def build_squarederror_regression(self):
self.X_sym = S.csr_matrix(name='inputs', dtype=self.dtype)
self.Y_sym = T.matrix(name='y_true', dtype=self.dtype)
self.X_autoencoder_sym = T.matrix(name='x_autoencoder',
dtype=self.dtype)
self.Y_autoencoder_sym = T.matrix(name='y_autoencoder',
dtype=self.dtype)
l_in_text = lasagne.layers.InputLayer(shape=(None, self.input_size),
input_var=self.X_sym)
if self.drop_out and self.dropout_coef > 0:
l_in_text = lasagne_layers.SparseInputDropoutLayer(
l_in_text, p=self.dropout_coef)
l_hid_text = SparseInputDenseLayer(
l_in_text,
num_units=self.hid_size,
nonlinearity=lasagne.nonlinearities.tanh,
W=lasagne.init.GlorotUniform())
if self.drop_out and self.dropout_coef > 0:
l_hid_text = lasagne.layers.dropout(l_hid_text,
p=self.dropout_coef)
self.l_out = lasagne.layers.DenseLayer(
l_hid_text,
num_units=2,
nonlinearity=lasagne.nonlinearities.linear,
W=lasagne.init.GlorotUniform())
output = lasagne.layers.get_output(self.l_out, self.X_sym)
loss = lasagne.objectives.squared_error(output, self.Y_sym).mean()
output_eval = lasagne.layers.get_output(self.l_out,
self.X_sym,
deterministic=True)
if self.regul_coef:
l1_share_out = 0.5
l1_share_hid = 0.5
regul_coef_out, regul_coef_hid = self.regul_coef, self.regul_coef
logging.info(
'regul coefficient for output and hidden lasagne_layers is ' +
str(self.regul_coef))
l1_penalty = lasagne.regularization.regularize_layer_params(
self.l_out, l1) * regul_coef_out * l1_share_out
l2_penalty = lasagne.regularization.regularize_layer_params(
self.l_out, l2) * regul_coef_out * (1 - l1_share_out)
l1_penalty = lasagne.regularization.regularize_layer_params(
l_hid_text, l1) * regul_coef_hid * l1_share_hid
l2_penalty = lasagne.regularization.regularize_layer_params(
l_hid_text, l2) * regul_coef_hid * (1 - l1_share_hid)
loss = loss + l1_penalty + l2_penalty
parameters = lasagne.layers.get_all_params(self.l_out, trainable=True)
updates = lasagne.updates.adam(loss,
parameters,
learning_rate=1e-3,
beta1=0.9,
beta2=0.999,
epsilon=1e-8)
self.f_train = theano.function([self.X_sym, self.Y_sym],
loss,
updates=updates,
on_unused_input='warn')
self.f_val = theano.function([self.X_sym, self.Y_sym],
loss,
on_unused_input='warn')
self.f_predict = theano.function([self.X_sym],
output_eval,
on_unused_input='warn')
def build(self):
self.X_sym = S.csr_matrix(name='inputs', dtype=self.dtype)
self.Y_sym = T.matrix(name='y_true', dtype=self.dtype)
l_in_text = lasagne.layers.InputLayer(shape=(None, self.input_size),
input_var=self.X_sym)
if self.drop_out and self.dropout_coef > 0:
l_in_text = lasagne_layers.SparseInputDropoutLayer(
l_in_text, p=self.dropout_coef)
l_hid_text = SparseInputDenseLayer(
l_in_text,
num_units=self.hid_size,
nonlinearity=lasagne.nonlinearities.tanh,
W=lasagne.init.GlorotUniform())
#if self.drop_out and self.dropout_coef > 0:
# l_hid_text = lasagne.layers.dropout(l_hid_text, p=self.dropout_coef)
self.l_out_gaus = lasagne.layers.DenseLayer(
l_hid_text,
num_units=self.n_bigaus_comp * 6,
nonlinearity=lasagne.nonlinearities.linear,
W=lasagne.init.GlorotUniform())
#sq_error_coef = 0.01
output = lasagne.layers.get_output(self.l_out_gaus, self.X_sym)
mus, sigmas, corxy, pis = self.unpack_params(output,
n_comp=self.n_bigaus_comp)
loss = self.nll_loss(mus, sigmas, corxy, pis, self.Y_sym)
#predicted_mu = self.get_symb_mus(mus, sigmas, corxy, pis)
#loss += lasagne.objectives.squared_error(predicted_mu, self.Y_sym).mean() * sq_error_coef
if self.regul_coef:
l1_share_out = 0.5
l1_share_hid = 0.5
regul_coef_out, regul_coef_hid = self.regul_coef, self.regul_coef
logging.info(
'regul coefficient for output and hidden lasagne_layers is ' +
str(self.regul_coef))
l1_penalty = lasagne.regularization.regularize_layer_params(
self.l_out_gaus, l1) * regul_coef_out * l1_share_out
l2_penalty = lasagne.regularization.regularize_layer_params(
self.l_out_gaus, l2) * regul_coef_out * (1 - l1_share_out)
l1_penalty = lasagne.regularization.regularize_layer_params(
l_hid_text, l1) * regul_coef_hid * l1_share_hid
l2_penalty = lasagne.regularization.regularize_layer_params(
l_hid_text, l2) * regul_coef_hid * (1 - l1_share_hid)
loss = loss + l1_penalty + l2_penalty
parameters = lasagne.layers.get_all_params(self.l_out_gaus,
trainable=True)
updates = lasagne.updates.adam(loss,
parameters,
learning_rate=1e-3,
beta1=0.9,
beta2=0.999,
epsilon=1e-8)
self.f_train = theano.function([self.X_sym, self.Y_sym],
loss,
updates=updates,
on_unused_input='warn')
self.f_val = theano.function([self.X_sym, self.Y_sym],
loss,
on_unused_input='warn')
self.f_predict = theano.function([self.X_sym],
[mus, sigmas, corxy, pis],
on_unused_input='warn')