def __init__(self,
sensor_models,
calibration_model,
lr=1e-4,
batch_size=20,
log_dir=None,
**kwargs):
self.graph = T.core.Graph()
self.log_dir = log_dir
with self.graph.as_default():
self.calibration_model = calibration_model
self.board_ids = list(sensor_models.keys())
self.board_map = {b: i for i, b in enumerate(self.board_ids)}
self.sensor_map = sensor_models
self.sensor_models = [
sensor_models[board_id] for board_id in self.board_ids
]
self.architecture = pickle.dumps(
[sensor_models, calibration_model])
self.batch_size = batch_size
self.lr = lr
self.learning_rate = T.placeholder(T.floatx(), [])
self.sensors = T.placeholder(T.floatx(), [None, 3])
self.env = T.placeholder(T.floatx(), [None, 3])
self.board = T.placeholder(T.core.int32, [None])
self.boards = T.transpose(
T.pack([self.board,
T.range(T.shape(self.board)[0])]))
self.rep = T.gather_nd(
T.pack([
sensor_model(self.sensors)
for sensor_model in self.sensor_models
]), self.boards)
self.rep_ = T.placeholder(T.floatx(),
[None, self.rep.get_shape()[-1]])
rep_env = T.concat([self.rep, self.env], -1)
rep_env_ = T.concat([self.rep_, self.env], -1)
self.y_ = self.calibration_model(rep_env)
self.y_rep = self.calibration_model(rep_env_)
self.y = T.placeholder(T.floatx(), [None, 2])
self.loss = T.mean((self.y - self.y_)**2)
self.mae = T.mean(T.abs(self.y - self.y_))
T.core.summary.scalar('MSE', self.loss)
T.core.summary.scalar('MAE', self.mae)
self.summary = T.core.summary.merge_all()
self.train_op = T.core.train.AdamOptimizer(
self.learning_rate).minimize(self.loss)
self.session = T.interactive_session(graph=self.graph)
def log_prior(self, leaf_values):
return T.mean(
log_normal(leaf_values,
T.zeros_like(leaf_values, dtype='float32'),
T.ones_like(leaf_values, dtype='float32'),
self.embedding_size,
dim=2))
def kl_divergence(self, q_X, q_A, _):
# q_Xt - [N, H, ds]
# q_At - [N, H, da]
if (q_X, q_A) not in self.cache:
info = {}
if self.smooth:
state_prior = stats.GaussianScaleDiag([
T.ones(self.ds),
T.zeros(self.ds)
])
p_X = stats.LDS(
(self.sufficient_statistics(), state_prior, None, q_A.expected_value(), self.horizon),
'internal')
kl = T.mean(stats.kl_divergence(q_X, p_X), axis=0)
Q = self.get_dynamics()[1]
info['model-stdev'] = T.sqrt(T.matrix_diag_part(Q))
else:
q_Xt = q_X.__class__([
q_X.get_parameters('regular')[0][:, :-1],
q_X.get_parameters('regular')[1][:, :-1],
])
q_At = q_A.__class__([
q_A.get_parameters('regular')[0][:, :-1],
q_A.get_parameters('regular')[1][:, :-1],
])
p_Xt1 = self.forward(q_Xt, q_At)
q_Xt1 = q_X.__class__([
q_X.get_parameters('regular')[0][:, 1:],
q_X.get_parameters('regular')[1][:, 1:],
])
rmse = T.sqrt(T.sum(T.square(q_Xt1.get_parameters('regular')[1] - p_Xt1.get_parameters('regular')[1]), axis=-1))
kl = T.mean(T.sum(stats.kl_divergence(q_Xt1, p_Xt1), axis=-1), axis=0)
Q = self.get_dynamics()[1]
model_stdev = T.sqrt(T.matrix_diag_part(Q))
info['rmse'] = rmse
info['model-stdev'] = model_stdev
self.cache[(q_X, q_A)] = kl, info
return self.cache[(q_X, q_A)]
def __init__(self, features, model=None, batch_size=20, lr=1e-4):
super(NeuralNetwork, self).__init__(features)
self.graph = T.core.Graph()
with self.graph.as_default():
self.architecture = pickle.dumps(model)
self.model = model #Relu(6, 200) >> Relu(200) >> Relu(200) >> Relu(200) >> Linear(2)
self.batch_size = batch_size
self.lr = lr
self.X = T.placeholder(T.floatx(), [None, 6])
self.y = T.placeholder(T.floatx(), [None, 2])
self.y_ = self.model(self.X)
self.loss = T.mean((self.y - self.y_) ** 2)
self.train_op = T.core.train.AdamOptimizer(self.lr).minimize(self.loss)
self.session = T.interactive_session(graph=self.graph)
y[i, labels[i]] = 1
split = int(0.9 * N)
train_idx, test_idx = idx[:split], idx[split:]
Xtrain, Xtest = X[train_idx], X[test_idx]
ytrain, ytest = y[train_idx], y[test_idx]
X_in = T.placeholder(T.floatx(), [None, 28, 28, 1])
Y_in = T.placeholder(T.floatx(), [None, 10])
conv_net = Conv((2, 2, 10)) >> Conv((2, 2, 20)) >> Flatten() >> Linear(10)
logits = conv_net(X_in)
predictions = T.argmax(logits, -1)
loss = T.mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y_in))
train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)
sess = T.interactive_session()
def train(n_iter, batch_size=20):
for i in range(n_iter):
idx = np.random.permutation(Xtrain.shape[0])[:batch_size]
result = sess.run([loss, train_op], { X_in : Xtrain[idx], Y_in : ytrain[idx] })
print("Loss:", result[0])
preds = sess.run(predictions, { X_in : Xtest }).astype(np.int32)
print("Error: ", 1 - (preds == labels[test_idx]).sum() / float(N - split))
train(1000)
def kl_divergence(self, q_X, q_A, num_data):
if (q_X, q_A) not in self.cache:
if self.smooth:
state_prior = stats.GaussianScaleDiag(
[T.ones(self.ds), T.zeros(self.ds)])
self.p_X = stats.LDS(
(self.sufficient_statistics(), state_prior, None,
q_A.expected_value(), self.horizon), 'internal')
local_kl = stats.kl_divergence(q_X, self.p_X)
if self.time_varying:
global_kl = T.sum(
stats.kl_divergence(self.A_variational, self.A_prior))
else:
global_kl = stats.kl_divergence(self.A_variational,
self.A_prior)
prior_kl = T.mean(local_kl,
axis=0) + global_kl / T.to_float(num_data)
A, Q = self.get_dynamics()
model_stdev = T.sqrt(T.matrix_diag_part(Q))
self.cache[(q_X, q_A)] = prior_kl, {
'local-kl': local_kl,
'global-kl': global_kl,
'model-stdev': model_stdev,
}
else:
q_Xt = q_X.__class__([
q_X.get_parameters('regular')[0][:, :-1],
q_X.get_parameters('regular')[1][:, :-1],
])
q_At = q_A.__class__([
q_A.get_parameters('regular')[0][:, :-1],
q_A.get_parameters('regular')[1][:, :-1],
])
p_Xt1 = self.forward(q_Xt, q_At)
q_Xt1 = q_X.__class__([
q_X.get_parameters('regular')[0][:, 1:],
q_X.get_parameters('regular')[1][:, 1:],
])
num_data = T.to_float(num_data)
rmse = T.sqrt(
T.sum(T.square(
q_Xt1.get_parameters('regular')[1] -
p_Xt1.get_parameters('regular')[1]),
axis=-1))
A, Q = self.get_dynamics()
model_stdev = T.sqrt(T.matrix_diag_part(Q))
local_kl = T.sum(stats.kl_divergence(q_Xt1, p_Xt1), axis=1)
if self.time_varying:
global_kl = T.sum(
stats.kl_divergence(self.A_variational, self.A_prior))
else:
global_kl = stats.kl_divergence(self.A_variational,
self.A_prior)
self.cache[(q_X, q_A)] = (T.mean(local_kl, axis=0) +
global_kl / T.to_float(num_data), {
'rmse': rmse,
'model-stdev': model_stdev,
'local-kl': local_kl,
'global-kl': global_kl
})
return self.cache[(q_X, q_A)]
def kl_divergence(self, q_X, q_A, num_data):
mu_shape = T.shape(q_X.get_parameters('regular')[1])
p_X = stats.GaussianScaleDiag([T.ones(mu_shape), T.zeros(mu_shape)])
return T.mean(T.sum(stats.kl_divergence(q_X, p_X), -1), 0), {}
if __name__ == "__main__":
args = parse_args()
model_path = Path('results') / args.name / 'models' / 'model_latest.pkl'
dataset1 = load(args.round1, args.location1, args.board1)
dataset2 = load(args.round2, args.location2, args.board2)
train = dataset1[0].join(dataset2[0], lsuffix='-left').dropna()
test = dataset1[1].join(dataset2[1], lsuffix='-left').dropna()
model = joblib.load(model_path)
fixer_model = pickle.loads(model.architecture)[0][args.board2]
X = T.placeholder(T.floatx(), [None, 3])
Y = T.placeholder(T.floatx(), [None, 3])
Y_ = fixer_model(X)
loss = T.mean((Y - Y_)**2)
train_op = T.core.train.AdamOptimizer(1e-4).minimize(
loss, var_list=fixer_model.get_parameters())
X_data_train = train[[s + '-left' for s in sensor_features]].as_matrix()
Y_data_train = model.representation(train[sensor_features], train['board'])
X_data_test = test[[s + '-left' for s in sensor_features]].as_matrix()
Y_data_test = model.representation(test[sensor_features], test['board'])
sess = T.interactive_session()
fit_nn(X_data_train, Y_data_train, fixer_model, batch_size=64)
train_preds = model.calibrate(sess.run(Y_, {X: X_data_train}),
train[env_features])
train_mae = abs(train_preds - train[Y_features])
test_preds = model.calibrate(sess.run(Y_, {X: X_data_test}),
test[env_features])
def initialize(self):
self.graph = T.core.Graph()
with self.graph.as_default():
prior_params = self.prior_params.copy()
prior_type = prior_params.pop('prior_type')
self.prior = PRIOR_MAP[prior_type](self.ds, self.da, self.horizon, **prior_params)
cost_params = self.cost_params.copy()
cost_type = cost_params.pop('cost_type')
self.cost = COST_MAP[cost_type](self.ds, self.da, **cost_params)
self.O = T.placeholder(T.floatx(), [None, None, self.do])
self.U = T.placeholder(T.floatx(), [None, None, self.du])
self.C = T.placeholder(T.floatx(), [None, None])
self.S = T.placeholder(T.floatx(), [None, None, self.ds])
self.A = T.placeholder(T.floatx(), [None, None, self.da])
self.t = T.placeholder(T.int32, [])
self.state, self.action = T.placeholder(T.floatx(), [None, self.ds]), T.placeholder(T.floatx(), [None, self.da])
if self.prior.has_dynamics():
self.next_state = self.prior.next_state(self.state, self.action, self.t)
self.prior_dynamics = self.prior.get_dynamics()
self.num_data = T.scalar()
self.beta = T.placeholder(T.floatx(), [])
self.learning_rate = T.placeholder(T.floatx(), [])
self.model_learning_rate = T.placeholder(T.floatx(), [])
self.S_potentials = util.map_network(self.state_encoder)(self.O)
self.A_potentials = util.map_network(self.action_encoder)(self.U)
if self.prior.is_dynamics_prior():
self.data_strength = T.placeholder(T.floatx(), [])
self.max_iter = T.placeholder(T.int32, [])
posterior_dynamics, (encodings, actions) = \
self.prior.posterior_dynamics(self.S_potentials, self.A_potentials,
data_strength=self.data_strength,
max_iter=self.max_iter)
self.posterior_dynamics_ = posterior_dynamics, (encodings.expected_value(), actions.expected_value())
if self.prior.is_filtering_prior():
self.prior_dynamics_stats = self.prior.sufficient_statistics()
self.dynamics_stats = (
T.placeholder(T.floatx(), [None, self.ds, self.ds]),
T.placeholder(T.floatx(), [None, self.ds, self.ds + self.da]),
T.placeholder(T.floatx(), [None, self.ds + self.da, self.ds + self.da]),
T.placeholder(T.floatx(), [None]),
)
S_natparam = self.S_potentials.get_parameters('natural')
num_steps = T.shape(S_natparam)[1]
self.padded_S = stats.Gaussian(T.core.pad(
self.S_potentials.get_parameters('natural'),
[[0, 0], [0, self.horizon - num_steps], [0, 0], [0, 0]]
), 'natural')
self.padded_A = stats.GaussianScaleDiag([
T.core.pad(self.A_potentials.get_parameters('regular')[0],
[[0, 0], [0, self.horizon - num_steps], [0, 0]]),
T.core.pad(self.A_potentials.get_parameters('regular')[1],
[[0, 0], [0, self.horizon - num_steps], [0, 0]])
], 'regular')
self.q_S_padded, self.q_A_padded = self.prior.encode(
self.padded_S, self.padded_A,
dynamics_stats=self.dynamics_stats
)
self.q_S_filter = self.q_S_padded.filter(max_steps=num_steps)
self.q_A_filter = self.q_A_padded.__class__(
self.q_A_padded.get_parameters('natural')[:, :num_steps]
, 'natural')
self.e_q_S_filter = self.q_S_filter.expected_value()
self.e_q_A_filter = self.q_A_filter.expected_value()
(self.q_S, self.q_A), self.prior_kl, self.kl_grads, self.info = self.prior.posterior_kl_grads(
self.S_potentials, self.A_potentials, self.num_data
)
self.q_S_sample = self.q_S.sample()[0]
self.q_A_sample = self.q_A.sample()[0]
self.q_O = util.map_network(self.state_decoder)(self.q_S_sample)
self.q_U = util.map_network(self.action_decoder)(self.q_A_sample)
self.q_O_sample = self.q_O.sample()[0]
self.q_U_sample = self.q_U.sample()[0]
self.q_O_ = util.map_network(self.state_decoder)(self.S)
self.q_U_ = util.map_network(self.action_decoder)(self.A)
self.q_O__sample = self.q_O_.sample()[0]
self.q_U__sample = self.q_U_.sample()[0]
self.cost_likelihood = self.cost.log_likelihood(self.q_S_sample, self.C)
if self.cost.is_cost_function():
self.evaluated_cost = self.cost.evaluate(self.S)
self.log_likelihood = T.sum(self.q_O.log_likelihood(self.O), axis=1)
self.elbo = T.mean(self.log_likelihood + self.cost_likelihood - self.prior_kl)
train_elbo = T.mean(self.log_likelihood + self.beta * (self.cost_likelihood - self.prior_kl))
T.core.summary.scalar("encoder-stdev", T.mean(self.S_potentials.get_parameters('regular')[0]))
T.core.summary.scalar("log-likelihood", T.mean(self.log_likelihood))
T.core.summary.scalar("cost-likelihood", T.mean(self.cost_likelihood))
T.core.summary.scalar("prior-kl", T.mean(self.prior_kl))
T.core.summary.scalar("beta", self.beta)
T.core.summary.scalar("elbo", self.elbo)
T.core.summary.scalar("beta-elbo", train_elbo)
for k, v in self.info.items():
T.core.summary.scalar(k, T.mean(v))
self.summary = T.core.summary.merge_all()
neural_params = (
self.state_encoder.get_parameters()
+ self.state_decoder.get_parameters()
+ self.action_encoder.get_parameters()
+ self.action_decoder.get_parameters()
)
cost_params = self.cost.get_parameters()
if len(neural_params) > 0:
optimizer = T.core.train.AdamOptimizer(self.learning_rate)
gradients, variables = zip(*optimizer.compute_gradients(-train_elbo, var_list=neural_params))
gradients, _ = tf.clip_by_global_norm(gradients, 5.0)
self.neural_op = optimizer.apply_gradients(zip(gradients, variables))
else:
self.neural_op = T.core.no_op()
if len(cost_params) > 0:
self.cost_op = T.core.train.AdamOptimizer(self.learning_rate).minimize(-self.elbo, var_list=cost_params)
else:
self.cost_op = T.core.no_op()
if len(self.kl_grads) > 0:
if self.prior.is_dynamics_prior():
# opt = lambda x: T.core.train.MomentumOptimizer(x, 0.5)
opt = lambda x: T.core.train.GradientDescentOptimizer(x)
else:
opt = T.core.train.AdamOptimizer
self.dynamics_op = opt(self.model_learning_rate).apply_gradients([
(b, a) for a, b in self.kl_grads
])
else:
self.dynamics_op = T.core.no_op()
self.train_op = T.core.group(self.neural_op, self.dynamics_op, self.cost_op)
self.session = T.interactive_session(graph=self.graph, allow_soft_placement=True, log_device_placement=False)
q_w.get_parameters('natural')) / N
next_w = Gaussian(q_w.get_parameters('natural') + lr * natural_gradient,
parameter_type='natural')
l_w = kl_divergence(q_w, p_w)[0]
p_y = Bernoulli(T.sigmoid(T.einsum('jw,iw->ij', next_w.expected_value(), x)))
l_y = T.sum(p_y.log_likelihood(y[..., None]))
elbo = l_w + l_y
nat_op = T.assign(q_w.get_parameters('natural'),
next_w.get_parameters('natural'))
grad_op = tf.train.RMSPropOptimizer(1e-4).minimize(-elbo)
train_op = tf.group(nat_op, grad_op)
sess = T.interactive_session()
predictions = T.cast(
T.sigmoid(T.einsum('jw,iw->i', q_w.expected_value(), T.to_float(X))) + 0.5,
np.int32)
accuracy = T.mean(
T.to_float(T.equal(predictions, T.constant(Y.astype(np.int32)))))
def iter(num_iter=1, b=100):
for _ in range(num_iter):
idx = np.random.permutation(N)[:b]
sess.run(train_op, {x: X[idx], y: Y[idx]})
print("%f" % (tuple(sess.run([elbo], {x: X, y: Y}))))
print(sess.run(q_w.get_parameters('regular')[1][0]), coef_[0])
print(sess.run(accuracy), score_)
def log_likelihood(self, batch, batch_z):
z = Vector(self.input_size, placeholder=batch_z, is_input=False)
p = (z >> self.p_network).get_graph_outputs()[0]
return T.mean(batch * p + (1 - batch) * T.log(1 - p + 1e-10))
def log_likelihood(self, batch_z, batch):
x = Vector(self.input_size, placeholder=batch, is_input=False)
mu, sigma = (x >> self.q_network).get_graph_outputs()
sigma = T.sqrt(T.exp(sigma))
return T.mean(
log_normal(batch_z, mu, sigma, self.embedding_size, dim=2))