我是新手使用tensorflow。我想构造一个具有以下属性的bijector：它采用维度概率分布p（x1，x2，...，xn），它只转换两个特定维度i和j，这样xi'= xi，xj '= xj * exp（s（xi））+ t（xj），其中s和t是使用神经网络实现的两个函数。它输出p（x1，x2，...，xi'，..，xj'，..，xn）。 我的基本代码如下所示：
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)
但它没有像我想的那样奏效。当我使用该类时，会弹出一个错误：
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)
有错误：
---------------------------------------------------------------------------
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
真的希望有些专家可以帮助我理解我做错的地方以及如何解决这个问题。非常感谢！ 小时。
答案 0 :(得分：0)
我认为问题出在这里（为了清晰起见，稍微重新格式化）：
output_tensor = tf.concat([
x[:,0:self.idx1],
x_left,
x[:, self.idx1+1:self.idx2],
y_right,
x[:, (self.idx2+1):]
], axis = 1)
这假定idx2 > idx1在您提供idx1=1和idx2=0的情况下不是这样。这使得你得到的东西多于你想要的第二个暗淡的4而不是2。
我在_forward中打印了形状：
print("self.idx1: %s" % self.idx1)
print("self.idx2: %s" % self.idx2)
print("x[:,0:self.idx1]: %s" % x[:,0:self.idx1].shape)
print("x_left: %s" % x_left.shape)
print("x[:, self.idx1+1:self.idx2]: %s" %
x[:, self.idx1+1:self.idx2].shape)
print("x_right.shape: %s" % x_right.shape)
print("y_right: %s" % y_right.shape)
print("x[:, (self.idx2+1):]: %s" % x[:, (self.idx2+1):].shape)
print("output_tensor.shape: %s" % output_tensor.shape)
得到了这个输出：
self.idx1: 0
self.idx2: 1
x[:,0:self.idx1]: (1000, 0)
x_left: (1000, 1)
x[:, self.idx1+1:self.idx2]: (1000, 0)
x_right.shape: (1000, 1)
y_right: (1000, 1)
x[:, (self.idx2+1):]: (1000, 0)
output_tensor.shape: (1000, 2)
self.idx1: 1
self.idx2: 0
x[:,0:self.idx1]: (1000, 1)
x_left: (1000, 1)
x[:, self.idx1+1:self.idx2]: (1000, 0)
x_right.shape: (1000, 1)
y_right: (1000, 1)
x[:, (self.idx2+1):]: (1000, 1)
output_tensor.shape: (1000, 4)
self.idx1: 0
self.idx2: 1
x[:,0:self.idx1]: (1000, 0)
x_left: (1000, 1)
x[:, self.idx1+1:self.idx2]: (1000, 0)
x_right.shape: (1000, 1)
y_right: (1000, 1)
x[:, (self.idx2+1):]: (1000, 2)
output_tensor.shape: (1000, 4)
self.idx1: 0
self.idx2: 1
x[:,0:self.idx1]: (1000, 0)
x_left: (1000, 1)
x[:, self.idx1+1:self.idx2]: (1000, 0)
x_right.shape: (1000, 1)
y_right: (1000, 1)
x[:, (self.idx2+1):]: (1000, 2)
output_tensor.shape: (1000, 4)
我认为你需要更仔细地考虑重新组装这个块中的连续部分，当idx1＆gt; IDX2。
希望这能让你回到正轨！