我试图在TensorFlow中建立一个相当特殊的网络，我有点工作了。可悲的是，我偶然发现了一个错误，我无法解决，甚至找不到合适的地方。 据我所知，网络建立成功，直到定义了损失函数。然后，错误消息说明了不兼容的形状：
ValueError: Shapes (1, 17, 17, 44) and (1, 16, 16, 44) are not compatible
事情是错误没有说明问题发生在哪个张量或代码行。我已经打印出了所有可以提出的形状，我甚至找不到形状的东西（1,17,17,44）。
from tensorflow.python.framework import ops
from tensorflow.python.ops import gen_nn_ops
@ops.RegisterGradient("MaxPoolWithArgmax")
def _MaxPoolWithArgmaxGrad(op, grad, some_other_arg):
return gen_nn_ops._max_pool_grad(op.inputs[0],
op.outputs[0],
grad,
op.get_attr("ksize"),
op.get_attr("strides"),
padding=op.get_attr("padding"),
data_format='NHWC')
class FCN_RGBD:
def __init__(self, checkpoint_dir='./checkpoints/'):
self.build(1)
# "allow_soft_placement = True" makes TensorFlow automatically choose an existing and supported GPU device
self.config = tf.ConfigProto(allow_soft_placement = True)
self.session = tf.Session(config = self.config)
self.session.run(tf.global_variables_initializer())
def weight_variable(self, shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(self, shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv_layer(self, x, W_shape, b_shape, strides, name, padding):
W = self.weight_variable(W_shape)
b = self.bias_variable([b_shape])
return tf.nn.relu(tf.nn.conv2d(x, W, strides=strides, padding=padding) + b)
def conv_skip_layer(self, x, W_shape, b_shape, name, padding):
W = self.weight_variable(W_shape)
b = self.bias_variable([b_shape])
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding=padding) + b
def deconv_layer(self, x, out_shape, W_shape, b_shape, strides, name, padding):
W = self.weight_variable(W_shape)
b = self.bias_variable([b_shape])
return tf.nn.conv2d_transpose(x, W, output_shape=out_shape, strides=strides, padding=padding) + b
def pool_layer3x3(self, x):
with tf.device('/gpu:0'):
return tf.nn.max_pool_with_argmax(x, ksize=[1, 3, 3, 1], strides=[1, 3, 3, 1], padding='SAME')
def pool_layer2x2(self, x):
with tf.device('/gpu:0'):
return tf.nn.max_pool_with_argmax(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
def build(self, batchsize):
print('Building the FCN...')
with tf.device('/gpu:0'):
self.x = tf.placeholder(tf.float32, shape=(batchsize, 250, 250, 1))
self.y = tf.placeholder(tf.int64, shape=(batchsize, 250, 250, 1))
self.rate = tf.placeholder(tf.float32, shape=[])
conv1 = self.conv_layer(self.x, [5, 5, 1, 64], 64, [1, 2, 2, 1], 'conv1', 'SAME')
pool1, pool_1_argmax = self.pool_layer3x3(conv1)
conv1_skip = self.conv_skip_layer(pool1, [1, 1, 64, 44], 44, 'conv1_skip', 'VALID')
conv2 = self.conv_layer(pool1, [3, 3, 64, 128], 128, [1, 1, 1, 1], 'conv2', 'VALID')
pool2, pool_2_argmax = self.pool_layer2x2(conv2)
conv2_skip = self.conv_skip_layer(pool2, [1, 1, 128, 44], 44, 'conv2_skip', 'VALID')
conv3 = self.conv_layer(pool2, [5, 5, 128, 256], 256, [1, 1, 1, 1], 'conv3', 'VALID')
conv4 = self.conv_layer(conv3, [3, 3, 256, 44], 44, [1, 1, 1, 1], 'conv4', 'SAME')
deconv1 = self.deconv_layer(conv4, tf.stack([batchsize, 16, 16, 44]), [3, 3, 44, 44], 44, [1, 1, 1, 1], 'deconv1', 'SAME')
conv2_skip = tf.image.resize_image_with_crop_or_pad(conv2_skip, 16, 16)
sum1 = conv2_skip + deconv1
dropout1 = tf.nn.dropout(sum1, keep_prob=0.5)
deconv2 = self.deconv_layer(dropout1, tf.stack([batchsize, 34, 34, 44]), [4, 4, 44, 44], 44, [1, 2, 2, 1], 'deconv2', 'SAME')
conv1_skip = tf.image.resize_image_with_crop_or_pad(conv1_skip, 34, 34)
sum2 = conv1_skip + deconv2
dropout2 = tf.nn.dropout(sum2, keep_prob=0.5)
deconv_final = self.deconv_layer(dropout2, tf.stack([batchsize, 250, 250, 44]), [19, 19, 44, 44], 44, [1, 7, 7, 1], 'deconv_final', 'VALID')
annotation_pred = tf.argmax(deconv_final, dimension=3, name='prediction')
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=tf.squeeze(self.y, squeeze_dims=[3]), logits=deconv_final)
self.loss = tf.reduce_mean(cross_entropy, name='cross_entropy_mean')
self.train_step = tf.train.AdamOptimizer(self.rate).minimize(self.loss)
self.prediction = tf.argmax(tf.reshape(tf.nn.softmax(logits), tf.shape(deconv_final)), dimension=3)
这是错误消息：
Traceback (most recent call last):
File "C:\Users\user\AppData\Local\conda\conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\framework\tensor_shape.py", line 560, in merge_with
new_dims.append(dim.merge_with(other[i]))
File "C:\Users\user\AppData\Local\conda\conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\framework\tensor_shape.py", line 135, in merge_with
self.assert_is_compatible_with(other)
File "C:\Users\user\AppData\Local\conda\conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\framework\tensor_shape.py", line 108, in assert_is_compatible_with
% (self, other))
ValueError: Dimensions 17 and 16 are not compatible
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "main.py", line 5, in <module>
fcn_rgbd = FCN_RGBD()
File "C:\Users\user\netcase\Workspace\Depth_BPC_v1\FCN_RGBD.py", line 23, in __init__
self.build(1)
File "C:\Users\user\netcase\Workspace\Depth_BPC_v1\FCN_RGBD.py", line 162, in build
self.train_step = tf.train.AdamOptimizer(self.rate).minimize(self.loss)
File "C:\Users\user\AppData\Local\conda\conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\training\optimizer.py", line 315, in minimize
grad_loss=grad_loss)
File "C:\Users\user\AppData\Local\conda\conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\training\optimizer.py", line 386, in compute_gradients
colocate_gradients_with_ops=colocate_gradients_with_ops)
File "C:\Users\user\AppData\Local\conda\conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\ops\gradients_impl.py", line 580, in gradients
in_grad.set_shape(t_in.get_shape())
File "C:\Users\user\AppData\Local\conda\conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\framework\ops.py", line 413, in set_shape
self._shape = self._shape.merge_with(shape)
File "C:\Users\user\AppData\Local\conda\conda\envs\tensorflow-gpu\lib\site-packages\tensorflow\python\framework\tensor_shape.py", line 564, in merge_with
(self, other))
ValueError: Shapes (1, 17, 17, 44) and (1, 16, 16, 44) are not compatible
我对这个含糊不清的问题感到非常抱歉，但我真的没有从哪里开始的概念。
答案 0 :(得分：2)
原来是不同层中尺寸错误的问题。不幸的是，conv2d_transpose的错误消息不是很有帮助。这篇文章给了我很多帮助：Confused about conv2d_transpose