.shape[1], x.shape[0] / 2, x.shape[2] * 2])
# x2.shape = [batch_size, seq_len / 2, hdim]
x2 = x1.dot(self.W) + self.b
# x3.shape = [seq_len / 2, batch_size, hdim]
x3 = x2.dimshuffle([1, 0, 2])
self.out = T.tanh(x3)
self.params = [self.W, self.b]
def __init__(self, x, dim, suffix=''):
# NOTE if want to stack should equal hdim
self.W, self.b = _linear_params(dim * 2, dim, 'ds%s' % suffix)
# x.shape = [seq_len, batch_size, hdim]
# x1.shape = [batch_size, seq_len / 2, hdim * 2]
x1 = x.dimshuffle([1, 0, 2]).reshape(
[x.shape[1], x.shape[0] / 2, x.shape[2] * 2])
# x2.shape = [batch_size, seq_len / 2, hdim]
x2 = x1.dot(self.W) + self.b
# x3.shape = [seq_len / 2, batch_size, hdim]
x3 = x2.dimshuffle([1, 0, 2])
self.out = T.tanh(x3)
self.params = [self.W, self.b]
def __init__(self, x, mask, seqmask, x_dim, outputs_info, args, suffix="", backwards=False):
# NOTE if want to stack should equal hdim
self.xdim = x_dim
self.hdim = args.rnn_dim
self.backwards = backwards
self.recdrop = args.recdrop
self.stocdrop = args.stocdrop
# initialize parameters
# TODO maybe try initialization here: https://github.com/kyunghyuncho/dl4mt-material/blob/master/session1/nmt.py, helps for memorizing long sequences
self.W_z, self.b_wz = _linear_params(self.xdim, self.hdim, "wz%s" % suffix, act=T.nnet.sigmoid)
self.U_z, self.b_uz = _linear_params(self.hdim, self.hdim, "uz%s" % suffix, act=T.nnet.sigmoid)
self.W_r, self.b_wr = _linear_params(self.xdim, self.hdim, "wr%s" % suffix, act=T.nnet.sigmoid)
self.U_r, self.b_ur = _linear_params(self.hdim, self.hdim, "ur%s" % suffix, act=T.nnet.sigmoid)
self.W_h, self.b_wh = _linear_params(self.xdim, self.hdim, "wh%s" % suffix)
self.U_h, self.b_uh = _linear_params(self.hdim, self.hdim, "uh%s" % suffix)
self.setup(x, mask, seqmask, outputs_info)
def __init__(self,
x,
mask,
seqmask,
x_dim,
outputs_info,
args,
suffix='',
backwards=False):
# NOTE if want to stack should equal hdim
self.xdim = x_dim
self.hdim = args.rnn_dim
self.backwards = backwards
self.recdrop = args.recdrop
self.stocdrop = args.stocdrop
# initialize parameters
# TODO maybe try initialization here: https://github.com/kyunghyuncho/dl4mt-material/blob/master/session1/nmt.py, helps for memorizing long sequences
self.W_z, self.b_wz = _linear_params(self.xdim,
self.hdim,
'wz%s' % suffix,
act=T.nnet.sigmoid)
self.U_z, self.b_uz = _linear_params(self.hdim,
self.hdim,
'uz%s' % suffix,
act=T.nnet.sigmoid)
self.W_r, self.b_wr = _linear_params(self.xdim,
self.hdim,
'wr%s' % suffix,
act=T.nnet.sigmoid)
self.U_r, self.b_ur = _linear_params(self.hdim,
self.hdim,
'ur%s' % suffix,
act=T.nnet.sigmoid)
self.W_h, self.b_wh = _linear_params(self.xdim, self.hdim,
'wh%s' % suffix)
self.U_h, self.b_uh = _linear_params(self.hdim, self.hdim,
'uh%s' % suffix)
self.setup(x, mask, seqmask, outputs_info)
