def forward_MLP(self, name, all_params, input_tensor=None,
batch_normalization=False, reuse=True, is_training=False, bias_transform=False):
# is_training and reuse are for batch norm, irrelevant if batch_norm set to False
# set reuse to False if the first time this func is called.
with tf.variable_scope(name):
if input_tensor is None:
l_in = make_input(shape=self.input_shape, input_var=None, name='input')
else:
l_in = input_tensor
l_hid = l_in
for idx in range(self.n_hidden):
bias_transform_ = all_params['bias_transform' + str(idx)] if bias_transform else None
l_hid = forward_dense_bias_transform(l_hid, all_params['W' + str(idx)], all_params['b' + str(idx)],
bias_transform=bias_transform_,
batch_norm=batch_normalization,
nonlinearity=self.hidden_nonlinearity,
scope=str(idx), reuse=reuse,
is_training=is_training
)
bias_transform = all_params['bias_transform' + str(self.n_hidden)] if bias_transform else None
output = forward_dense_bias_transform(l_hid, all_params['W' + str(self.n_hidden)],
all_params['b' + str(self.n_hidden)],
bias_transform=bias_transform, batch_norm=False,
nonlinearity=self.output_nonlinearity,
)
return l_in, output
def forward_MLP(self,
name,
all_params,
input_tensor=None,
input_shape=None,
n_hidden=-1,
hidden_nonlinearity=tf.identity,
output_nonlinearity=tf.identity,
batch_normalization=False,
reuse=True,
is_training=False):
# is_training and reuse are for batch norm, irrelevant if batch_norm set to False
# set reuse to False if the first time this func is called.
with tf.variable_scope(name):
if input_tensor is None:
assert input_shape is not None
l_in = make_input(shape=(None, ) + input_shape,
input_var=None,
name='input')
else:
l_in = input_tensor
l_hid = l_in
for idx in range(n_hidden):
l_hid = forward_dense_layer(l_hid,
all_params['W' + str(idx)],
all_params['b' + str(idx)],
batch_norm=batch_normalization,
nonlinearity=hidden_nonlinearity,
scope=str(idx),
reuse=reuse,
is_training=is_training)
output = forward_dense_layer(
l_hid,
all_params['W' + str(n_hidden)],
all_params['b' + str(n_hidden)],
batch_norm=False,
nonlinearity=output_nonlinearity,
)
return l_in, output