def build_cnn(config, use_noise=True, use_bn=True):
# NOTE: Neither Conv2DDNNLayer nor Conv2DMMLayer will not work
# with T.Rop operation, which used for the Fisher-vector product.
l_input = L.InputLayer((None, 1, config['height'], config['width']))
l_out = L.Conv2DLayer(l_input,
num_filters=config['cnn_f1'], filter_size=(6,6), stride=2,
nonlinearity=relu, W=LI.HeUniform('relu'), b=LI.Constant(0.)
)
# https://arxiv.org/pdf/1602.01407v2.pdf
# QUOTE: KFC-pre and BN can be combined synergistically.
if use_bn: l_out = L.batch_norm(l_out, beta=None, gamma=None)
l_out = L.Conv2DLayer(l_out,
num_filters=config['cnn_f2'], filter_size=(4,4), stride=2,
nonlinearity=relu, W=LI.HeUniform('relu'), b=LI.Constant(0.)
)
if use_bn: l_out = L.batch_norm(l_out, beta=None, gamma=None)
if use_noise: l_out = L.dropout(l_out)
l_out = L.Conv2DLayer(l_out,
num_filters=config['cnn_f3'], filter_size=(4,4), stride=2,
nonlinearity=relu, W=LI.HeUniform('relu'), b=LI.Constant(0.)
)
if use_bn: l_out = L.batch_norm(l_out, beta=None, gamma=None)
if use_noise: l_out = L.dropout(l_out)
return l_input, l_out
def __init__(self, incomings, num_steps, num_units, att_units, num_actns=7,
zero_trainable=True, zero_init=LI.Constant(0.),
W_init=LI.HeUniform(), b_init=LI.Constant(0.), s_init=LI.Constant(1.)):
super(GRULayer, self).__init__(incomings, name=None)
# Output of CNN has shape [batch * step, channel, height, width]
cnn_shape = self.input_shapes[1]
num_chann = cnn_shape[1]
num_pixel = cnn_shape[2] * cnn_shape[3]
# Weights for the initial hidden state and attention map
self.a0 = self.add_param(zero_init, (1, num_pixel), name="a0",
trainable=zero_trainable,
regularizable=False)
self.h01 = self.add_param(zero_init, (1, num_units), name="h01",
trainable=zero_trainable,
regularizable=False)
self.h02 = self.add_param(zero_init, (1, num_units), name="h02",
trainable=zero_trainable,
regularizable=False)
# Weights for the attention gate
self.W_ha = self.add_param(W_init, (num_units, att_units), name="W_ha")
self.W_aa = self.add_param(W_init, (num_pixel, att_units), name="W_aa")
self.W_ua = self.add_param(W_init, (num_actns, att_units), name="W_ua")
self.W_xa = self.add_param(W_init, (num_chann, att_units), name="W_xa")
self.b_p = self.add_param(b_init, (att_units,), name="b_p", regularizable=False)
self.W_pa = self.add_param(W_init, (att_units,1), name="W_pa")
self.b_a = self.add_param(b_init, (1,), name="b_a", regularizable=False)
# Weights for the reset/update gate of 1 layer
self.W_xg = self.add_param(W_init, (num_chann, num_units*2), name="W_xg")
self.W_hg = self.add_param(W_init, (num_units, num_units*2), name="W_hg")
self.b_g = self.add_param(b_init, (num_units*2,), name="b_g", regularizable=False)
# Weights for the cell gate of 1 layer
self.W_xc = self.add_param(W_init, (num_chann, num_units), name="W_xc")
self.W_hc = self.add_param(W_init, (num_units, num_units), name="W_hc")
self.b_c = self.add_param(b_init, (num_units,), name="b_c", regularizable=False)
# Weights for the reset/update gate of 2 layer
self.W2_xg = self.add_param(W_init, (num_units, num_units*2), name="W2_xg")
self.W2_hg = self.add_param(W_init, (num_units, num_units*2), name="W2_hg")
self.b2_g = self.add_param(b_init, (num_units*2,), name="b2_g", regularizable=False)
# Weights for the cell gate of 2 layer
self.W2_xc = self.add_param(W_init, (num_units, num_units), name="W2_xc")
self.W2_hc = self.add_param(W_init, (num_units, num_units), name="W2_hc")
self.b2_c = self.add_param(b_init, (num_units,), name="b2_c", regularizable=False)
self.attention = None
self.hidden1 = None
self.num_units = num_units
self.num_steps = num_steps
self.num_pixel = num_pixel
self.num_chann = num_chann
self.step_shape = (-1, num_chann, num_pixel)
def __init__(self,
env_spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=NL.rectify,
hidden_w_init=LI.HeUniform(),
hidden_b_init=LI.Constant(0.),
output_nonlinearity=NL.tanh,
output_w_init=LI.Uniform(-3e-3, 3e-3),
output_b_init=LI.Uniform(-3e-3, 3e-3),
bn=False):
assert isinstance(env_spec.action_space, Box)
Serializable.quick_init(self, locals())
l_obs = L.InputLayer(shape=(None, env_spec.observation_space.flat_dim))
l_hidden = l_obs
if bn:
l_hidden = batch_norm(l_hidden)
for idx, size in enumerate(hidden_sizes):
l_hidden = L.DenseLayer(
l_hidden,
num_units=size,
W=hidden_w_init,
b=hidden_b_init,
nonlinearity=hidden_nonlinearity,
name="h%d" % idx)
if bn:
l_hidden = batch_norm(l_hidden)
l_output = L.DenseLayer(
l_hidden,
num_units=env_spec.action_space.flat_dim,
W=output_w_init,
b=output_b_init,
nonlinearity=output_nonlinearity,
name="output")
# Note the deterministic=True argument. It makes sure that when getting
# actions from single observations, we do not update params in the
# batch normalization layers
action_var = L.get_output(l_output, deterministic=True)
self._output_layer = l_output
self._f_actions = tensor_utils.compile_function([l_obs.input_var],
action_var)
super(DeterministicMLPPolicy, self).__init__(env_spec)
LasagnePowered.__init__(self, [l_output])
def __init__(self,
incoming,
num_units,
hidden_nonlinearity,
gate_nonlinearity=LN.sigmoid,
name=None,
W_init=LI.HeUniform(),
b_init=LI.Constant(0.),
hidden_init=LI.Constant(0.),
hidden_init_trainable=True):
if hidden_nonlinearity is None:
hidden_nonlinearity = LN.identity
if gate_nonlinearity is None:
gate_nonlinearity = LN.identity
super(GRULayer, self).__init__(incoming, name=name)
input_shape = self.input_shape[2:]
input_dim = ext.flatten_shape_dim(input_shape)
# self._name = name
# Weights for the initial hidden state
self.h0 = self.add_param(hidden_init, (num_units, ),
name="h0",
trainable=hidden_init_trainable,
regularizable=False)
# Weights for the reset gate
self.W_xr = self.add_param(W_init, (input_dim, num_units), name="W_xr")
self.W_hr = self.add_param(W_init, (num_units, num_units), name="W_hr")
self.b_r = self.add_param(b_init, (num_units, ),
name="b_r",
regularizable=False)
# Weights for the update gate
self.W_xu = self.add_param(W_init, (input_dim, num_units), name="W_xu")
self.W_hu = self.add_param(W_init, (num_units, num_units), name="W_hu")
self.b_u = self.add_param(b_init, (num_units, ),
name="b_u",
regularizable=False)
# Weights for the cell gate
self.W_xc = self.add_param(W_init, (input_dim, num_units), name="W_xc")
self.W_hc = self.add_param(W_init, (num_units, num_units), name="W_hc")
self.b_c = self.add_param(b_init, (num_units, ),
name="b_c",
regularizable=False)
self.gate_nonlinearity = gate_nonlinearity
self.num_units = num_units
self.nonlinearity = hidden_nonlinearity
def run(self, shape):
return LI.HeUniform(gain=self.gain).sample(shape)