Python 3.x Tensorflow中的登录和标签不匹配

在一次热编码后，Tensorflow中的logits和标签之间存在不匹配。 我的批量是256。如何在标签Tensor中获得批量大小？我猜这个问题与LabelEncoder和一个热编码器有关。任何帮助都是值得的

请在下面查找代码

from sklearn import preprocessing
le = preprocessing.LabelEncoder()

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = logits, labels = tf.one_hot(le.fit_transform(labels), n_classes)))

optimizer = tf.train.GradientDescentOptimizer(learning_rate = learn_rate).minimize(cost)

correct_prediction = tf.equal(tf.argmax(logits,1), tf.argmax(tf.one_hot(le.fit_transform(labels), n_classes),1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

batchSize =  256 

epochs = 20 #  200epoch+.5lr = 99.6
init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init) 
    total_batches = batches(batchSize, train_features, train_labels)

    for epoch in range(epochs): 
        for batch_features, batch_labels in total_batches: 
            train_data = {features: batch_features, labels : batch_labels, keep_prob : 0.5}
            sess.run(optimizer, feed_dict = train_data)
        # Print status for every 100 epochs
        if epoch % 10 == 0:
            valid_accuracy = sess.run(
                accuracy,
                feed_dict={
                    features: val_features,
                    labels: val_labels,
                    keep_prob : 0.5})
            print('Epoch {:<3} - Validation Accuracy: {}'.format(
                epoch,
                valid_accuracy))
    Accuracy = sess.run(accuracy, feed_dict={features : test_features, labels :test_labels, keep_prob : 1.0})
    # Save the model
    saver.save(sess, save_file)
    print('Trained Model Saved.')

    prediction=tf.argmax(logits,1)
    output_array = le.inverse_transform(prediction.eval(feed_dict={features : test_features, keep_prob: 1.0}))
    prediction = np.reshape(prediction, (test_features.shape[0],1))
    np.savetxt("prediction.csv", prediction, delimiter=",")

从sklearn导入预处理
le=预处理。LabelEncoder（）
cost=tf.reduce\u mean（tf.nn.softmax\u cross\u entropy\u with\u logits（logits=logits，labels=tf.one\u hot（le.fit\u transform（labels），n类）））
优化器=tf.train.GradientDescentOptimizer（学习率=学习率）。最小化（成本）
正确的预测=tf.equal（tf.argmax（logits，1），tf.argmax（tf.one_hot（le.fit_变换（标签），n_类），1））
准确度=tf.reduce_平均值（tf.cast（正确的预测，tf.float32））
batchSize=256
纪元=20#200纪元+0.5lr=99.6
init=tf.global_variables_initializer（）
使用tf.Session（）作为sess：
sess.run（初始化）
总批次=批次（批次大小、系列特征、系列标签）
对于范围内的历元（历元）：
对于批次特征，批次标签在总批次中：
train_data={features:batch_features，labels:batch_labels，keep_prob:0.5}
sess.run（优化器，feed\u dict=train\u数据）
#每100个纪元打印一次状态
如果历元%10==0：
有效精度=sess.run(
精确
饮食={
特征：val_特征，
标签：val_标签，
保持_prob:0.5}）
print（'Epoch{:的问题是tf.one_hot（le.fit_transform（标签），n_类）

这将传递一个需要numpy数组的张量。为此张量调用eval（）后，问题得到解决。
您是否检查了
批处理标签的形状，是256？是的，Vijay。批处理标签是256。登录应该是tf.one_hot（标签，n类）以获取（256，n类）成本=tf。使用登录减少平均值（tf.nn.softmax）交叉熵（logits=tf.one_-hot（labels，n_-classes）），labels=tf.one_-hot（labels，n_-classes）））正如您所说，但是对于错误计算，我们需要使用labels和logits。我真的不知道如何使用这样的方法。
InvalidArgumentError: logits and labels must be same size: logits_size=[256,1161] labels_size=[1,1161]
     [[Node: SoftmaxCrossEntropyWithLogits = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](Reshape, Reshape_1)]]

Caused by op 'SoftmaxCrossEntropyWithLogits', defined at:
  File "C:\Anaconda\envs\gpu\lib\runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "C:\Anaconda\envs\gpu\lib\runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "C:\Anaconda\envs\gpu\lib\site-packages\ipykernel\__main__.py", line 3, in <module>
    app.launch_new_instance()
  File "C:\Anaconda\envs\gpu\lib\site-packages\traitlets\config\application.py", line 658, in launch_instance
    app.start()
  File "C:\Anaconda\envs\gpu\lib\site-packages\ipykernel\kernelapp.py", line 477, in start
    ioloop.IOLoop.instance().start()
  File "C:\Anaconda\envs\gpu\lib\site-packages\zmq\eventloop\ioloop.py", line 177, in start
    super(ZMQIOLoop, self).start()
  File "C:\Anaconda\envs\gpu\lib\site-packages\tornado\ioloop.py", line 888, in start
    handler_func(fd_obj, events)
  File "C:\Anaconda\envs\gpu\lib\site-packages\tornado\stack_context.py", line 277, in null_wrapper
    return fn(*args, **kwargs)
  File "C:\Anaconda\envs\gpu\lib\site-packages\zmq\eventloop\zmqstream.py", line 440, in _handle_events
    self._handle_recv()
  File "C:\Anaconda\envs\gpu\lib\site-packages\zmq\eventloop\zmqstream.py", line 472, in _handle_recv
    self._run_callback(callback, msg)
  File "C:\Anaconda\envs\gpu\lib\site-packages\zmq\eventloop\zmqstream.py", line 414, in _run_callback
    callback(*args, **kwargs)
  File "C:\Anaconda\envs\gpu\lib\site-packages\tornado\stack_context.py", line 277, in null_wrapper
    return fn(*args, **kwargs)
  File "C:\Anaconda\envs\gpu\lib\site-packages\ipykernel\kernelbase.py", line 283, in dispatcher
    return self.dispatch_shell(stream, msg)
  File "C:\Anaconda\envs\gpu\lib\site-packages\ipykernel\kernelbase.py", line 235, in dispatch_shell
    handler(stream, idents, msg)
  File "C:\Anaconda\envs\gpu\lib\site-packages\ipykernel\kernelbase.py", line 399, in execute_request
    user_expressions, allow_stdin)
  File "C:\Anaconda\envs\gpu\lib\site-packages\ipykernel\ipkernel.py", line 196, in do_execute
    res = shell.run_cell(code, store_history=store_history, silent=silent)
  File "C:\Anaconda\envs\gpu\lib\site-packages\ipykernel\zmqshell.py", line 533, in run_cell
    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
  File "C:\Anaconda\envs\gpu\lib\site-packages\IPython\core\interactiveshell.py", line 2698, in run_cell
    interactivity=interactivity, compiler=compiler, result=result)
  File "C:\Anaconda\envs\gpu\lib\site-packages\IPython\core\interactiveshell.py", line 2802, in run_ast_nodes
    if self.run_code(code, result):
  File "C:\Anaconda\envs\gpu\lib\site-packages\IPython\core\interactiveshell.py", line 2862, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-5-9a6fe2134e3e>", line 52, in <module>
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = logits, labels = tf.one_hot(le.fit_transform(labels), n_classes)))
  File "C:\Anaconda\envs\gpu\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 1594, in softmax_cross_entropy_with_logits
    precise_logits, labels, name=name)
  File "C:\Anaconda\envs\gpu\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 2380, in _softmax_cross_entropy_with_logits
    features=features, labels=labels, name=name)
  File "C:\Anaconda\envs\gpu\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 767, in apply_op
    op_def=op_def)
  File "C:\Anaconda\envs\gpu\lib\site-packages\tensorflow\python\framework\ops.py", line 2506, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "C:\Anaconda\envs\gpu\lib\site-packages\tensorflow\python\framework\ops.py", line 1269, in __init__
    self._traceback = _extract_stack()

InvalidArgumentError (see above for traceback): logits and labels must be same size: logits_size=[256,1161] labels_size=[1,1161]
     [[Node: SoftmaxCrossEntropyWithLogits = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](Reshape, Reshape_1)]]