我有一个在图像上运行的VAE。如果我使用交叉熵，L1或L2损失，那么一切都会正常进行。如果我使用MS-SSIM丢失，则在<= 128px的图像上可以正常工作，但是在> 128px的图像上得到NaN（经过几次迭代，通常在5K迭代之前）。使用L1，L2或CE丢失的任何大小的图像都不会出现问题，使用<= 128px的图像时，MS-SSIM也不会出现问题。
我的问题有两个：
详细信息：
丢失函数的构造如下：
if loss_fn == 0: self.gen_loss = tf.reduce_mean ( msa.tf.mmath.ce_loss(msa.tf.mmath.lmap(self.x, input_range, (0,1)), msa.tf.mmath.lmap(self.y, output_range, (0,1))) )# / data_size
elif loss_fn == 1: self.gen_loss = self.l1_loss
elif loss_fn == 2: self.gen_loss = self.l2_loss
elif loss_fn == 3: self.gen_loss = 1.0 - tf.reduce_mean( msa.tf.ssim.ms_ssim(self.x, self.y) ) # see end of post for ms_ssim
self.kl_loss = tf.reduce_mean ( msa.tf.mmath.kl_loss(self.z_mu, self.z_log_sigma_sq) )
self.loss = kl_weight * self.kl_loss + self.gen_loss
就像我说的那样，问题仅在loss_fn == 3 时才会发生。 完整的错误在下面，但是我知道它是gen_loss，因为我可以看到它毫无预警地变成了nan_loss，有时甚至很早（在这里，是第9次迭代）：
i:1 e:0.006 | gen_loss:0.9598 kl_loss:0.0000 l1_loss:0.5096 l2_loss:0.3638 loss:0.9598
i:2 e:0.011 | gen_loss:0.9582 kl_loss:0.0000 l1_loss:0.5017 l2_loss:0.3555 loss:0.9582
i:3 e:0.017 | gen_loss:0.9633 kl_loss:0.0000 l1_loss:0.5131 l2_loss:0.3693 loss:0.9633
i:4 e:0.022 | gen_loss:0.9583 kl_loss:0.0000 l1_loss:0.4945 l2_loss:0.3424 loss:0.9583
i:5 e:0.028 | gen_loss:0.9490 kl_loss:0.0000 l1_loss:0.4848 l2_loss:0.3359 loss:0.9490
i:6 e:0.033 | gen_loss:0.9493 kl_loss:0.0000 l1_loss:0.5229 l2_loss:0.3786 loss:0.9493
i:7 e:0.039 | gen_loss:0.9400 kl_loss:0.0000 l1_loss:0.4548 l2_loss:0.2993 loss:0.9400
i:8 e:0.044 | gen_loss:0.9497 kl_loss:0.0000 l1_loss:0.4968 l2_loss:0.3501 loss:0.9498
i:9 e:0.050 | gen_loss:nan kl_loss:0.0000 l1_loss:0.4854 l2_loss:0.3355 loss:nan
有趣的是-这是关键-仅在使用256x256或更大的图像时才会发生。当我使用128x128的图像时，MS-SSIM可以正常运行。我使用MS-SSIM（以及使用L1的256x256 +）以128x128训练了数以百计的模型达数月之久，而且它们都能正常工作。一旦我转到256x256（或512x512），就会发生这种情况。
我尝试过：
完整错误：
InvalidArgumentError: Nan in summary histogram for: z [[Node: z = HistogramSummary[T=DT_FLOAT,
_device="/job:localhost/replica:0/task:0/device:CPU:0"](z/tag, vae/add/_41)]]
Caused by op u'z', defined at: File "/home/memo/anaconda2/lib/python2.7/site-packages/spyder/utils/ipython/start_kernel.py", line 227, in <module>
main() File "/home/memo/anaconda2/lib/python2.7/site-packages/spyder/utils/ipython/start_kernel.py", line 223, in main
kernel.start() File "/home/memo/anaconda2/lib/python2.7/site-packages/ipykernel/kernelapp.py", line 474, in start
ioloop.IOLoop.instance().start() File "/home/memo/anaconda2/lib/python2.7/site-packages/zmq/eventloop/ioloop.py", line 177, in start
super(ZMQIOLoop, self).start() File "/home/memo/anaconda2/lib/python2.7/site-packages/tornado/ioloop.py", line 888, in start
handler_func(fd_obj, events) File "/home/memo/anaconda2/lib/python2.7/site-packages/tornado/stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs) File "/home/memo/anaconda2/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
self._handle_recv() File "/home/memo/anaconda2/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg) File "/home/memo/anaconda2/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs) File "/home/memo/anaconda2/lib/python2.7/site-packages/tornado/stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs) File "/home/memo/anaconda2/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 276, in dispatcher
return self.dispatch_shell(stream, msg) File "/home/memo/anaconda2/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 228, in dispatch_shell
handler(stream, idents, msg) File "/home/memo/anaconda2/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 390, in execute_request
user_expressions, allow_stdin) File "/home/memo/anaconda2/lib/python2.7/site-packages/ipykernel/ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent) File "/home/memo/anaconda2/lib/python2.7/site-packages/ipykernel/zmqshell.py", line 501, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs) File "/home/memo/anaconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2717, in run_cell
interactivity=interactivity, compiler=compiler, result=result) File "/home/memo/anaconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2827, in run_ast_nodes
if self.run_code(code, result): File "/home/memo/anaconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2881, in run_code
exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-1-1c98e289993a>", line 1, in <module>
runfile('/home/memo/Dropbox/research/pypackages/msa/__tests/train_autovae.py', wdir='/home/memo/Dropbox/research/pypackages/msa/__tests') File "/home/memo/anaconda2/lib/python2.7/site-packages/spyder/utils/site/sitecustomize.py", line 866, in runfile
execfile(filename, namespace) File "/home/memo/anaconda2/lib/python2.7/site-packages/spyder/utils/site/sitecustomize.py", line 94, in execfile
builtins.execfile(filename, *where) File "/home/memo/Dropbox/research/pypackages/msa/__tests/train_autovae.py", line 88, in <module>
adam_beta1 = a.adam_beta1, File "/mnt/data/Dropbox/research/pypackages/msa/tf/models/autovae.py", line 385, in __init__
if log_z: tf.summary.histogram('z', self.z) File "/home/memo/anaconda2/lib/python2.7/site-packages/tensorflow/python/summary/summary.py", line 203, in histogram
tag=tag, values=values, name=scope) File "/home/memo/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/gen_logging_ops.py", line 283, in histogram_summary
"HistogramSummary", tag=tag, values=values, name=name) File "/home/memo/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def) File "/home/memo/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3392, in create_op
op_def=op_def) File "/home/memo/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1718, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): Nan in summary histogram for: z [[Node: z = HistogramSummary[T=DT_FLOAT,
_device="/job:localhost/replica:0/task:0/device:CPU:0"](z/tag, vae/add/_41)]]
ms-ssim丢失的完整代码
"""
Structural Similarity index for images in tensorflow
adapted from
https://stackoverflow.com/questions/39051451/ssim-ms-ssim-for-tensorflow
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import numpy as np
import math
EPS = 1e-6
def _fspecial_gauss(size, sigma):
"""Function to mimic the 'fspecial' gaussian MATLAB function
"""
if type(size)==int: size=(size,size)
y_data, x_data = np.mgrid[-size[0]//2 + 1:size[0]//2 + 1, -size[1]//2 + 1:size[1]//2 + 1]
x_data = np.expand_dims(x_data, axis=-1)
x_data = np.expand_dims(x_data, axis=-1)
y_data = np.expand_dims(y_data, axis=-1)
y_data = np.expand_dims(y_data, axis=-1)
x = tf.constant(x_data, dtype=tf.float32)
y = tf.constant(y_data, dtype=tf.float32)
g = tf.exp(-((x**2 + y**2)/(2.0*sigma**2)))
return g / tf.reduce_sum(g)
def ssim(img1, img2, cs_map=False, mean_metric=True, size=11, sigma=1.5):
# convert multichannel (e.g. RGB) images to batch
img1 = tf.expand_dims(tf.concat(tf.unstack(img1, axis=-1), axis=0), axis=-1)
img2 = tf.expand_dims(tf.concat(tf.unstack(img2, axis=-1), axis=0), axis=-1)
window = _fspecial_gauss(size, sigma) # window shape [size, size, 1, 1]
K1 = 0.01
K2 = 0.03
L = 1 # depth of image (255 in case the image has a differnt scale)
C1 = (K1*L)**2
C2 = (K2*L)**2
mu1 = tf.nn.conv2d(img1, window, strides=[1,1,1,1], padding='VALID')
mu2 = tf.nn.conv2d(img2, window, strides=[1,1,1,1], padding='VALID')
mu1_sq = mu1*mu1
mu2_sq = mu2*mu2
mu1_mu2 = mu1*mu2
sigma1_sq = tf.nn.conv2d(img1*img1, window, strides=[1,1,1,1], padding='VALID') - mu1_sq
sigma2_sq = tf.nn.conv2d(img2*img2, window, strides=[1,1,1,1], padding='VALID') - mu2_sq
sigma12 = tf.nn.conv2d(img1*img2, window, strides=[1,1,1,1], padding='VALID') - mu1_mu2
if cs_map:
value = (((2*mu1_mu2 + C1)*(2*sigma12 + C2))/(EPS + (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)),
(2.0*sigma12 + C2)/(EPS + sigma1_sq + sigma2_sq + C2))
else:
value = ((2*mu1_mu2 + C1)*(2*sigma12 + C2))/(EPS + (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
if mean_metric:
value = tf.reduce_mean(value)
return value
def ms_ssim(img1, img2, mean_metric=True, level=None, max_level=4, size=11, sigma=1.5):
if type(size)==int: size=(size,size)
if level is None:
img_shape = np.int32(img1.shape.as_list()[1:3])
size = np.int32(size)
size_log2 = np.log2(size)
levels = np.int32(np.log2(img_shape) - size_log2)+1 # find levels for each
level = min(levels[0], levels[1])
if max_level: level = min(level, max_level)
print('ms_ssim | levels:', levels, ', using:', level, ', smallest dims:', np.array(img_shape)//(2**(level-1)))
# weight = tf.constant([0.0448, 0.2856, 0.3001, 0.2363, 0.1333], dtype=tf.float32)
weight = tf.constant([1.0/level]*level, dtype=tf.float32)
mssim = []
mcs = []
for l in range(level):
ssim_map, cs_map = ssim(img1, img2, cs_map=True, mean_metric=False, size=size, sigma=sigma)
mssim.append(tf.reduce_mean(ssim_map))
mcs.append(tf.reduce_mean(cs_map))
filtered_im1 = tf.nn.avg_pool(img1, [1,2,2,1], [1,2,2,1], padding='SAME')
filtered_im2 = tf.nn.avg_pool(img2, [1,2,2,1], [1,2,2,1], padding='SAME')
img1 = filtered_im1
img2 = filtered_im2
# list to tensor of dim D+1
mssim = tf.stack(mssim, axis=0)
mcs = tf.stack(mcs, axis=0)
value = (tf.reduce_prod(mcs[0:level-1]**weight[0:level-1])*
(mssim[level-1]**weight[level-1]))
if mean_metric:
value = tf.reduce_mean(value)
return value