Tensorflow登录和标签错误，但形状相同

这个问题已经被问过好几次了，但我似乎无法将以前的解决方案应用到我的代码中。因此，如能就如何解决这一问题提供任何建议，我将不胜感激。我尝试过使用pdb，并在问题出现之前设置了一个跟踪点，但没有提供太多信息。 我正在根据我的问题调整本教程：

数据形状：

x_train.shape: (1161, 68, 68, 1)
x_test.shape: (216, 68, 68, 1)
y_test.shape: (216,)
y_train.shape: (1161,)

发生错误的地方：

#Train the Model
steps = int(x_train.shape[0]/batchSize)
for i in range(numEpochs):
    print(i)
    accHist = []
    accHist2 = []
    #x_train, y_train = imf.shuffle(x_train, y_train)
    for j in range(steps):
        print(j)
        #Calculate our current step
        step = i * steps + j
        #Feed forward batch of train images into graph and log accuracy
        acc = sess.run([accuracy], feed_dict={X: x_train[(j*batchSize):((j+1)*batchSize),:,:,:], Y_: np.array(y_train[(j*batchSize):((j+1)*batchSize)]).reshape(1,30), keepRate1: 1, keepRate2: 1})
        print(accHist)
        accHist.append(acc)

        #Back propigate using adam optimizer to update weights and biases.
        sess.run(train_step, feed_dict={X: x_train[(j*batchSize):((j+1)*batchSize),:,:,:], Y_: np.array(y_train[(j*batchSize):((j+1)*batchSize)]).reshape(1,30), keepRate1: 0.2, keepRate2: 0.5})
        print("success")

    print('Epoch number {} Training Accuracy: {}'.format(i+1, np.mean(accHist)))

    #Feed forward all test images into graph and log accuracy
    for k in range(int(x_test.shape[0]/batchSize)):
        acc = sess.run(accuracy, feed_dict={X: x_test[(k*batchSize):((k+1)*batchSize),:,:,:], Y_: np.array(y_test[(k*batchSize):((k+1)*batchSize)]).reshape(1,30), keepRate1: 1, keepRate2: 1})
        accHist2.append(acc)
    print("Test Set Accuracy: {}".format(np.mean(accHist2)))

我收到以下错误消息：

InvalidArgumentError: logits and labels must be same size: logits_size=[30,30] labels_size=[1,30]
     [[Node: cross_entropy_7/SoftmaxCrossEntropyWithLogits = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](cross_entropy_7/Reshape, cross_entropy_7/Reshape_1)]]

在本教程之后，我认为登录设置如下：

#FULLY CONNECTED 3 & SOFTMAX OUTPUT
with tf.name_scope('softmax') as scope:
    fc2w = tf.Variable(tf.truncated_normal([512, classes], dtype=tf.float32,
                                           stddev=1e-1), name='weights3_2')
    fc2b = tf.Variable(tf.constant(1.0, shape=[classes], dtype=tf.float32),
                       trainable=True, name='biases3_2')
    Ylogits = tf.nn.bias_add(tf.matmul(fc1_drop, fc2w), fc2b)
    Y = tf.nn.softmax(Ylogits)

这里的print（Ylogits.shape）给了我：（？，30）。课程设置为30，因此这似乎是有意义的

这似乎是不起作用的函数，所以我打印了形状：

with tf.name_scope('cross_entropy'):
    print(Ylogits.shape)
    print(Y.shape)
    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits, labels=Y_)
    loss = tf.reduce_mean(cross_entropy)

这给了我：

(?, 30)
(?, 30)

执行上面的反向传播行时，尽管这似乎不起作用。有人能帮忙吗

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回应评论（这基本上是上述链接中的教程代码）：

席位持有人：

    classes = 30
    X = tf.placeholder(tf.float32, name="X-placeholder", shape=(None, 68, 68, 1))
    Y_ = tf.placeholder(tf.float32, [None, classes], name="Y_-placeholder")
    keepRate1 = tf.placeholder(tf.float32, name="keepRate1-placeholder")
    keepRate2 = tf.placeholder(tf.float32, name="keepRate2-placeholder")

型号：

# CONVOLUTION 1 - 1
with tf.name_scope('conv1_1'):
    filter1_1 = tf.Variable(tf.truncated_normal([3, 3, 1, 32], dtype=tf.float32,
                            stddev=1e-1), name='weights1_1')
    stride = [1,1,1,1]
    conv = tf.nn.conv2d(X, filter1_1, stride, padding='SAME')
    biases = tf.Variable(tf.constant(0.0, shape=[32], dtype=tf.float32),
                         trainable=True, name='biases1_1')
    out = tf.nn.bias_add(conv, biases)
    conv1_1 = tf.nn.relu(out)

# CONVOLUTION 1 - 2
with tf.name_scope('conv1_2'):
    filter1_2 = tf.Variable(tf.truncated_normal([3, 3, 32, 32], dtype=tf.float32,
                                                stddev=1e-1), name='weights1_2')
    conv = tf.nn.conv2d(conv1_1, filter1_2, [1,1,1,1], padding='SAME')
    biases = tf.Variable(tf.constant(0.0, shape=[32], dtype=tf.float32),
                         trainable=True, name='biases1_2')
    out = tf.nn.bias_add(conv, biases)
    conv1_2 = tf.nn.relu(out)

# POOL 1
with tf.name_scope('pool1'):
    pool1_1 = tf.nn.max_pool(conv1_2,
                             ksize=[1, 2, 2, 1],
                             strides=[1, 2, 2, 1],
                             padding='SAME',
                             name='pool1_1')
    pool1_1_drop = tf.nn.dropout(pool1_1, keepRate1)

# CONVOLUTION 2 - 1
with tf.name_scope('conv2_1'):
    filter2_1 = tf.Variable(tf.truncated_normal([3, 3, 32, 64], dtype=tf.float32,
                                                stddev=1e-1), name='weights2_1')
    conv = tf.nn.conv2d(pool1_1_drop, filter2_1, [1, 1, 1, 1], padding='SAME')
    biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),
                         trainable=True, name='biases2_1')
    out = tf.nn.bias_add(conv, biases)
    conv2_1 = tf.nn.relu(out)

# CONVOLUTION 2 - 2
with tf.name_scope('conv2_2'):
    filter2_2 = tf.Variable(tf.truncated_normal([3, 3, 64, 64], dtype=tf.float32,
                                                stddev=1e-1), name='weights2_2')
    conv = tf.nn.conv2d(conv2_1, filter2_2, [1, 1, 1, 1], padding='SAME')
    biases = tf.Variable(tf.constant(0.0, shape=[64], dtype=tf.float32),
                         trainable=True, name='biases2_2')
    out = tf.nn.bias_add(conv, biases)
    conv2_2 = tf.nn.relu(out)

# POOL 2
with tf.name_scope('pool2'):
    pool2_1 = tf.nn.max_pool(conv2_2,
                             ksize=[1, 2, 2, 1],
                             strides=[1, 2, 2, 1],
                             padding='SAME',
                             name='pool2_1')
    pool2_1_drop = tf.nn.dropout(pool2_1, keepRate1)

#FULLY CONNECTED 1
with tf.name_scope('fc1') as scope:
    shape = int(np.prod(pool2_1_drop.get_shape()[1:]))
    fc1w = tf.Variable(tf.truncated_normal([shape, 512], dtype=tf.float32,
                                           stddev=1e-1), name='weights3_1')
    fc1b = tf.Variable(tf.constant(1.0, shape=[512], dtype=tf.float32),
                       trainable=True, name='biases3_1')
    pool2_flat = tf.reshape(pool2_1_drop, [-1, shape])
    out = tf.nn.bias_add(tf.matmul(pool2_flat, fc1w), fc1b)
    fc1 = tf.nn.relu(out)
    fc1_drop = tf.nn.dropout(fc1, keepRate2)

#FULLY CONNECTED 3 & SOFTMAX OUTPUT
with tf.name_scope('softmax') as scope:
    fc2w = tf.Variable(tf.truncated_normal([512, classes], dtype=tf.float32,
                                           stddev=1e-1), name='weights3_2')
    fc2b = tf.Variable(tf.constant(1.0, shape=[classes], dtype=tf.float32),
                       trainable=True, name='biases3_2')
    Ylogits = tf.nn.bias_add(tf.matmul(fc1_drop, fc2w), fc2b)
    Y = tf.nn.softmax(Ylogits)

numEpochs = 400
batchSize = 30
alpha = 1e-5

with tf.name_scope('cross_entropy'):
    print(Ylogits.shape)
    print(Y.shape)
    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits, labels=Y_)
    loss = tf.reduce_mean(cross_entropy)

with tf.name_scope('accuracy'):
    correct_prediction = tf.equal(tf.argmax(Y, 1), tf.argmax(Y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

with tf.name_scope('train'):
    train_step = tf.train.AdamOptimizer(learning_rate=alpha).minimize(loss)

#Create Session and insert variables
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)

张量形状（？，30）表示未设置批次大小，因此您可以将任何批次大小数据输入到图形中，问题是您可能会遇到此类问题，并且需要在头脑中跟踪张量形状

您需要修复的是：要么一批中有30个图像，但一批中只有一个标签，这需要修复，因为您无法计算只有一个标签的30个图像的损失，您要么需要将图像数量减少到1，要么将标签批大小增加到30，也可能是在某个地方，你不正确地重塑了张量

我会看看你在哪里读取数据，然后对数据进行批处理，这很可能是问题所在，或者是你正在重塑数据的地方

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发布您的全部代码，这将更有帮助。

如何定义fc1_-drop？@jafergas我在上面添加了定义fc1_-drop的代码，因为它太长，无法发表评论。KeepRate被定义为：keepRate2=tf.placeholder（tf.float32，name=“keepRate2 placeholder”），正如所怀疑的，您的批大小是30，所以您的logits张量是可以的，但您的标签张量不是，具体地说，您可以：acc=sess.run（[accurity]，feed_dict={X:X_train[（jbatchSize）：（（j+1）*batchSize），：，：，：，：，：，：，：，：]，Y_u:np.数组（Y_train[（jbatchSize）：(（j+1）*batchSize）]）。重塑（1,30），keepRate1:1，keepRate2:1}）在代码中的3个位置，您可以看到您正在将Y张量重塑为（1,30），它与logits张量（30,30）的形状不匹配。您需要加载30个标签，然后为30个类正确编码，因此它们也具有形状（30,30）。