def _init_exprs(self):
self.exprs = {
'inpt_mean': T.matrix('inpt_mean'),
'target': T.matrix('target')
}
P = self.parameters
hidden_to_hiddens = [
getattr(P, 'hidden_to_hidden_%i' % i)
for i in range(len(self.n_hiddens) - 1)
]
hidden_biases = [
getattr(P, 'hidden_bias_%i' % i)
for i in range(len(self.n_hiddens))
]
inpt_var = T.zeros_like(self.exprs['inpt_mean']) + self.inpt_var
self.exprs.update(
varprop_mlp.exprs(self.exprs['inpt_mean'], inpt_var,
P.in_to_hidden, hidden_to_hiddens,
P.hidden_to_out, hidden_biases,
[1 for _ in hidden_biases], P.out_bias, 1,
self.hidden_transfers, self.out_transfer,
self.p_dropout_inpt, self.p_dropout_hiddens))
self.exprs['inpt'] = self.exprs['inpt_mean']
self.exprs.update(
varprop_supervised_loss(self.exprs['target'], self.exprs['output'],
self.loss))
def _init_exprs(self):
self.exprs = {
'inpt_mean': T.matrix('inpt_mean'),
'target': T.matrix('target')}
P = self.parameters
hidden_to_hiddens = [getattr(P, 'hidden_to_hidden_%i' % i)
for i in range(len(self.n_hiddens) - 1)]
hidden_biases = [getattr(P, 'hidden_bias_%i' % i)
for i in range(len(self.n_hiddens))]
inpt_var = T.zeros_like(self.exprs['inpt_mean']) + self.inpt_var
self.exprs.update(varprop_mlp.exprs(
self.exprs['inpt_mean'], inpt_var, self.exprs['target'],
P.in_to_hidden,
hidden_to_hiddens,
P.hidden_to_out,
hidden_biases,
[1 for _ in hidden_biases],
P.out_bias,
1,
self.hidden_transfers, self.out_transfer,
self.p_dropout_inpt, self.p_dropout_hiddens))
self.exprs['inpt'] = self.exprs['inpt_mean']
self.exprs.update(varprop_supervised_loss(
self.exprs['target'], self.exprs['output'], self.loss))
def _init_exprs(self):
self.exprs = {'inpt': T.tensor3('inpt'),
'target': T.tensor3('target')}
self.exprs['inpt'].tag.test_value = np.zeros((5, 2, self.n_inpt)
).astype(theano.config.floatX)
self.exprs['target'].tag.test_value = np.zeros((5, 2, self.n_output)
).astype(theano.config.floatX)
if self.imp_weight:
self.exprs['imp_weight'] = T.tensor3('imp_weight')
self.exprs['imp_weight'].tag.test_value = np.zeros(
(5, 2, self.n_output)).astype(theano.config.floatX)
P = self.parameters
n_layers = len(self.n_hiddens)
hidden_to_hiddens = [getattr(P, 'hidden_to_hidden_%i' % i)
for i in range(n_layers - 1)]
recurrents = [getattr(P, 'recurrent_%i' % i)
for i in range(n_layers)]
initial_hidden_means = [getattr(P, 'initial_hidden_means_%i' % i)
for i in range(n_layers)]
initial_hidden_vars = [getattr(P, 'initial_hidden_vars_%i' % i) ** 2 + 1e-4
for i in range(n_layers)]
hidden_biases = [getattr(P, 'hidden_bias_%i' % i)
for i in range(n_layers)]
if self.skip_to_out:
skip_to_outs = [getattr(P, 'hidden_%i_to_out' % i)
for i in range(n_layers)]
in_to_out = P.in_to_out
else:
in_to_out = skip_to_outs = None
inpt_var = T.zeros_like(self.exprs['inpt'])
p_dropouts = ([self.p_dropout_inpt]
+ self.p_dropout_hiddens
+ [self.p_dropout_hidden_to_out])
hidden_var_scales_sqrt = [int(i) for i in self.use_varprop_at[:-1]]
out_var_scale_sqrt = int(self.use_varprop_at[-1])
self.exprs.update(varprop_rnn.exprs(
self.exprs['inpt'], inpt_var, P.in_to_hidden, hidden_to_hiddens,
P.hidden_to_out, hidden_biases,
hidden_var_scales_sqrt, initial_hidden_means, initial_hidden_vars,
recurrents, P.out_bias, out_var_scale_sqrt,
self.hidden_transfers, self.out_transfer,
in_to_out=in_to_out, skip_to_outs=skip_to_outs,
p_dropouts=p_dropouts, hotk_inpt=False))
imp_weight = False if not self.imp_weight else self.exprs['imp_weight']
self.exprs.update(varprop_supervised_loss(
self.exprs['target'], self.exprs['output'],
self.loss, 2, imp_weight=imp_weight))
def _init_exprs(self):
self.exprs = {'inpt': T.tensor3('inpt'), 'target': T.tensor3('target')}
P = self.parameters
n_layers = len(self.n_hiddens)
hidden_to_hiddens = [
getattr(P, 'hidden_to_hidden_%i' % i) for i in range(n_layers - 1)
]
recurrents = [getattr(P, 'recurrent_%i' % i) for i in range(n_layers)]
initial_hiddens = [
getattr(P, 'initial_hiddens_%i' % i) for i in range(n_layers)
]
hidden_biases = [
getattr(P, 'hidden_bias_%i' % i) for i in range(n_layers)
]
if self.skip_to_out:
skip_to_outs = [
getattr(P, 'hidden_%i_to_out' % i) for i in range(n_layers)
]
in_to_out = P.in_to_out
else:
in_to_out = skip_to_outs = None
inpt_var = T.zeros_like(self.exprs['inpt'])
p_dropouts = ([self.p_dropout_inpt] + self.p_dropout_hiddens +
[self.p_dropout_hidden_to_out])
hidden_var_scales_sqrt = [int(i) for i in self.use_varprop_at[:-1]]
out_var_scale_sqrt = int(self.use_varprop_at[-1])
self.exprs.update(
varprop_rnn.exprs(self.exprs['inpt'],
inpt_var,
P.in_to_hidden,
hidden_to_hiddens,
P.hidden_to_out,
hidden_biases,
hidden_var_scales_sqrt,
initial_hiddens,
recurrents,
P.out_bias,
out_var_scale_sqrt,
self.hidden_transfers,
self.out_transfer,
in_to_out=in_to_out,
skip_to_outs=skip_to_outs,
p_dropouts=p_dropouts,
hotk_inpt=False))
self.exprs.update(
varprop_supervised_loss(self.exprs['target'], self.exprs['output'],
self.loss, 2))
def _init_exprs(self):
self.exprs = {'inpt': T.tensor3('inpt'),
'target': T.tensor3('target')}
P = self.parameters
n_layers = len(self.n_hiddens)
hidden_to_hiddens = [getattr(P, 'hidden_to_hidden_%i' % i)
for i in range(n_layers - 1)]
recurrents = [getattr(P, 'recurrent_%i' % i)
for i in range(n_layers)]
initial_hiddens = [getattr(P, 'initial_hiddens_%i' % i)
for i in range(n_layers)]
hidden_biases = [getattr(P, 'hidden_bias_%i' % i)
for i in range(n_layers)]
if self.skip_to_out:
skip_to_outs = [getattr(P, 'hidden_%i_to_out' % i)
for i in range(n_layers)]
in_to_out = P.in_to_out
else:
in_to_out = skip_to_outs = None
inpt_var = T.zeros_like(self.exprs['inpt'])
p_dropouts = ([self.p_dropout_inpt]
+ self.p_dropout_hiddens
+ [self.p_dropout_hidden_to_out])
hidden_var_scales_sqrt = [int(i) for i in self.use_varprop_at[:-1]]
out_var_scale_sqrt = int(self.use_varprop_at[-1])
self.exprs.update(varprop_rnn.exprs(
self.exprs['inpt'], inpt_var, P.in_to_hidden, hidden_to_hiddens,
P.hidden_to_out, hidden_biases,
hidden_var_scales_sqrt, initial_hiddens,
recurrents, P.out_bias, out_var_scale_sqrt,
self.hidden_transfers, self.out_transfer,
in_to_out=in_to_out, skip_to_outs=skip_to_outs,
p_dropouts=p_dropouts, hotk_inpt=False))
self.exprs.update(varprop_supervised_loss(
self.exprs['target'], self.exprs['output'], self.loss, 2))
def _init_exprs(self):
self.exprs = {'inpt': T.tensor3('inpt'), 'target': T.tensor3('target')}
self.exprs['inpt'].tag.test_value = np.zeros(
(5, 2, self.n_inpt)).astype(theano.config.floatX)
self.exprs['target'].tag.test_value = np.zeros(
(5, 2, self.n_output)).astype(theano.config.floatX)
if self.imp_weight:
self.exprs['imp_weight'] = T.tensor3('imp_weight')
self.exprs['imp_weight'].tag.test_value = np.zeros(
(5, 2, self.n_output)).astype(theano.config.floatX)
P = self.parameters
n_layers = len(self.n_hiddens)
hidden_to_hiddens = [
getattr(P, 'hidden_to_hidden_%i' % i) for i in range(n_layers - 1)
]
recurrents = [getattr(P, 'recurrent_%i' % i) for i in range(n_layers)]
initial_hidden_means = [
getattr(P, 'initial_hidden_means_%i' % i) for i in range(n_layers)
]
initial_hidden_vars = [
getattr(P, 'initial_hidden_vars_%i' % i)**2 + 1e-4
for i in range(n_layers)
]
hidden_biases = [
getattr(P, 'hidden_bias_%i' % i) for i in range(n_layers)
]
if self.skip_to_out:
skip_to_outs = [
getattr(P, 'hidden_%i_to_out' % i) for i in range(n_layers)
]
in_to_out = P.in_to_out
else:
in_to_out = skip_to_outs = None
inpt_var = T.zeros_like(self.exprs['inpt'])
p_dropouts = ([self.p_dropout_inpt] + self.p_dropout_hiddens +
[self.p_dropout_hidden_to_out])
hidden_var_scales_sqrt = [int(i) for i in self.use_varprop_at[:-1]]
out_var_scale_sqrt = int(self.use_varprop_at[-1])
self.exprs.update(
varprop_rnn.exprs(self.exprs['inpt'],
inpt_var,
P.in_to_hidden,
hidden_to_hiddens,
P.hidden_to_out,
hidden_biases,
hidden_var_scales_sqrt,
initial_hidden_means,
initial_hidden_vars,
recurrents,
P.out_bias,
out_var_scale_sqrt,
self.hidden_transfers,
self.out_transfer,
in_to_out=in_to_out,
skip_to_outs=skip_to_outs,
p_dropouts=p_dropouts,
hotk_inpt=False))
imp_weight = False if not self.imp_weight else self.exprs['imp_weight']
self.exprs.update(
varprop_supervised_loss(self.exprs['target'],
self.exprs['output'],
self.loss,
2,
imp_weight=imp_weight))
