Tensorflow 张量流错误：登录和标签必须是可广播的

尝试运行tensorflow时显示以下错误：

InvalidArgumentError: logits and labels must be broadcastable: logits_size=[30,2] labels_size=[8,2]

下面是我的代码。我从中获得了代码第一部分的部分，从中获得了代码第二部分的部分。我把它们应用到我正在研究的东西中，我有一些属于两个不同类别的图像。对于培训，每个图像类都放在同一个培训文件夹中；对于测试，每个图像类都放在同一个测试文件夹中。我认为错误是指logits和label之间的不匹配。我尝试过在下面的代码中定义的权重和偏差中调整形状，但这并没有解决问题。我还试图篡改批量大小，但仍然没有解决方案。有人知道是什么导致了这个错误吗？我是如何安排我的训练和测试的

ROOT_PATH = "/my/file/path/images"
train_data_directory = os.path.join(ROOT_PATH, "data/train")
test_data_directory = os.path.join(ROOT_PATH, "data/test")

train_data = train_data_directory
test_data = test_data_directory

def one_hot_label(img):
    label = img.split('.')[0]
    global ohl
    ohl = []
    if label == 'A':
        ohl = np.array([1,0])
    elif label == 'B':
        ohl = np.array([0,1])
    return ohl

def train_data_with_label():
    train_images = []
    for i in tqdm(os.listdir(train_data)):
        path = os.path.join(train_data,i)
        img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
        img = cv2.resize(img, (28,28))
        train_images.append([np.array(img), one_hot_label(i)])
    shuffle(train_images)
    return train_images

def test_data_with_label():
    test_images = []
    for i in tqdm(os.listdir(test_data)):
        path = os.path.join(test_data,i)
        img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
        img = cv2.resize(img, (28,28))
        test_images.append([np.array(img), one_hot_label(i)])
    shuffle(test_images)
    return test_images

training_images = train_data_with_label()
testing_images = test_data_with_label()

#both placeholders are of type float
x = tf.placeholder("float", [None, 28,28,1])
y = tf.placeholder("float", [None, n_classes])



def conv2d(x, W, b, strides=1):
    # Conv2D wrapper, with bias and relu activation
    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
    x = tf.nn.bias_add(x, b)
    return tf.nn.relu(x) 

def maxpool2d(x, k=2):
    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],padding='SAME')

weights = {
    'wc1': tf.get_variable('W0', shape=(3,3,1,32), initializer=tf.contrib.layers.xavier_initializer()), 
    'wc2': tf.get_variable('W1', shape=(3,3,32,64), initializer=tf.contrib.layers.xavier_initializer()), 
    'wc3': tf.get_variable('W2', shape=(3,3,64,128), initializer=tf.contrib.layers.xavier_initializer()), 
    'wd1': tf.get_variable('W3', shape=(4*4*128,128), initializer=tf.contrib.layers.xavier_initializer()), 
    'out': tf.get_variable('W6', shape=(128,n_classes), initializer=tf.contrib.layers.xavier_initializer()), 
}
biases = {
    'bc1': tf.get_variable('B0', shape=(32), initializer=tf.contrib.layers.xavier_initializer()),
    'bc2': tf.get_variable('B1', shape=(64), initializer=tf.contrib.layers.xavier_initializer()),
    'bc3': tf.get_variable('B2', shape=(128), initializer=tf.contrib.layers.xavier_initializer()),
    'bd1': tf.get_variable('B3', shape=(128), initializer=tf.contrib.layers.xavier_initializer()),
    'out': tf.get_variable('B4', shape=(2), initializer=tf.contrib.layers.xavier_initializer()),
}

def conv_net(x, weights, biases):  

    # here we call the conv2d function we had defined above and pass the input image x, weights wc1 and bias bc1.
    conv1 = conv2d(x, weights['wc1'], biases['bc1'])
    # Max Pooling (down-sampling), this chooses the max value from a 2*2 matrix window and outputs a 14*14 matrix.
    conv1 = maxpool2d(conv1, k=2)

    # Convolution Layer
    # here we call the conv2d function we had defined above and pass the input image x, weights wc2 and bias bc2.
    conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
    # Max Pooling (down-sampling), this chooses the max value from a 2*2 matrix window and outputs a 7*7 matrix.
    conv2 = maxpool2d(conv2, k=2)

    conv3 = conv2d(conv2, weights['wc3'], biases['bc3'])
    # Max Pooling (down-sampling), this chooses the max value from a 2*2 matrix window and outputs a 4*4.
    conv3 = maxpool2d(conv3, k=2)

    #print(conv3.shape)

    # Fully connected layer
    # Reshape conv2 output to fit fully connected layer input
    fc1 = tf.reshape(conv3, [-1, weights['wd1'].get_shape().as_list()[0]])
    fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
    fc1 = tf.nn.relu(fc1)
    # Output, class prediction
    # finally we multiply the fully connected layer with the weights and add a bias term. 
    out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
    print(out.shape)
    return out

    #print(out.shape)

pred = conv_net(x, weights, biases)
#pred.shape
#labelsa = tf.constant(1., shape=y.shape)
#logsa = tf.constant(1., shape=pred.shape)
#labels = labels + tf.zeros_like(logsa)
print(pred)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=pred, labels=y))
print(y)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

with tf.Session() as sess:
    sess.run(init) 
    train_loss = []
    test_loss = []
    train_accuracy = []
    test_accuracy = []
    summary_writer = tf.summary.FileWriter('./Output', sess.graph)
    for i in range(training_iters):
        #print('here')
        for batch in range(len(train_X)//batch_size):
            print('here')
            #offset = (batch * batch_size) % (train_Y.shape[0] - batch_size)
            batch_x = train_X[batch*batch_size:min((batch+1)*batch_size,len(train_X))]
            batch_y = train_Y[batch*batch_size:min((batch+1)*batch_size,len(train_Y))]    
            # Run optimization op (backprop).
                # Calculate batch loss and accuracy
            print(batch_y.shape)
            opt = sess.run(optimizer, feed_dict={x: batch_x,
                                                              y: batch_y})
            loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,
                                                              y: batch_y})
        print("Iter " + str(i) + ", Loss= " + \
                      "{:.6f}".format(loss) + ", Training Accuracy= " + \
                      "{:.5f}".format(acc))
        print("Optimization Finished!")

        # Calculate accuracy for all 10000 mnist test images
        test_acc,valid_loss = sess.run([accuracy,cost], feed_dict={x: test_X,y: test_Y})
        train_loss.append(loss)
        test_loss.append(valid_loss)
        train_accuracy.append(acc)
        test_accuracy.append(test_acc)
        print("Testing Accuracy:","{:.5f}".format(test_acc))
    summary_writer.close()

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列车X和Y的类型是什么？它们都是NumPy数组吗？是的，它们都是NumPy数组。