def line_search_general(objective_func,
var_list=None,
equalities=None,
inequalities=None,
methods=None,
maxiter=DEFAULT_LINE_SEARCH_BFGS_MAX_ITER):
"""
Line Search Optimizer for non-convex optimization problem:
L-BFGS by default.
"""
if methods == 'backtracking':
# TODO
raise NotImplementedError
elif methods is not None:
return ScipyOptimizerInterface(objective_func,
var_list,
equalities,
inequalities,
method=methods,
options={'maxiter': maxiter})
else:
return ScipyOptimizerInterface(objective_func,
var_list,
equalities,
inequalities,
options={'maxiter': maxiter})
def _build_net(self):
with tf.Graph().as_default() as g:
self._init_variables()
self._init_placeholders()
self.U_hat = self.__NN(self.X)
self.loss_U = self._get_loss(self.U, self.U_hat)
self.loss_dU = self._get_loss_du()
self.loss = self.loss_U + self.regularization_param * self.loss_dU
self.optimizer_BFGS = ScipyOptimizerInterface(
self.loss,
method='L-BFGS-B',
options={'maxiter': 50000,
'maxfun': 50000,
'maxcor': 50,
'maxls': 50,
'ftol': 1.0 * np.finfo(float).eps,
'gtol': 1.0 * np.finfo(float).eps})
init = tf.global_variables_initializer()
self.sess = tf.Session(graph=g)
self.sess.run(init)
self.sess.graph.finalize()
def __init__(self,
loss,
var_list=None,
lr=1e-3,
clip_val=10.0,
iteration=500,
**optimizer_kwargs):
super(ScipyADAMOptimizerInterface,
self).__init__(loss, var_list, lr, clip_val, iteration,
**optimizer_kwargs)
self.scipy_optimizer = ScipyOptimizerInterface(loss, var_list)
def fit(self,
sess,
inputs,
outputs,
var_list=None,
spo_config=None,
feed_dict=None,
**kwargs):
'''
Fit a given model state via MAP estimation.
'''
assert inputs.ndim == outputs.ndim == 2, 'Tensor rank should be 2'
if (feed_dict is None): feed_dict = dict()
# Which <tf.Variable> should we optimize?
if (var_list is None):
filter_fn = lambda v: isinstance(v, tf.Variable)
var_list = filter(filter_fn, self.state.values())
var_list = tuple(var_list)
# Build/retrieve negative log likelihood
input_dim, output_dim = inputs.shape[-1], outputs.shape[-1]
inputs_ref = self.get_or_create_ref('inputs/old', [None, input_dim])
outputs_ref = self.get_or_create_ref('outputs/old', [None, output_dim])
nll = self.get_or_create_node\
(
group='log_likelihood',
fn=self.log_likelihood,
args=(inputs_ref, outputs_ref),
kwargs={**kwargs, 'state_id':self.active_state, 'as_negative':True},
)
# Get (updated) copy of configuration for Scipy Optimize
spo_config = self.update_dict(self.spo_config,
spo_config,
as_copy=True)
# For active parameters, replace <string> keys with <tf.Variable>
var_to_bounds = dict()
for key, bounds in spo_config.get('var_to_bounds', {}).items():
for var in var_list:
if key in var.name: #[!] too permissive, improve me...
var_to_bounds[var] = bounds
break
spo_config['var_to_bounds'] = var_to_bounds
# Initialize/run optimizer
feed_dict = {**feed_dict, inputs_ref: inputs, outputs_ref: outputs}
optimizer = ScipyOptimizerInterface(nll, var_list, **spo_config)
optimizer.minimize(sess, feed_dict)
return var_list
def X_optimizer(self):
"""
Get the optimizer that has the method 'minimize' to do the optimization.
:return:
An ExternalOptimizerInterface
"""
if self.loss == 'none' and self.prev_layer.activation == 'none':
return XFINInterface(self)
if self.prev_layer.activation == 'relu':
return ScipyOptimizerInterface(
self.X_loss,
var_list=[self.X],
var_to_bounds={self.X: (0, np.infty)})
else:
return ScipyOptimizerInterface(self.X_loss, var_list=[self.X])
def __init__(self, settings):
self.settings = settings
self.placeholder = self.build_placeholder()
self.model = self.build_model()
self.optimizer = ScipyOptimizerInterface(self.model['cost'],
method='L-BFGS-B',
options={
'maxiter':
self.settings['max_iter'],
'disp':
True
})
init = tf.global_variables_initializer()
self.sess = tf.Session(config=tf.ConfigProto(
log_device_placement=False))
self.sess.run(init)
def W_optimizer(self):
"""
Get the optimizer that has the method 'minimize' to do the optimization.
:return:
An ExternalOptimizerInterface
"""
alpha = self.rhod if self.rhod > 0.0 else self.rho
if self.is_last_layer and self.loss == 'cross_entropy':
return ScipyOptimizerInterface(self.Wb_loss, var_list=[self.W])
#return RidgeInterface(self.W, self.b, self.X, self.X_next, W_offset=self.W_0, alpha=alpha, normalize=False)
elif self.is_last_layer and self.loss == 'none':
return RidgeInterface(self.W,
self.b,
self.X,
self.X_next,
W_offset=self.W_0,
alpha=alpha,
normalize=False)
else:
# optimize both W and b
#return ScipyOptimizerInterface(self.Wb_loss, var_list=[self.W, self.b])
return RidgeInterface(self.W,
self.b,
self.X,
self.X_next,
W_offset=self.W_0,
alpha=alpha / self.lmbda,
normalize=False)
def W_optimizer(self):
"""
Get the optimizer that has the method 'minimize' to do the optimization.
:return:
An ExternalOptimizerInterface
"""
return ScipyOptimizerInterface(self.Wb_loss, var_list=[self.W, self.b])
def X_optimizer(self):
"""
Get the optimizer that has the method 'minimize' to do the optimization.
:return:
An ExternalOptimizerInterface
"""
if self.prev_layer.activation == 'relu':
return ScipyOptimizerInterface(
self.X_loss,
var_list=[self.X],
var_to_bounds={
self.X: (0, np.infty)
}) #, options={'ftol': 2e-15, 'gtol': 1e-11, 'maxls': 100})
else:
return ScipyOptimizerInterface(self.X_loss, var_list=[self.X])
raise NotImplementedError
def build_trpo_SOI(self):
# I use index _k to fix variables in graph
# Fixed probs on k-th iteration
self.soft_out_k = tf.placeholder('float32', name="SOFTOUT_K")
# Fixed advantage on k-th iteration
self.A_k = tf.placeholder('float32', name="A_K")
# Number of steps to estimate expectation
self.N = tf.placeholder('float32', name="number")
# Advantage function = emperical_return - baseline
self.A = self.q_return - self.q_out
# Choosing particular action "actions" and multiply by A_k
#here was a mistake -> A instead of A_k
trpo_obj = -tf.reduce_mean(self.A_k * tf.gather(tf.exp(self.soft_out - self.soft_out_k), self.actions))
# KL(soft_out_k, soft_out) should be less than KL_delta
constraints = [(-self.kl(self.soft_out_k, self.soft_out) + self.KL_delta)]
# Use ScipyOptimiztationInterface (SOI) to solve optimization task with constrains
self.trpo_opt = SOI(trpo_obj,
method='SLSQP',
inequalities=constraints,
options={'maxiter': 3})
class ScipyADAMOptimizerInterface(ADAMOptimizerInterface):
def __init__(self,
loss,
var_list=None,
lr=1e-3,
clip_val=10.0,
iteration=500,
**optimizer_kwargs):
super(ScipyADAMOptimizerInterface,
self).__init__(loss, var_list, lr, clip_val, iteration,
**optimizer_kwargs)
self.scipy_optimizer = ScipyOptimizerInterface(loss, var_list)
def minimize(self, sess, feed_dict):
super(ScipyADAMOptimizerInterface, self).minimize(sess, feed_dict)
self.scipy_optimizer.minimize(sess, feed_dict)
def W_optimizer(self):
"""
Get the optimizer that has the method 'minimize' to do the optimization.
:return:
An ExternalOptimizerInterface
"""
return ScipyOptimizerInterface(
self.Wb_loss, var_list=[self.W, self.b]
) #, options={'ftol': 2e-15, 'gtol': 1e-15, 'maxls': 100, 'eps': 1e-12})
raise NotImplementedError
def _optimize_zinb(mu, dropout, theta=None):
pred, a, b, t = _tf_zinb_zero(mu, theta)
#loss = tf.reduce_mean(tf.abs(tf_logit(pred) - tf_logit(dropout)))
loss = tf.losses.log_loss(labels=dropout.astype('float32'),
predictions=pred)
optimizer = ScipyOptimizerInterface(loss, options={'maxiter': 100})
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
optimizer.minimize(sess)
ret_a = sess.run(a)
ret_b = sess.run(b)
if theta is None:
ret_t = sess.run(t)
else:
ret_t = t
return ret_a, ret_b, ret_t
def get_optimizer(loss, n_opt_steps, var_to_bounds, inequalities):
options = {'maxiter': n_opt_steps, 'disp': True, 'ftol': 1e-15}
with tf.name_scope('optimizer'):
optimizer = ScipyOptimizerInterface(loss,
options=options,
method='SLSQP',
var_to_bounds=var_to_bounds,
inequalities=inequalities)
return optimizer
def fit(self, sess, data, feed_dict, maxiter):
pred = self.get_pred(data)
loss, pred_normed, labels_normed = self.get_loss(pred, data['labels'])
optimizer = ScipyOptimizerInterface(loss, options={'maxiter': maxiter})
self.losses = []
def append_loss(loss):
self.losses.append(loss)
optimizer.minimize(sess,
feed_dict=feed_dict,
loss_callback=append_loss,
fetches=[loss])
for name, var in self.vars.items():
self.vars_evals[name] = sess.run(var)
self.eval_pred, self.eval_pred_normed, self.eval_label, self.eval_label_normed = sess.run(
[pred, pred_normed, data['labels'], labels_normed],
feed_dict=feed_dict)
self.r2 = stats.linregress(self.eval_pred_normed.flatten(),
self.eval_label_normed.flatten())[2]**2
self.final_loss = sess.run(loss, feed_dict=feed_dict)
def __init__(self, linear_system_solver_gen=None, tolerance=None, name='ImplicitHG'):
super(ImplicitHG, self).__init__(name)
if linear_system_solver_gen is None:
linear_system_solver_gen = lambda _obj, var_list, _tolerance: ScipyOptimizerInterface(
_obj, var_list=var_list, options={'maxiter': 100}, method='cg', tol=_tolerance)
self.linear_system_solver = linear_system_solver_gen
if tolerance is None:
tolerance = lambda _k: 0.1 * (0.9 ** _k)
self.tolerance = tolerance
self._lin_sys = []
self._qs = []
def reconmodel(config, data, sigma=0.01**0.5, maxiter=100):
bs, nc = config['boxsize'], config['nc']
kmesh = sum(kk**2 for kk in config['kvec'])**0.5
priorwt = config['ipklin'](kmesh) * bs**-3
g = tf.Graph()
with g.as_default():
initlin = tf.placeholder(tf.float32, data.shape, name='initlin')
linear = tf.get_variable('linmesh',
shape=(nc, nc, nc),
initializer=tf.random_normal_initializer(),
trainable=True)
initlin_op = linear.assign(initlin, name='initlin_op')
#PM
icstate = tfpm.lptinit(linear, config, name='icstate')
fnstate = tfpm.nbody(icstate, config, verbose=False, name='fnstate')
final = tf.zeros_like(linear)
final = tfpf.cic_paint(final, fnstate[0], boxsize=bs, name='final')
#
#Prior
lineark = tfpf.r2c3d(linear, norm=nc**3)
priormesh = tf.square(tf.cast(tf.abs(lineark), tf.float32))
prior = tf.reduce_sum(tf.multiply(priormesh, 1 / priorwt))
prior = tf.multiply(prior, 1 / nc**3, name='prior')
data2d = data.sum(axis=0)
final2d = tf.reduce_sum(final, axis=0)
residual = tf.subtract(final2d, data2d)
residual = tf.multiply(residual, 1 / sigma)
chisq = tf.multiply(residual, residual)
chisq = tf.reduce_sum(chisq)
chisq = tf.multiply(chisq, 1 / nc**2, name='chisq')
loss = tf.add(chisq, prior, name='loss')
optimizer = ScipyOptimizerInterface(loss,
var_list=[linear],
method='L-BFGS-B',
options={'maxiter': maxiter})
tf.add_to_collection('utils', [initlin_op, initlin])
tf.add_to_collection('opt', optimizer)
tf.add_to_collection('diagnostics', [prior, chisq, loss])
tf.add_to_collection('reconpm', [linear, final, fnstate, final2d])
tf.add_to_collection('data', [data, data2d])
return g
def _init_optimizers(self):
'''
Initialize optimizers
By default LBFGS-B and Adam are initialized.
'''
self.optimizer_BFGS = ScipyOptimizerInterface(
self.loss,
method='L-BFGS-B',
options={
'maxiter': 50000,
'maxfun': 50000,
'maxcor': 50,
'maxls': 50,
'gtol': 1.0 * np.finfo(float).eps,
'ftol': 1.0 * np.finfo(float).eps
})
if self.learning_rate is not None:
self.optimizer_Adam = tf.train.AdamOptimizer(
self.learning_rate).minimize(self.loss)
else:
self.optimizer_Adam = tf.train.AdamOptimizer(
**self.optimizer_kwargs).minimize(self.loss)
def __init__(
self,
inner_method="Trad",
linear_system_solver_gen=None,
name="BMLOuterGradImplicit",
):
super(BOMLOuterGradImplicit, self).__init__(name)
self._inner_method = inner_method
if linear_system_solver_gen is None:
linear_system_solver_gen = lambda _obj, var_list, _tolerance: ScipyOptimizerInterface(
_obj,
var_list=var_list,
options={"maxiter": 5},
method="cg",
tol=_tolerance,
)
self.linear_system_solver = linear_system_solver_gen
self.tolerance = lambda _k: 0.1 * (0.9 ** _k)
self._lin_sys = []
self._qs = []
def minimize_task(config,
sess,
task,
num_starts=None,
num_options=None,
scope='minimize_task',
reuse=None,
spo_config=None,
dtype=None,
rng=None):
'''
Estimate task minimum using multi-start L-BFGS-B.
'''
if (rng is None): rng = npr.RandomState(config.seed)
if (dtype is None): dtype = task.dtype
if (spo_config is None): spo_config = dict()
if (num_starts is None): num_starts = config.num_starts_fmin
if (num_options is None): num_options = config.num_options_fmin
with tf.variable_scope(scope, reuse=reuse) as vs:
# Build minimization target
shape = [num_starts, task.input_dim]
inputs_var = tf.get_variable(
'inputs',
shape=shape,
dtype=dtype,
initializer=tf.random_uniform_initializer())
task_op = task.tensorflow(inputs_var, noisy=False, stop_gradient=False)
loss_op = tf.reduce_mean(task_op)
# Find starting positions via initial random sweep
counter, x_mins, f_mins, = 0, None, None
while counter + num_starts <= num_options:
inputs = rng.rand(*shape)
outputs = np.squeeze(sess.run(task_op, {inputs_var: inputs}))
if (counter == 0):
x_mins, f_mins = inputs, outputs
else:
inputs = np.vstack([x_mins, inputs])
outputs = np.hstack([f_mins, outputs])
argmins = np.argpartition(outputs, num_starts - 1)[:num_starts]
x_mins, f_mins = inputs[argmins], outputs[argmins]
counter += num_starts
_ = sess.run(tf.assign(inputs_var, x_mins))
# Initialize task optimizer
spo_config =\
{
'method' : 'L-BFGS-B',
'var_list' : [inputs_var],
'var_to_bounds' : {inputs_var : (0, 1)},
'options' : {'maxiter' : 1024},
**spo_config, #user-specified settings take precedence
}
optimizer = ScipyOptimizerInterface(loss_op, **spo_config)
# Run task optimizer
_ = sess.run(tf.variables_initializer([inputs_var]))
_ = optimizer.minimize(sess)
# Evaluate task at optimized input locations
inputs, outputs = sess.run([inputs_var, task_op])
argmin = np.argmin(outputs)
x_min = inputs[argmin]
f_min = outputs[argmin, 0]
return x_min, f_min
def _set_up_graph(self):
"""Initialize TF objects (needed before fitting or restoring)."""
# Input values.
if self.is_sparse_:
self._x_inds = tf.placeholder(tf.int64, [None, 2], "x_inds")
self._x_vals = tf.placeholder(tf.float32, [None], "x_vals")
self._x_shape = tf.placeholder(tf.int64, [2], "x_shape")
self._x = tf.sparse_reorder(
tf.SparseTensor(self._x_inds, self._x_vals, self._x_shape))
x2 = tf.sparse_reorder(
tf.SparseTensor(self._x_inds, self._x_vals * self._x_vals,
self._x_shape))
matmul = tf.sparse_tensor_dense_matmul
else:
self._x = tf.placeholder(tf.float32, [None, self.n_dims_], "x")
x2 = self._x * self._x
matmul = tf.matmul
if self._output_size == 1:
self._y = tf.placeholder(tf.float32, [None], "y")
else:
self._y = tf.placeholder(tf.int32, [None], "y")
with tf.variable_scope("fm"):
self._v = tf.get_variable(
"v", [self.rank, self.n_dims_, self._output_size])
self._beta = tf.get_variable("beta",
[self.n_dims_, self._output_size])
self._beta0 = tf.get_variable("beta0", [self._output_size])
vx = tf.stack(
[matmul(self._x, self._v[i, :, :]) for i in range(self.rank)],
axis=-1)
v2 = self._v * self._v
v2x2 = tf.stack([matmul(x2, v2[i, :, :]) for i in range(self.rank)],
axis=-1)
int_term = 0.5 * tf.reduce_sum(tf.square(vx) - v2x2, axis=-1)
self._logit_y_proba \
= self._beta0 + matmul(self._x, self._beta) + int_term
if self._output_size == 1:
self._logit_y_proba = tf.squeeze(self._logit_y_proba)
self._obj_func = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=self._logit_y_proba, labels=self._y))
self._y_proba = tf.sigmoid(self._logit_y_proba)
else:
self._obj_func = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=self._logit_y_proba, labels=self._y))
self._y_proba = tf.nn.softmax(self._logit_y_proba)
if self.lambda_v > 0:
self._obj_func \
+= self.lambda_v * tf.reduce_sum(tf.square(self._v))
if self.lambda_beta > 0:
self._obj_func \
+= self.lambda_beta * tf.reduce_sum(tf.square(self._beta))
if isinstance(self.solver, str):
from tensorflow.contrib.opt import ScipyOptimizerInterface
self._train_step = ScipyOptimizerInterface(
self._obj_func,
method=self.solver,
options=self.solver_kwargs if self.solver_kwargs else {})
else:
self._train_step = self.solver(
**self.solver_kwargs if self.solver_kwargs else {}).minimize(
self._obj_func)
x_flat = tf.reshape(x,[-1])
input_features=int(x_flat.get_shape()[0])
W = tf.Variable(tf.random_uniform(shape=[input_features, output_features], name="weight"))
b = tf.Variable(tf.random_uniform(shape=[output_features], name="bias"))
pred = tf.add(tf.multiply(X, W), b)
return pred, W, b
#pred = linear(X, 1)
pred, W, b = linear(X,1, return_params=True)
# Mean squared error
cost = tf.reduce_mean(tf.pow(pred-Y, 2))/(2*n_samples)
# Gradient descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
optimizer = ScipyOptimizerInterface(cost, options={ 'maxiter': 100}, method='BFGS')
# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()
# Start training
with tf.Session() as sess:
sess.run(init)
# Fit all training data
for epoch in range(training_epochs):
for (x, y) in zip(train_X, train_Y):
optimizer.minimize(sess, feed_dict={X: x, Y: y} )
# print(a)
sess.run(cost)
# sess.run(optimizer.minimize(cost), feed_dict={X: x, Y: y})
#Display logs per epoch step
def tsne(X,
perplexity=50,
dim=2,
theta=0.5,
knn_method='knnparallel',
pca_dim=50,
exag=12.,
exag_iter=250,
max_iter=1000,
verbose=False,
print_iter=50,
lr=200.,
init_momentum=0.5,
final_momentum=0.8,
save_snapshots=False,
optimizer='momentum',
tf_optimizer='AdamOptimizer',
seed=42):
X -= X.mean(axis=0)
N = X.shape[0]
result = {}
assert optimizer in (
'momentum', 'tensorflow',
'bfgs'), 'Available options: momentum, tensorflow and bfgs'
if pca_dim is not None:
result['PCA'] = PCA(n_components=pca_dim)
X = result['PCA'].fit_transform(X)
P = x2p(X, perplexity=perplexity, method=knn_method, verbose=verbose)
result['P'] = P
result['exag_iter'] = exag_iter
result['print_iter'] = print_iter
result['loss'] = []
if save_snapshots:
result['snapshots'] = []
tf.reset_default_graph()
tf.set_random_seed(seed)
with tf.Session() as sess:
step = 1
def step_callback(Y_var):
nonlocal step
if step % print_iter == 0:
print('Step: %d, error: %.16f' % (step, result['loss'][-1]))
if save_snapshots:
result['snapshots'].append(Y_var.reshape((N, dim)).copy())
if step == exag_iter:
sess.run(tf.assign(exag_var, 1.))
#zero mean
sess.run(tf.assign(Y, Y - tf.reduce_mean(Y, axis=0)))
step += 1
def loss_callback(err):
result['loss'].append(err)
stddev = 1. if optimizer == 'bfgs' else 0.01
Y = tf.Variable(
tf.random_normal((N, dim), stddev=stddev, dtype=X.dtype))
exag_var = tf.Variable(exag, dtype=P.dtype)
if isinstance(P, sp.sparse.csr_matrix):
loss = tsne_op((P.indptr, P.indices, P.data * exag_var), Y)
else:
loss = tsne_op(P * exag_var, Y)
if optimizer == 'bfgs':
opt = ScipyOptimizerInterface(loss,
var_list=[Y],
method='L-BFGS-B',
options={
'eps': 1.,
'gtol': 0.,
'ftol': 0.,
'disp': False,
'maxiter': max_iter,
'maxls': 100
})
tf.global_variables_initializer().run()
opt.minimize(sess,
fetches=[loss],
loss_callback=loss_callback,
step_callback=step_callback)
Y_final = Y.eval()
else:
zero_mean = tf.assign(Y, Y - tf.reduce_mean(Y, axis=0))
if optimizer == 'tensorflow':
opt = getattr(tf.train, tf_optimizer)(learning_rate=lr)
update = opt.minimize(loss, var_list=[Y])
else:
mom_var = tf.Variable(init_momentum, dtype=X.dtype)
uY = tf.Variable(tf.zeros((N, dim), dtype=X.dtype))
gains = tf.Variable(tf.ones((N, dim), dtype=X.dtype))
dY = tf.gradients(loss, [Y])[0]
gains = tf.assign(
gains,
tf.where(tf.equal(tf.sign(dY), tf.sign(uY)), gains * .8,
gains + .2))
gains = tf.assign(gains, tf.maximum(gains, 0.01))
uY = tf.assign(uY, mom_var * uY - lr * gains * dY)
update = tf.assign_add(Y, uY)
tf.global_variables_initializer().run()
t = time.time()
for i in range(1, max_iter + 1):
if i == exag_iter:
if optimizer == 'momentum':
sess.run(tf.assign(mom_var, final_momentum))
sess.run(tf.assign(exag_var, 1.))
sess.run(update)
sess.run(zero_mean)
if i % print_iter == 0:
kl = loss.eval()
result['loss'].append(kl)
if verbose:
print('Step: %d, error: %f (in %f sec.)' %
(i, kl, (time.time() - t)))
t = time.time()
if save_snapshots:
result['snapshots'].append(Y.eval())
Y_final = Y.eval()
result['Y'] = Y_final
return result
class FMClassifier(TFPicklingBase, ClassifierMixin, BaseEstimator):
"""Factorization machine classifier.
Parameters
----------
rank : int, optional
Rank of the underlying low-rank representation.
batch_size : int, optional
The batch size for learning and prediction. If there are fewer
examples than the batch size during fitting, then the the number of
examples will be used instead.
n_epochs : int, optional
The number of epochs (iterations through the training data) when
fitting. These are counted for the positive training examples, not
the unlabeled data.
random_state: int, RandomState instance or None, optional
If int, the random number generator seed. If RandomState instance,
the random number generator itself. If None, then `np.random` will be
used.
lambda_v : float, optional
L2 regularization strength for the low-rank embedding.
lambda_beta : float, optional
L2 regularization strength for the linear coefficients.
init_scale : float, optional
Standard deviation of random normal initialization.
solver : a subclass of `tf.train.Optimizer` or str, optional
Solver to use. If a string is passed, then the corresponding solver
from `scipy.optimize.minimize` is used.
solver_kwargs : dict, optional
Additional keyword arguments to pass to `solver` upon construction.
See the TensorFlow documentation for possible options. Typically,
one would want to set the `learning_rate`.
Attributes
----------
n_dims_ : int
Number of input dimensions.
classes_ : array
Classes from the data.
n_classes_ : int
Number of classes.
is_sparse_ : bool
Whether a model taking sparse input was fit.
"""
def __init__(self,
rank=8,
batch_size=64,
n_epochs=5,
random_state=None,
lambda_v=0.0,
lambda_beta=0.0,
solver=tf.train.AdadeltaOptimizer,
init_scale=0.1,
solver_kwargs=None):
self.rank = rank
self.batch_size = batch_size
self.n_epochs = n_epochs
self.random_state = random_state
self.lambda_v = lambda_v
self.lambda_beta = lambda_beta
self.solver = solver
self.init_scale = init_scale
self.solver_kwargs = solver_kwargs
def _set_up_graph(self):
"""Initialize TF objects (needed before fitting or restoring)."""
# Input values.
if self.is_sparse_:
self._x_inds = tf.placeholder(tf.int64, [None, 2], "x_inds")
self._x_vals = tf.placeholder(tf.float32, [None], "x_vals")
self._x_shape = tf.placeholder(tf.int64, [2], "x_shape")
self._x = tf.sparse_reorder(
tf.SparseTensor(self._x_inds, self._x_vals, self._x_shape))
x2 = tf.sparse_reorder(
tf.SparseTensor(self._x_inds, self._x_vals * self._x_vals,
self._x_shape))
matmul = tf.sparse_tensor_dense_matmul
else:
self._x = tf.placeholder(tf.float32, [None, self.n_dims_], "x")
x2 = self._x * self._x
matmul = tf.matmul
if self._output_size == 1:
self._y = tf.placeholder(tf.float32, [None], "y")
else:
self._y = tf.placeholder(tf.int32, [None], "y")
with tf.variable_scope("fm"):
self._v = tf.get_variable(
"v", [self.rank, self.n_dims_, self._output_size])
self._beta = tf.get_variable("beta",
[self.n_dims_, self._output_size])
self._beta0 = tf.get_variable("beta0", [self._output_size])
vx = tf.stack(
[matmul(self._x, self._v[i, :, :]) for i in range(self.rank)],
axis=-1)
v2 = self._v * self._v
v2x2 = tf.stack([matmul(x2, v2[i, :, :]) for i in range(self.rank)],
axis=-1)
int_term = 0.5 * tf.reduce_sum(tf.square(vx) - v2x2, axis=-1)
self._logit_y_proba \
= self._beta0 + matmul(self._x, self._beta) + int_term
if self._output_size == 1:
self._logit_y_proba = tf.squeeze(self._logit_y_proba)
self._obj_func = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=self._logit_y_proba, labels=self._y))
self._y_proba = tf.sigmoid(self._logit_y_proba)
else:
self._obj_func = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=self._logit_y_proba, labels=self._y))
self._y_proba = tf.nn.softmax(self._logit_y_proba)
if self.lambda_v > 0:
self._obj_func \
+= self.lambda_v * tf.reduce_sum(tf.square(self._v))
if self.lambda_beta > 0:
self._obj_func \
+= self.lambda_beta * tf.reduce_sum(tf.square(self._beta))
if isinstance(self.solver, str):
from tensorflow.contrib.opt import ScipyOptimizerInterface
self._train_step = ScipyOptimizerInterface(
self._obj_func,
method=self.solver,
options=self.solver_kwargs if self.solver_kwargs else {})
else:
self._train_step = self.solver(
**self.solver_kwargs if self.solver_kwargs else {}).minimize(
self._obj_func)
def _make_feed_dict(self, X, y):
# Make the dictionary mapping tensor placeholders to input data.
if self.is_sparse_:
x_inds = np.vstack(X.nonzero())
x_srt = np.lexsort(x_inds[::-1, :])
x_inds = x_inds[:, x_srt].T.astype(np.int64)
x_vals = np.squeeze(np.array(X[x_inds[:, 0],
x_inds[:, 1]])).astype(np.float32)
x_shape = np.array(X.shape).astype(np.int64)
feed_dict = {
self._x_inds: x_inds,
self._x_vals: x_vals,
self._x_shape: x_shape
}
else:
feed_dict = {self._x: X.astype(np.float32)}
if self._output_size == 1:
feed_dict[self._y] = y.astype(np.float32)
else:
feed_dict[self._y] = y.astype(np.int32)
return feed_dict
def _check_data(self, X):
"""check input data
Raises an error if number of features doesn't match.
If the estimator has not yet been fitted, then do nothing.
"""
if self._is_fitted:
if X.shape[1] != self.n_dims_:
raise ValueError("Number of features in the input data does "
"not match the number assumed by the "
"estimator!")
def __getstate__(self):
# Handles TF persistence
state = super(FMClassifier, self).__getstate__()
# Add attributes of this estimator
state.update(
dict(rank=self.rank,
batch_size=self.batch_size,
n_epochs=self.n_epochs,
random_state=self.random_state,
lambda_v=self.lambda_v,
lambda_beta=self.lambda_beta,
solver=self.solver,
init_scale=self.init_scale,
solver_kwargs=self.solver_kwargs))
# Add fitted attributes if the model has been fitted.
if self._is_fitted:
state['n_dims_'] = self.n_dims_
state['_random_state'] = self._random_state
state['_enc'] = self._enc
state['classes_'] = self.classes_
state['n_classes_'] = self.n_classes_
state['_output_size'] = self._output_size
state['is_sparse_'] = self.is_sparse_
return state
def fit(self, X, y):
"""Fit the classifier.
Parameters
----------
X : numpy array or sparse matrix [n_samples, n_features]
Training data.
y : numpy array [n_samples]
Targets.
Returns
-------
self : returns an instance of self.
"""
_LOGGER.info("Fitting %s", re.sub(r"\s+", r" ", repr(self)))
# Mark the model as not fitted (i.e., not fully initialized based on
# the data).
self._is_fitted = False
# Call partial fit, which will initialize and then train the model.
return self.partial_fit(X, y)
def partial_fit(self, X, y, classes=None, monitor=None):
"""Fit the classifier.
Parameters
----------
X : numpy array or sparse matrix [n_samples, n_features]
Training data.
y : numpy array [n_samples]
Targets.
classes : array, shape (n_classes,)
Classes to be used across calls to partial_fit. If not set in the
first call, it will be inferred from the given targets. If
subsequent calls include additional classes, they will fail.
monitor : callable, optional
The monitor is called after each iteration with the current
iteration, a reference to the estimator, and a dictionary with
{'loss': loss_value} representing the loss calculated by the
objective function at this iteration.
If the callable returns True the fitting procedure is stopped.
The monitor can be used for various things such as computing
held-out estimates, early stopping, model introspection,
and snapshotting.
Returns
-------
self : returns an instance of self.
"""
X, y = check_X_y(X, y, accept_sparse='csr')
# check target type
target_type = type_of_target(y)
if target_type not in ['binary', 'multiclass']:
# Raise an error, as in
# sklearn.utils.multiclass.check_classification_targets.
raise ValueError("Unknown label type: %r" % y)
# Initialize the model if it hasn't been already by a previous call.
if not self._is_fitted:
self._random_state = check_random_state(self.random_state)
assert self.batch_size > 0, "batch_size <= 0"
self.n_dims_ = X.shape[1]
if classes is not None:
self._enc = LabelEncoder().fit(classes)
else:
self._enc = LabelEncoder().fit(y)
self.classes_ = self._enc.classes_
self.n_classes_ = len(self.classes_)
if self.n_classes_ <= 2:
self._output_size = 1
else:
self._output_size = self.n_classes_
if sp.issparse(X):
self.is_sparse_ = True
else:
self.is_sparse_ = False
# Instantiate the graph. TensorFlow seems easier to use by just
# adding to the default graph, and as_default lets you temporarily
# set a graph to be treated as the default graph.
self.graph_ = tf.Graph()
with self.graph_.as_default():
tf.set_random_seed(self._random_state.randint(0, 10000000))
tf.get_variable_scope().set_initializer(
tf.random_normal_initializer(stddev=self.init_scale))
self._build_tf_graph()
# Train model parameters.
self._session.run(tf.global_variables_initializer())
# Set an attributed to mark this as at least partially fitted.
self._is_fitted = True
# Check input data against internal data.
# Raises an error on failure.
self._check_data(X)
# transform targets
if sp.issparse(y):
y = y.toarray()
y = self._enc.transform(y)
# Train the model with the given data.
with self.graph_.as_default():
if not isinstance(self.solver, str):
n_examples = X.shape[0]
indices = np.arange(n_examples)
for epoch in range(self.n_epochs):
self._random_state.shuffle(indices)
for start_idx in range(0, n_examples, self.batch_size):
max_ind = min(start_idx + self.batch_size, n_examples)
batch_ind = indices[start_idx:max_ind]
feed_dict = self._make_feed_dict(
X[batch_ind], y[batch_ind])
obj_val, _ = self._session.run(
[self._obj_func, self._train_step],
feed_dict=feed_dict)
_LOGGER.debug("objective: %.4f, epoch: %d, idx: %d",
obj_val, epoch, start_idx)
_LOGGER.info("objective: %.4f, epoch: %d, idx: %d",
obj_val, epoch, start_idx)
if monitor:
stop_early = monitor(epoch, self, {'loss': obj_val})
if stop_early:
_LOGGER.info(
"stopping early due to monitor function.")
return self
else:
feed_dict = self._make_feed_dict(X, y)
self._train_step.minimize(self._session, feed_dict=feed_dict)
return self
def predict_log_proba(self, X):
"""Compute log p(y=1).
Parameters
----------
X : numpy array or sparse matrix [n_samples, n_features]
Data.
Returns
-------
numpy array [n_samples]
Log probabilities.
"""
if not self._is_fitted:
raise NotFittedError("Call fit before predict_log_proba!")
return np.log(self.predict_proba(X))
def predict_proba(self, X):
"""Compute p(y=1).
Parameters
----------
X : numpy array or sparse matrix [n_samples, n_features]
Data.
Returns
-------
numpy array [n_samples]
Probabilities.
"""
if not self._is_fitted:
raise NotFittedError("Call fit before predict_proba!")
X = check_array(X, accept_sparse='csr')
# Check input data against internal data.
# Raises an error on failure.
self._check_data(X)
# Compute weights in batches.
probs = []
start_idx = 0
n_examples = X.shape[0]
with self.graph_.as_default():
while start_idx < n_examples:
X_batch = \
X[start_idx:min(start_idx + self.batch_size, n_examples)]
feed_dict = self._make_feed_dict(X_batch,
np.zeros(self.n_dims_))
start_idx += self.batch_size
probs.append(
self._y_proba.eval(session=self._session,
feed_dict=feed_dict))
probs = np.concatenate(probs, axis=0)
if probs.ndim == 1:
return np.column_stack([1.0 - probs, probs])
else:
return probs
def predict(self, X):
"""Compute the predicted class.
Parameters
----------
X : numpy array or sparse matrix [n_samples, n_features]
Data.
Returns
-------
numpy array [n_samples]
Predicted class.
"""
if not self._is_fitted:
raise NotFittedError("Call fit before predict!")
return self.classes_[self.predict_proba(X).argmax(axis=1)]
# Advantage function = emperical_return - baseline
A = q_return - q_out
cumulative_trpo_obj = 0
# Choosing particular action "actions" and multiply by A_k
trpo_obj = (tf.gather(tf.squeeze(soft_out), actions) /
tf.gather(tf.squeeze(soft_out_k), actions) * A_k)
cumulative_trpo_obj += trpo_obj
# KL(soft_out_k, soft_out) should be less than KL_delta
constraints = [(-kl(soft_out_k, soft_out) + KL_delta)]
#Use ScipyOptimiztationInterface (SOI) to solve optimization task with constrains
trpo_opt = SOI(-1. / N * cumulative_trpo_obj,
inequalities=constraints,
method='SLSQP',
options={'maxiter': 1}) #it is not enough!
'''
=======================
HyperParams
=======================
'''
num_episodes = 10000
num_steps = 200
trpo_steps = 5
gamma = 0.9
successes = []
success_episodes = []
slice = 10
success_counter = 0
def match(query_full,
d_query,
query_2d_full,
scene,
intr,
gap,
tr_ground,
scale,
thresh_log_conf=7.5,
w_3d=0.01,
fps=3,
step_samples=100):
with_y = False # optimize for y as well
np.set_printoptions(suppress=True, linewidth=220)
pjoin = os.path.join
len_gap = gap[1] - gap[0] + 1
query, q_v = get_partial_scenelet(query_full,
start=gap[0],
end=gap[1] + 1,
fps=1)
q_v_sum = np.sum(q_v)
q_v_sum_inv = np.float32(1. / q_v_sum)
# lg.debug("q_v_sum: %s/%s" % (q_v_sum, q_v.size))
# scene_min_y = scene.skeleton.get_min_y(tr_ground)
# lg.debug("scene_min_y: %s" % repr(scene_min_y))
mid_frames = range(len_gap * fps,
scene.skeleton.poses.shape[0] - len_gap * fps,
step_samples)
if not len(mid_frames):
return []
scenelets, sc_v = (np.array(e) for e in zip(*[
get_partial_scenelet(
scene, mid_frame_id=mid_frame_id, n_frames=len_gap, fps=fps)
for mid_frame_id in mid_frames
]))
# for i, (scenelet, sc_v_) in enumerate(zip(scenelets, sc_v)):
# mn = np.min(scenelet[sc_v_.astype('b1'), 1, :])
# scenelets[i, :, 1, :] -= mn
# mn = np.min(scenelets[i, sc_v_.astype('b1'), 1, :])
# scenelets = np.array(scenelets, dtype=np.float32)
# sc_v = np.array(sc_v, dtype=np.int32)
# print("sc_v: %s" % sc_v)
# print("q_v: %s" % q_v)
lg.debug("have %d/%d 3D poses in scenelet, and %d/%d in query" %
(np.sum(sc_v), sc_v.shape[0], np.sum(q_v), q_v.shape[0]))
query_2d = np.zeros((len_gap, 2, 16), dtype=np.float32)
conf_2d = np.zeros((len_gap, 1, 16), dtype=np.float32)
for lin_id, frame_id in enumerate(range(gap[0], gap[1] + 1)):
if query_2d_full.has_pose(frame_id):
query_2d[lin_id, :, :] = query_2d_full.get_pose(frame_id)[:2, :]
# else:
# lg.warning("Query2d_full does not have pose at %d?" % frame_id)
# im = im_.copy()
if query_2d_full.has_confidence(frame_id):
# print("showing %s" % frame_id)
for joint, conf in query_2d_full._confidence[frame_id].items():
log_conf = abs(np.log(conf)) if conf >= 0. else 0.
# print("conf: %g, log_conf: %g" % (conf, log_conf))
# if log_conf <= thresh_log_conf:
# p2d = scale * query_2d_full.get_joint_3d(joint,
# frame_id=frame_id)
# p2d = (int(round(p2d[0])), int(round(p2d[1])))
# cv2.circle(im, center=p2d,
# radius=int(round(3)),
# color=(1., 1., 1., 0.5), thickness=1)
conf_2d[lin_id, 0, joint] = max(
0., (thresh_log_conf - log_conf) / thresh_log_conf)
# cv2.imshow('im', im)
# cv2.waitKey(100)
# while cv2.waitKey() != 27: pass
conf_2d /= np.max(conf_2d)
# scale from Denis' scale to current image size
query_2d *= scale
# move to normalized camera coordinates
query_2d -= intr[:2, 2:3]
query_2d[:, 0, :] /= intr[0, 0]
query_2d[:, 1, :] /= intr[1, 1]
#
# initialize translation
#
# centroid of query poses
c3d = np.mean(query[q_v.astype('b1'), :, :], axis=(0, 2))
# estimate scenelet centroids
sclt_means = np.array([
np.mean(scenelets[i, sc_v[i, ...].astype('b1'), ...], axis=(0, 2))
for i in range(scenelets.shape[0])
],
dtype=np.float32)
# don't change height
sclt_means[:, 1] = 0
scenelets -= sclt_means[:, None, :, None]
lg.debug("means: %s" % repr(sclt_means.shape))
if with_y:
np_translation = np.array([c3d for i in range(scenelets.shape[0])],
dtype=np.float32)
else:
np_translation = np.array(
[c3d[[0, 2]] for i in range(scenelets.shape[0])], dtype=np.float32)
np_rotation = np.array(
[np.pi * (i % 2) for i in range(scenelets.shape[0])],
dtype=np.float32)[:, None]
n_cands = np_translation.shape[0]
graph = tf.Graph()
with graph.as_default(), tf.device('/gpu:0'):
# 3D translation
translation_ = tf.Variable(initial_value=np_translation,
name='translation',
dtype=tf.float32)
t_y = tf.fill(dims=(n_cands, ),
value=(tr_ground[1, 3]).astype(np.float32))
# t_y = tf.fill(dims=(n_cands,), value=np.float32(0.))
lg.debug("t_y: %s" % t_y)
if with_y:
translation = translation_
else:
translation = tf.concat(
(translation_[:, 0:1], t_y[:, None], translation_[:, 1:2]),
axis=1)
lg.debug("translation: %s" % translation)
# 3D rotation (Euler XYZ)
rotation = tf.Variable(np_rotation, name='rotation', dtype=tf.float32)
# lg.debug("rotation: %s" % rotation)
w = tf.Variable(conf_2d, trainable=False, name='w', dtype=tf.float32)
pos_3d_in = tf.Variable(query,
trainable=False,
name='pos_3d_in',
dtype=tf.float32)
# pos_3d_in = tf.constant(query, name='pos_3d_in', dtype=tf.float32)
pos_2d_in = tf.Variable(query_2d,
trainable=False,
name='pos_2d_in',
dtype=tf.float32)
# pos_2d_in = tf.constant(query_2d, name='pos_2d_in',
# dtype=tf.float32)
pos_3d_sclt = tf.Variable(scenelets,
trainable=False,
name='pos_3d_sclt',
dtype=tf.float32)
# print("pos_3d_sclt: %s" % pos_3d_sclt)
# rotation around y
my_zeros = tf.zeros((n_cands, 1), dtype=tf.float32, name='my_zeros')
# tf.add_to_collection('to_init', my_zeros)
my_ones = tf.ones((n_cands, 1))
# tf.add_to_collection('to_init', my_ones)
c = tf.cos(rotation, 'cos')
# tf.add_to_collection('to_init', c)
s = tf.sin(rotation, 'sin')
# t0 = tf.concat([c, my_zeros, -s], axis=1)
# t1 = tf.concat([my_zeros, my_ones, my_zeros], axis=1)
# t2 = tf.concat([s, my_zeros, c], axis=1)
# transform = tf.stack([t0, t1, t2], axis=2, name="transform")
# print("t: %s" % transform)
transform = tf.concat(
[c, my_zeros, -s, my_zeros, my_ones, my_zeros, s, my_zeros, c],
axis=1)
transform = tf.reshape(transform, ((-1, 3, 3)), name='transform')
print("t2: %s" % transform)
# lg.debug("transform: %s" % transform)
# transform to 3d
# pos_3d = tf.matmul(transform, pos_3d_sclt) \
# + tf.tile(tf.expand_dims(translation, 2),
# [1, 1, int(pos_3d_in.shape[2])])
# pos_3d = tf.einsum("bjk,bcjd->bcjd", transform, pos_3d_sclt)
shp = pos_3d_sclt.get_shape().as_list()
transform_tiled = tf.tile(transform[:, None, :, :, None],
(1, shp[1], 1, 1, shp[3]))
# print("transform_tiled: %s" % transform_tiled)
pos_3d = tf.einsum("abijd,abjd->abid", transform_tiled, pos_3d_sclt)
# print("pos_3d: %s" % pos_3d)
pos_3d += translation[:, None, :, None]
#pos_3d = pos_3d_sclt
# print("pos_3d: %s" % pos_3d)
# perspective divide
# pos_2d = tf.divide(
# tf.slice(pos_3d, [0, 0, 0], [n_cands, 2, -1]),
# tf.slice(pos_3d, [0, 2, 0], [n_cands, 1, -1]))
pos_2d = tf.divide(pos_3d[:, :, :2, :], pos_3d[:, :, 2:3, :])
# print("pos_2d: %s" % pos_2d)
diff = pos_2d - pos_2d_in
# mask loss by 2d key-point visibility
# print("w: %s" % w)
# w_sum = tf.reduce_sum()
masked = tf.multiply(diff, w)
# print(masked)
# loss_reproj = tf.nn.l2_loss(masked)
# loss_reproj = tf.reduce_sum(tf.square(masked[:, :, 0, :])
# + tf.square(masked[:, :, 1, :]),
# axis=[1, 2])
masked_sqr = tf.square(masked[:, :, 0, :]) \
+ tf.square(masked[:, :, 1, :])
loss_reproj = tf.reduce_sum(masked_sqr, axis=[1, 2])
# lg.debug("loss_reproj: %s" % loss_reproj)
# distance from existing 3D skeletons
d_3d = q_v_sum_inv * tf.multiply(pos_3d - query[None, ...],
q_v[None, :, None, None],
name='diff_3d')
# print(d_3d)
loss_3d = w_3d * tf.reduce_sum(tf.square(d_3d[:, :, 0, :]) + tf.square(
d_3d[:, :, 1, :]) + tf.square(d_3d[:, :, 2, :]),
axis=[1, 2],
name='loss_3d_each')
# print(loss_3d)
loss = tf.reduce_sum(loss_reproj) + tf.reduce_sum(loss_3d)
# optimize
optimizer = ScipyOptimizerInterface(loss,
var_list=[translation_, rotation],
options={'gtol': 1e-12})
with Timer('solve', verbose=True) as t:
with tf.Session(graph=graph) as session:
session.run(tf.global_variables_initializer())
optimizer.minimize(session)
o_pos_3d, o_pos_2d, o_masked, o_t, o_r, o_w, o_d_3d, \
o_loss_reproj, o_loss_3d, o_transform, o_translation = \
session.run([
pos_3d, pos_2d, masked, translation, rotation, w,
d_3d, loss_reproj, loss_3d, transform, translation])
o_masked_sqr = session.run(masked_sqr)
# o_t, o_r = session.run([translation, rotation])
# print("pos_3d: %s" % o_pos_3d)
# print("pos_2d: %s" % o_pos_2d)
# print("o_loss_reproj: %s, o_loss_3d: %s" % (o_loss_reproj, o_loss_3d))
# print("t: %s" % o_t)
# print("r: %s" % o_r)
chosen = sorted((i for i in range(o_loss_reproj.shape[0])),
key=lambda i2: o_loss_reproj[i2] + o_loss_3d[i2])
lg.info("Best candidate is %d with error %g + %g" %
(chosen[0], o_loss_reproj[chosen[0]], o_loss_3d[chosen[0]]))
# print("masked: %s" % o_masked)
# opp = np.zeros_like(o_pos_3d)
# for i in range(o_pos_3d.shape[0]):
# for j in range(o_pos_3d.shape[1]):
# for k in range(16):
# opp[i, j, :2, k] = o_pos_3d[i, j, :2, k] / o_pos_3d[i, j, 2:3, k]
# # opp[i, j, 0, k] *= intr[0, 0]
# # opp[i, j, 1, k] *= intr[1, 1]
# # opp[i, j, :2, k] *= intr[1, 1]
# a = o_pos_2d[i, j, :, k]
# b = opp[i, j, :2, k]
# if not np.allclose(a, b):
# print("diff: %s, %s" % (a, b))
o_pos_2d[:, :, 0, :] *= intr[0, 0]
o_pos_2d[:, :, 1, :] *= intr[1, 1]
o_pos_2d += intr[:2, 2:3]
# for cand_id in range(o_pos_2d.shape[0]):
if False:
# return
# print("w: %s" % o_w)
# print("conf_2d: %s" % conf_2d)
# lg.debug("query_2d[0, 0, ...]: %s" % query_2d[0, 0, ...])
query_2d[:, 0, :] *= intr[0, 0]
query_2d[:, 1, :] *= intr[1, 1]
# lg.debug("query_2d[0, 0, ...]: %s" % query_2d[0, 0, ...])
query_2d += intr[:2, 2:3]
# lg.debug("query_2d[0, 0, ...]: %s" % query_2d[0, 0, ...])
ims = {}
for cand_id in chosen[:5]:
lg.debug("starting %s" % cand_id)
pos_ = o_pos_2d[cand_id, ...]
for lin_id in range(pos_.shape[0]):
frame_id = gap[0] + lin_id
try:
im = ims[frame_id].copy()
except KeyError:
p_im = pjoin(d_query, 'origjpg',
"color_%05d.jpg" % frame_id)
ims[frame_id] = cv2.imread(p_im)
im = ims[frame_id].copy()
# im = im_.copy()
for jid in range(pos_.shape[-1]):
xy2 = int(round(query_2d[lin_id, 0, jid])), \
int(round(query_2d[lin_id, 1, jid]))
# print("printing %s" % repr(xy))
cv2.circle(im,
center=xy2,
radius=5,
color=(10., 200., 10.),
thickness=-1)
if o_masked[cand_id, lin_id, 0, jid] > 0 \
or o_w[lin_id, 0, jid] > 0:
xy = int(round(pos_[lin_id, 0, jid])), \
int(round(pos_[lin_id, 1, jid]))
# print("printing %s" % repr(xy))
cv2.circle(im,
center=xy,
radius=3,
color=(200., 10., 10.),
thickness=-1)
cv2.putText(im,
"d2d: %g" %
o_masked_sqr[cand_id, lin_id, jid],
org=((xy2[0] - xy[0]) // 2 + xy[0],
(xy2[1] - xy[1]) // 2 + xy[1]),
fontFace=1,
fontScale=1,
color=(0., 0., 0.))
cv2.line(im, xy, xy2, color=(0., 0., 0.))
d3d = o_d_3d[cand_id, lin_id, :, jid]
d3d_norm = np.linalg.norm(d3d)
if d3d_norm > 0.:
cv2.putText(
im,
"%g" % d3d_norm,
org=((xy2[0] - xy[0]) // 2 + xy[0] + 10,
(xy2[1] - xy[1]) // 2 + xy[1]),
fontFace=1,
fontScale=1,
color=(0., 0., 255.))
cv2.putText(im,
text="%d::%02d" % (cand_id, lin_id),
org=(40, 80),
fontFace=1,
fontScale=2,
color=(255., 255., 255.))
# pos_2d_ = np.matmul(intr, pos_[lin_id, :2, :] / pos_[lin_id, 2:3, :])
# for p2d in pos_2d_
cv2.imshow('im', im)
cv2.waitKey()
break
while cv2.waitKey() != 27:
pass
out_scenelets = []
for cand_id in chosen[:1]:
lg.debug("score of %d is %g + %g = %g" %
(cand_id, o_loss_reproj[cand_id], o_loss_3d[cand_id],
o_loss_reproj[cand_id] + o_loss_3d[cand_id]))
scenelet = Scenelet()
rate = query_full.skeleton.get_rate()
prev_time = None
for lin_id, frame_id in enumerate(range(gap[0], gap[1] + 1)):
time_ = query_full.get_time(frame_id)
if lin_id and rate is None:
rate = time_ - prev_time
if time_ == frame_id:
time_ = prev_time + rate
scenelet.skeleton.set_pose(frame_id=frame_id,
pose=o_pos_3d[cand_id, lin_id, :, :],
time=time_)
prev_time = time_
tr = np.concatenate((np.concatenate(
(o_transform[cand_id, ...], o_translation[cand_id, None, :].T),
axis=1), [[0., 0., 0., 1.]]),
axis=0)
tr_m = np.concatenate(
(np.concatenate((np.identity(3), -sclt_means[cand_id, None, :].T),
axis=1), [[0., 0., 0., 1.]]),
axis=0)
tr = np.matmul(tr, tr_m)
for oid, ob in scene.objects.items():
if ob.label in ('wall', 'floor'):
continue
ob2 = copy.deepcopy(ob)
ob2.apply_transform(tr)
scenelet.add_object(obj_id=oid, scene_obj=ob2, clone=False)
scenelet.name_scene = scene.name_scene
out_scenelets.append((o_loss_reproj[cand_id], scenelet))
return out_scenelets
def reconmodel(config,
data,
sigma=0.01**0.5,
maxiter=100,
gtol=1e-5,
anneal=True):
bs, nc = config['boxsize'], config['nc']
kmesh = sum(kk**2 for kk in config['kvec'])**0.5
priorwt = config['ipklin'](kmesh) * bs**-3
g = tf.Graph()
with g.as_default():
module = hub.Module(modpath)
initlin = tf.placeholder(tf.float32, (nc, nc, nc), name='initlin')
linear = tf.get_variable('linmesh',
shape=(nc, nc, nc),
initializer=tf.random_normal_initializer(
mean=1.0, stddev=0.5),
trainable=True)
initlin_op = linear.assign(initlin, name='initlin_op')
#PM
icstate = tfpm.lptinit(linear, config, name='icstate')
fnstate = tfpm.nbody(icstate, config, verbose=False, name='fnstate')
final = tf.zeros_like(linear)
final = tfpf.cic_paint(final, fnstate[0], boxsize=bs, name='final')
#
#xx = tf.reshape(final, shape=[-1, cube_sizeft, cube_sizeft, cube_sizeft, nchannels], name='input')
xx = tf.concat((final[-pad:, :, :], final, final[:pad, :, :]), axis=0)
xx = tf.concat((xx[:, -pad:, :], xx, xx[:, :pad, :]), axis=1)
xx = tf.concat((xx[:, :, -pad:], xx, xx[:, :, :pad]), axis=2)
xx = tf.expand_dims(tf.expand_dims(xx, 0), -1)
#Halos
#yy = tf.reshape(data, shape=[-1, cube_size, cube_size, cube_size, 1], name='labels')
yy = tf.expand_dims(data, 0)
print('xx, yy shape :', xx.shape, yy.shape)
likelihood = module(dict(features=tf.cast(xx, tf.float32),
labels=tf.cast(yy, tf.float32)),
as_dict=True)['loglikelihood']
print(likelihood.shape)
##Anneal
Rsm = tf.placeholder(tf.float32, name='smoothing')
if anneal:
Rsm = tf.multiply(Rsm, bs / nc)
Rsmsq = tf.multiply(Rsm, Rsm)
smwts = tf.exp(tf.multiply(-kmesh**2, Rsmsq))
likelihood = tf.squeeze(likelihood)
print(likelihood.shape)
likelihoodk = tfpf.r2c3d(likelihood, norm=nc**3)
likelihoodk = tf.multiply(likelihoodk,
tf.cast(smwts, tf.complex64))
likelihood = tfpf.c2r3d(likelihoodk, norm=nc**3)
residual = -tf.reduce_sum(likelihood)
#Prior
lineark = tfpf.r2c3d(linear, norm=nc**3)
priormesh = tf.square(tf.cast(tf.abs(lineark), tf.float32))
prior = tf.reduce_sum(tf.multiply(priormesh, 1 / priorwt))
prior = tf.multiply(prior, 1 / nc**3, name='prior')
chisq = tf.multiply(residual, 1 / nc**0, name='chisq')
loss = tf.add(chisq, prior, name='loss')
optimizer = ScipyOptimizerInterface(loss,
var_list=[linear],
method='L-BFGS-B',
options={
'maxiter': maxiter,
'gtol': gtol
})
tf.add_to_collection('inits', [initlin_op, initlin])
tf.add_to_collection('opt', optimizer)
tf.add_to_collection('diagnostics', [prior, chisq, loss])
tf.add_to_collection('reconpm', [linear, final, fnstate])
tf.add_to_collection('data', data)
return g
def __init__(self):
self.__OptimizerWrap = \
lambda loss, max_steps: ScipyOptimizerInterface(loss,
options={'maxiter': max_steps,
'disp': 50})
def run(self):
content = self._load_image(self._content_image)
h, w = content.shape[1], content.shape[2]
style = self._load_image(self._style_image, size=(h, w))
print("Content shape: ", content.shape)
print("Style shape: ", style.shape)
image = tf.Variable(style,
dtype=tf.float32,
validate_shape=False,
name='image')
self._output_shape = content.shape
self._build_vgg19(image)
self._add_gramians()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
# Calculate loss function
sess.run(tf.global_variables_initializer())
with tf.name_scope('losses'):
style_losses = self._setup_style_losses(sess, image, style)
content_losses = self._setup_content_losses(
sess, image, content)
losses = content_losses + style_losses
if self._hist_weight > 0:
with tf.name_scope('histogram'), tf.device('/cpu:0'):
hist_loss = self._setup_histogram_loss(
image, style, sess)
losses += hist_loss
image.set_shape(
content.shape) # tv loss expects explicit shape
if self._tv_weight:
tv_loss = tf.image.total_variation(image[0])
tv_loss_weighted = tf.multiply(tv_loss,
self._tv_weight,
name='tv_loss')
losses += tv_loss_weighted
loss = tf.foldl(add, losses, name='loss')
# Set optimizator
if self._optimizer == 'Adam':
opt = tf.train.AdamOptimizer(10).minimize(loss)
sess.run(tf.global_variables_initializer())
self._set_initial_image(sess, image, content)
self._step_callback(sess.run(image))
for it in range(self._num_iterations):
_, ll, out = sess.run([opt, loss, image])
self._step_callback(out)
print("Iteration: {:3d}\tLoss = {:.6f}".format(it, ll))
elif self._optimizer == 'L-BFGS':
sess.run(tf.global_variables_initializer())
self._set_initial_image(sess, image, content)
self._step_callback(sess.run(image))
opt = ScipyOptimizerInterface(loss,
options={
'maxiter':
self._num_iterations,
'disp': self._print_iter
},
method='L-BFGS-B')
opt.minimize(sess, step_callback=self._step_callback)
else:
raise ValueError("Unknown optimization method")
self._save_image(self._output_image, sess.run(image))
# In[75]:
tf.reset_default_graph()
X = tf.placeholder("float")
Y = tf.placeholder("float")
M = tf.get_variable("beta", [n, 1], "float", initializer=tf.zeros_initializer)
y_pred = tf.matmul(X, M )
loss = tf.reduce_sum(tf.pow(Y-y_pred, 2))
optim = ScipyOptimizerInterface(loss, [M])
# In[76]:
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
optim.minimize(sess, feed_dict={X:x, Y:y[:,None]})
M_st = sess.run(M)
# ans = sess.run(loss, feed_dict={X: x, Y: y})
# In[77]:
print(M_st.T)
class TRPO():
def __init__(self, env, gamma=1, lr=0.01, num_episodes=1000, num_steps=200, KL_delta=10 ** (-4)):
#self.env = ArmEnv(size_x=4, size_y=3, cubes_cnt=4, episode_max_length=2000, finish_reward=200,
# action_minus_reward=0.0, tower_target_size=3)
self.env = env
self.gamma = gamma
self.lr = lr
self.num_episodes = num_episodes
self.num_steps = num_steps
self.KL_delta = KL_delta
self.success_counter = 0
self.build_graph()
self.obs_len = len(self.env.reset())
self.trajectory = []
self.total_rew = []
self.disc = 0
self.log_file = open('logs_' + str(time.time()) + '.txt', 'w+')
self.sess = tf.Session()
def build_graph(self):
tf.reset_default_graph()
self.state = tf.placeholder('float32', shape=[None, len(self.env.reset())], name="STATE")
self.actions = tf.squeeze(tf.placeholder('int32', name="ACTIONS"))
self.q_estimation = tf.placeholder('float32', name="Q-EST")
self.build_actor()
self.build_critic()
self.build_trpo_tf()
#self.build_trpo_SOI()
'''
============================
Actor = Policy Approxiamtion
============================
'''
def build_actor(self):
self.inp = tf.layers.dense(
self.state,
10,
name="ACTOR_INPUT",
kernel_initializer=tf.random_normal_initializer(mean=0, stddev=0.1),
bias_initializer=tf.initializers.constant(0)
)
self.out = tf.layers.dense(
self.inp,
self.env.action_space.n,
kernel_initializer=tf.random_normal_initializer(mean=0, stddev=0.1),
bias_initializer=tf.initializers.constant(0)
)
self.soft_out = tf.nn.softmax(self.out)
nl = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.out, labels=self.actions)
wnl = tf.multiply(nl, self.q_estimation)
self.loss = tf.reduce_mean(wnl)
self.opt = tf.train.AdamOptimizer(learning_rate=0.001).minimize(self.loss)
'''
======================================
Critic = Approximation of Q-function
======================================
'''
def build_critic(self):
self.q_return = tf.placeholder('float32', name="Q-Return") # sum of rewards on rest of traj
self.q_inp = tf.layers.dense(
tf.concat(self.state, self.actions),
10,
name="Q-input",
kernel_initializer=tf.random_normal_initializer(mean=0, stddev=0.1),
bias_initializer=tf.initializers.constant(0)
)
self.q_out = tf.layers.dense(
self.q_inp,
1,
name="Q-output",
kernel_initializer=tf.random_normal_initializer(mean=0, stddev=0.1),
bias_initializer=tf.initializers.constant(0)
)
self.q_loss = tf.losses.mean_squared_error(self.q_out, self.q_return)
self.q_opt = tf.train.AdamOptimizer(0.01).minimize(self.q_loss)
def build_trpo_SOI(self):
# I use index _k to fix variables in graph
# Fixed probs on k-th iteration
self.soft_out_k = tf.placeholder('float32', name="SOFTOUT_K")
# Fixed advantage on k-th iteration
self.A_k = tf.placeholder('float32', name="A_K")
# Number of steps to estimate expectation
self.N = tf.placeholder('float32', name="number")
# Advantage function = emperical_return - baseline
self.A = self.q_return - self.q_out
# Choosing particular action "actions" and multiply by A_k
#here was a mistake -> A instead of A_k
trpo_obj = -tf.reduce_mean(self.A_k * tf.gather(tf.exp(self.soft_out - self.soft_out_k), self.actions))
# KL(soft_out_k, soft_out) should be less than KL_delta
constraints = [(-self.kl(self.soft_out_k, self.soft_out) + self.KL_delta)]
# Use ScipyOptimiztationInterface (SOI) to solve optimization task with constrains
self.trpo_opt = SOI(trpo_obj,
method='SLSQP',
inequalities=constraints,
options={'maxiter': 3})
def apply_trpo_SOI(self, s, a, q_app, soft, r, adv):
#Use trajectory s -> a -> r to optimize policy in Trust-Region interval
feed_dict = [[self.state, [s]],
[self.soft_out_k, [soft]],
[self.actions, [a]],
[self.q_return, [r]],
[self.q_out, q_app],
[self.A_k, adv]]
self.trpo_opt.minimize(self.sess, feed_dict=feed_dict)
def build_trpo_tf(self):
self.beta = tf.placeholder('float32')
self.eta = tf.placeholder('float32')
self.learn_rate = tf.placeholder('float32')
self.learn_rate_value = 0.001
self.soft_out_k = tf.placeholder('float32', name="SOFTOUT_K")
# Fixed advantage on k-th iteration
self.A_k = tf.placeholder('float32', name="A_K")
self.A = self.q_return - self.q_out #?
self.D_KL = self.kl(self.soft_out, self.soft_out_k)
trpo_loss_1 = -tf.reduce_mean(self.A_k * tf.exp(self.soft_out - self.soft_out_k))
trpo_loss_2 = self.beta * self.D_KL
trpo_loss_3 = self.eta * tf.square(tf.maximum(0.0, self.KL_delta - 2 * self.D_KL))
trpo_total_loss = trpo_loss_1 + trpo_loss_2 + trpo_loss_3
self.trpo_opt = tf.train.AdamOptimizer(self.learn_rate).minimize(trpo_total_loss)
def apply_trpo_tf(self, old_policy, advantage, state, actions, num_steps):
beta = 0.5
eta = 0.5
DKL = 0.01
for i in range(num_steps):
if DKL > 2 * self.KL_delta:
beta *= 1.5
if beta > 30:
self.learn_rate_value /= 1.5
elif DKL < 0.5 * self.KL_delta:
beta /= 1.05
if beta < 1./30:
self.learn_rate_value *= 1.5
_, DKL = self.sess.run([self.trpo_opt, self.D_KL], feed_dict={self.A_k: advantage,
self.soft_out_k: old_policy,
self.actions: actions,
self.state: [state],
self.beta: beta,
self.eta: eta,
self.learn_rate: self.learn_rate_value})
def roll_trajectory(self, episode):
s = self.env.reset()
self.trajectory = []
self.total_rew = []
for step in range(self.num_steps):
output = self.sess.run([self.soft_out], feed_dict={self.state: [s]})
probs = output[0][0]
if not self.learn_flag:
a = np.random.choice(self.env.action_space.n,
p=[1. / self.env.action_space.n for _ in range(self.env.action_space.n)])
#print("probs: ", probs, self.learn_flag, "action: ", a)
self.log_file.write('probs: ' + str(probs) + '\n')
else:
a = np.random.choice(self.env.action_space.n, p=probs)
#print("probs: ", probs, self.learn_flag, "action: ", a)
self.log_file.write('probs: ' + str(probs) + '\n')
new_state, reward, done, _ = self.env.step(a)
self.total_rew.append(reward)
self.trajectory.append((s, a, reward))
if done:
if reward != 0:
self.env.render()
self.learn_flag = True
print(reward)
self.success_counter += 1
return
s = new_state
print('====================== end of episode {} ======================'.format(episode))
def learn(self):
self.learn_flag = False
with self.sess:
self.sess.run(tf.global_variables_initializer())
# Calculate metrics
self.successes = []
self.success_episodes = []
self.slice = 10
self.success_counter = 0
for episode in range(self.num_episodes):
self.roll_trajectory(episode)
disc = self.discount_and_norm_rewards(self.total_rew, self.gamma)
for n, st in enumerate(self.trajectory):
traj_state = st[0]
traj_action = int(st[1])
traj_reward = disc[n]
#Learning Critic
q_approximated, _ = self.sess.run([self.q_out, self.q_opt],
feed_dict={self.state: [traj_state],
self.actions: [traj_action],
self.q_return: traj_reward}
)
#Learning Actor
_, soft, adv = self.sess.run([self.opt, self.soft_out, self.A],
feed_dict={self.state: [traj_state],
self.actions: [traj_action],
self.q_estimation: q_approximated,
self.q_return: traj_reward}
)
#Optimization Actor-Parameters
#self.apply_trpo_SOI(traj_state, traj_action, q_approximated, soft, traj_reward, adv)
if episode > 500:
self.apply_trpo_tf(soft, adv, traj_state, traj_action, 20)
if episode % self.slice == 0:
print("Episode: ", episode)
print("Successes to all: ", self.success_counter / self.slice)
self.success_episodes.append(episode)
self.successes.append(self.success_counter / self.slice)
self.success_counter = 0
plt.plot(self.success_episodes, self.successes)
plt.show()
self.log_file.close()
print(self.successes)
@staticmethod
def kl(p, q):
return tf.reduce_sum(tf.multiply(p, tf.log(p / q)))
@staticmethod
def kl_num(p, q):
return np.sum(np.multiply(p, np.log(p/q)))
@staticmethod
def to_cat(a, n):
return np.array([1 if a == i else 0 for i in range(n)])
@staticmethod
def discount_and_norm_rewards(episode_rewards, gamma):
discounted_episode_rewards = np.zeros_like(episode_rewards)
cumulative = 0
for t in reversed(range(len(episode_rewards))):
cumulative = cumulative * gamma + episode_rewards[t]
discounted_episode_rewards[t] = cumulative
return discounted_episode_rewards
