neural-network - tensorflow.equal() op 上的不兼容形状用于正确的预测评估

Question

使用 MNIST tutorial of Tensorflow ，我尝试用 "Database of Faces" 做一个卷积网络用于人脸识别.

图像大小为 112x92，我使用 3 个以上的卷积层将其减小到 6 x 5，如建议 here

我是卷积网络的新手，我的大部分层声明都是通过类比 Tensorflow MNIST 教程进行的，它可能有点笨拙，因此请随时就此提出建议。

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)
h_pool4 = max_pool_2x2(h_conv4)

W_conv5 = weight_variable([5, 5, 256, 512])
b_conv5 = bias_variable([512])
h_conv5 = tf.nn.relu(conv2d(h_pool4, W_conv5) + b_conv5)
h_pool5 = max_pool_2x2(h_conv5)

W_fc1 = weight_variable([6 * 5 * 512, 1024])
b_fc1 = bias_variable([1024])
h_pool5_flat = tf.reshape(h_pool5, [-1, 6 * 5 * 512])
h_fc1 = tf.nn.relu(tf.matmul(h_pool5_flat, W_fc1) + b_fc1)

keep_prob = tf.placeholder("float")
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

print orlfaces.train.num_classes # 40
W_fc2 = weight_variable([1024, orlfaces.train.num_classes])
b_fc2 = bias_variable([orlfaces.train.num_classes])
y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

当 session 运行“correct_prediction”操作时出现我的问题

tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))

至少我认为给出了错误信息:

W tensorflow/core/common_runtime/executor.cc:1027] 0x19369d0 Compute status: Invalid argument: Incompatible shapes: [8] vs. [20]
     [[Node: Equal = Equal[T=DT_INT64, _device="/job:localhost/replica:0/task:0/cpu:0"](ArgMax, ArgMax_1)]]
Traceback (most recent call last):
  File "./convolutional.py", line 133, in <module>
    train_accuracy = accuracy.eval(feed_dict = {x: batch[0], y_: batch[1], keep_prob: 1.0})
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 405, in eval
    return _eval_using_default_session(self, feed_dict, self.graph, session)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2728, in _eval_using_default_session
    return session.run(tensors, feed_dict)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 345, in run
    results = self._do_run(target_list, unique_fetch_targets, feed_dict_string)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 419, in _do_run
    e.code)
tensorflow.python.framework.errors.InvalidArgumentError: Incompatible shapes: [8] vs. [20]
     [[Node: Equal = Equal[T=DT_INT64, _device="/job:localhost/replica:0/task:0/cpu:0"](ArgMax, ArgMax_1)]]
Caused by op u'Equal', defined at:
  File "./convolutional.py", line 125, in <module>
    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 328, in equal
    return _op_def_lib.apply_op("Equal", x=x, y=y, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/op_def_library.py", line 633, in apply_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1710, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 988, in __init__
    self._traceback = _extract_stack()

看起来 y_conv 输出了一个形状为 8 x batch_size 而不是 number_of_class x batch_size 的矩阵

如果我将批量大小从 20 更改为 10，错误消息保持不变，但 [8] 与 [20] 我得到 [4] 与 [10]。因此，我得出结论，问题可能来自 y_conv 声明(上面代码的最后一行)。

损失函数、优化器、训练等声明与 MNIST 教程中的相同:

cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
sess.run((tf.initialize_all_variables()))
for i in xrange(1000):
    batch = orlfaces.train.next_batch(20)
    if i % 100 == 0:
        train_accuracy = accuracy.eval(feed_dict = {x: batch[0], y_: batch[1], keep_prob: 1.0})
        print "Step %d, training accuracy %g" % (i, train_accuracy)
    train_step.run(feed_dict = {x: batch[0], y_: batch[1], keep_prob: 0.5})

print "Test accuracy %g" % accuracy.eval(feed_dict = {x: orlfaces.test.images, y_: orlfaces.test.labels, keep_prob: 1.0})

感谢阅读，祝您有美好的一天

Answer 1

好吧，经过大量调试后，我发现我的问题是由于标签实例化不当造成的。我没有创建充满零的数组并将一个值替换为一，而是使用随机值创建它们!愚蠢的错误。如果有人想知道我在那里做错了什么以及我如何解决它here是我所做的改变。

无论如何，在我所做的所有调试过程中，为了找到这个错误，我找到了一些有用的信息来调试此类问题:

对于交叉熵声明，tensorflow 的 MNIST 教程使用的公式可以导致 NaN 值

这个公式是

cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))

取而代之的是，我找到了两种以更安全的方式声明它的方法:

cross_entropy = -tf.reduce_sum(y_ * tf.log(tf.clip_by_value(y_conv, 1e-10, 1.0)))

或者:

cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logit, y_))

正如先生所说。打印张量的形状有助于检测形状异常。

要获得张量的形状，只需像这样调用他的 get_shape() 方法:

print "W shape:", W.get_shape()

user1111929 in this问题使用调试打印帮助我断言问题来自哪里。