Я новичок в использовании tenorflow.И я хочу построить биектор со следующими свойствами: он принимает размерное распределение вероятностей p (x1, x2, ..., xn) и преобразует только два определенных измерения i и j, так что xi '= xi, xj'= xj * exp (s (xi)) + t (xj), где s и t - две функции, реализованные с использованием нейронных сетей.Он выводит p (x1, x2, ..., xi ', .., xj', .., xn).У меня есть базовый код выглядит следующим образом:
def net(x, out_size, block_w_id, block_d_id, layer_id):
x = tf.contrib.layers.fully_connected(x, 256, reuse=tf.AUTO_REUSE, scope='x1_block_w_{}_block_d_{}_layer_{}'.format(block_w_id, \
block_d_id,\
layer_id))
x = tf.contrib.layers.fully_connected(x, 256, reuse=tf.AUTO_REUSE, scope='x2_block_w_{}_block_d_{}_layer_{}'.format(block_w_id,\
block_d_id,\
layer_id))
y = tf.contrib.layers.fully_connected(x, out_size, reuse=tf.AUTO_REUSE, scope='y_block_w_{}_block_d_{}_layer_{}'.format(block_w_id,\
block_d_id,\
layer_id))
# return layers.stack(x, layers.fully_connected(reuse=tf.AUTO_REUSE), [512, 512, out_size])
return y
class NVPCoupling(tfb.Bijector):
"""NVP affine coupling layer for 2D units.
"""
def __init__(self, input_idx1, input_idx2, block_w_id = 0, block_d_id = 0, layer_id = 0, validate_args = False\
, name="NVPCoupling"):
"""
NVPCoupling only manipulate two inputs with idx1 & idx2.
"""
super(NVPCoupling, self).__init__(\
event_ndims = 1, validate_args = validate_args, name = name)
self.idx1 = input_idx1
self.idx2 = input_idx2
self.block_w_id = block_w_id
self.block_d_id = block_d_id
self.layer_id = layer_id
# create variables
tmp = tf.placeholder(dtype=DTYPE, shape = [1, 1])
self.s(tmp)
self.t(tmp)
def s(self, xd):
with tf.variable_scope('s_block_w_id_{}_block_d_id_{}_layer_{}'.format(self.block_w_id,\
self.block_d_id,\
self.layer_id),\
reuse = tf.AUTO_REUSE):
return net(xd, 1, self.block_w_id, self.block_d_id, self.layer_id)
def t(self, xd):
with tf.variable_scope('t_block_w_id_{}_block_d_id_{}_layer_{}'.format(self.block_w_id,\
self.block_d_id,\
self.layer_id),\
reuse = tf.AUTO_REUSE):
return net(xd, 1, self.block_w_id, self.block_d_id, self.layer_id)
def _forward(self, x):
x_left, x_right = x[:, self.idx1:(self.idx1 + 1)], x[:, self.idx2:(self.idx2 + 1)]
y_right = x_right * tf.exp(self.s(x_left)) + self.t(x_left)
output_tensor = tf.concat([ x[:,0:self.idx1], x_left, x[:, self.idx1+1:self.idx2]\
, y_right, x[:, (self.idx2+1):]], axis = 1)
return output_tensor
def _inverse(self, y):
y_left, y_right = y[:, self.idx1:(self.idx1 + 1)], y[:, self.idx2:(self.idx2 + 1)]
x_right = (y_right - self.t(y_left)) * tf.exp(-self.s(y_left))
output_tensor = tf.concat([ y[:, 0:self.idx1], y_left, y[:, self.idx1+1 : self.idx2]\
, x_right, y[:, (self.idx2+1):]], axis = 1)
return output_tensor
def _forward_log_det_jacobian(self, x):
event_dims = self._event_dims_tensor(x)
x_left = x[:, self.idx1:(self.idx1+1)]
return tf.reduce_sum(self.s(x_left), axis=event_dims)
Но это не сработало, как мне кажется.Когда я использую класс, появляется сообщение:
base_dist = tfd.MultivariateNormalDiag(loc=tf.zeros([2], DTYPE))
num_bijectors = 4
bijectors = []
bijectors.append(NVPCoupling(input_idx1=0, input_idx2=1, \
block_w_id=0, block_d_id=0, layer_id=0))
bijectors.append(NVPCoupling(input_idx1=1, input_idx2=0, \
block_w_id=0, block_d_id=0, layer_id=1))
bijectors.append(NVPCoupling(input_idx1=0, input_idx2=1, \
block_w_id=0, block_d_id=0, layer_id=2))
bijectors.append(NVPCoupling(input_idx1=0, input_idx2=1, \
block_w_id=0, block_d_id=0, layer_id=3))
flow_bijector = tfb.Chain(list(reversed(bijectors)))
dist = tfd.TransformedDistribution(
distribution=base_dist,
bijector=flow_bijector)
dist.sample(1000)
с ошибкой:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-16-04da05d30f8d> in <module>()
----> 1 dist.sample(1000)
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/tensorflow/python/ops/distributions/distribution.pyc in sample(self, sample_shape, seed, name)
708 samples: a `Tensor` with prepended dimensions `sample_shape`.
709 """
--> 710 return self._call_sample_n(sample_shape, seed, name)
711
712 def _log_prob(self, value):
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/tensorflow/python/ops/distributions/transformed_distribution.pyc in _call_sample_n(self, sample_shape, seed, name, **kwargs)
412 # returned result.
413 y = self.bijector.forward(x, **kwargs)
--> 414 y = self._set_sample_static_shape(y, sample_shape)
415
416 return y
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/tensorflow/python/ops/distributions/distribution.pyc in _set_sample_static_shape(self, x, sample_shape)
1220 shape = tensor_shape.TensorShape(
1221 [None]*(ndims - event_ndims)).concatenate(self.event_shape)
-> 1222 x.set_shape(x.get_shape().merge_with(shape))
1223
1224 # Infer batch shape.
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/tensorflow/python/framework/tensor_shape.pyc in merge_with(self, other)
671 return TensorShape(new_dims)
672 except ValueError:
--> 673 raise ValueError("Shapes %s and %s are not compatible" % (self, other))
674
675 def concatenate(self, other):
ValueError: Shapes (1000, 4) and (?, 2) are not compatible
Действительно надеюсь, что некоторые эксперты могут помочь мне понять, где я это сделалнеправильно и как это исправить.Большое спасибо!H.