我正在尝试从以下存储库中获取代码，该存储库基于本文。它有很多错误，但我主要让它工作。但是，我一直遇到同样的问题，我真的不明白如何解决这个问题/甚至出了什么问题。
第二次验证是否满足语句标准时发生错误。第一次总是有效，然后在第二次中断。如果有帮助，我将包括它在中断之前打印的输出。请参阅下面的错误：
step = 1, train_loss = 1204.7784423828125, train_accuracy = 0.13725490868091583
counter = 1, dev_loss = 1188.6639287274584, dev_accuacy = 0.2814199453625912
step = 2, train_loss = 1000.983154296875, train_accuracy = 0.26249998807907104
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py in _do_call(self, fn, *args)
1364 try:
-> 1365 return fn(*args)
1366 except errors.OpError as e:
7 frames
InvalidArgumentError: 2 root error(s) found.
(0) Invalid argument: Incompatible shapes: [2,185] vs. [2,229]
[[{{node loss/cond/add_1}}]]
[[viterbi_decode/cond/rnn_1/while/Switch_3/_541]]
(1) Invalid argument: Incompatible shapes: [2,185] …使用Tensorflow的MNIST教程,我尝试使用"面部数据库"创建一个用于人脸识别的卷积网络.
图像大小为112x92,我使用3个卷积层将其减少到6 x 5,如此处所示
我在卷积网络上非常新,我的大部分层声明是通过类比Tensorflow MNIST教程制作的,它可能有点笨拙,所以请随时向我提出建议.
x_image = tf.reshape(x, [-1, 112, 92, 1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_conv3 = weight_variable([5, 5, 64, 128])
b_conv3 = bias_variable([128])
h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3)
h_pool3 = max_pool_2x2(h_conv3)
W_conv4 = weight_variable([5, 5, 128, 256])
b_conv4 = bias_variable([256])
h_conv4 = tf.nn.relu(conv2d(h_pool3, W_conv4) + b_conv4) …