Tensorflow 汇总合并错误：形状 [-1,784] 具有负尺寸

我试图总结下面神经网络的训练过程。

import tensorflow as tf 
import numpy as np 

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets(".\MNIST",one_hot=True)

# Create the model
def train_and_test(hidden1,hidden2, learning_rate, epochs, batch_size):

    with tf.name_scope("first_layer"):
        input_data = tf.placeholder(tf.float32, [batch_size, 784], name = "input")
        weights1  = tf.Variable(
        tf.random_normal(shape =[784, hidden1],stddev=0.1),name = "weights")
        bias = tf.Variable(tf.constant(0.0,shape =[hidden1]), name = "bias")
        activation = tf.nn.relu(
        tf.matmul(input_data, weights1) + bias, name = "relu_act")
        tf.summary.histogram("first_activation", activation)

    with tf.name_scope("second_layer"):
        weights2  = tf.Variable(
        tf.random_normal(shape =[hidden1, hidden2],stddev=0.1),
        name = "weights")
        bias2 = tf.Variable(tf.constant(0.0,shape =[hidden2]), name = "bias")
        activation2 = tf.nn.relu(
        tf.matmul(activation, weights2) + bias2, name = "relu_act")
        tf.summary.histogram("second_activation", activation2)

    with tf.name_scope("output_layer"):
        weights3 = tf.Variable(
            tf.random_normal(shape=[hidden2, 10],stddev=0.5), name = "weights")
        bias3 = tf.Variable(tf.constant(1.0, shape =[10]), name = "bias")
        output = tf.add(
        tf.matmul(activation2, weights3, name = "mul"), bias3, name = "output")
        tf.summary.histogram("output_activation", output)
    y_ = tf.placeholder(tf.float32, [batch_size, 10])

    with tf.name_scope("loss"):
        cross_entropy = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=output))
        tf.summary.scalar("cross_entropy", cross_entropy)
    with tf.name_scope("train"):
        train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy)

    with tf.name_scope("tests"):
        correct_prediction = tf.equal(tf.argmax(output, 1), tf.argmax(y_, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    summary_op = tf.summary.merge_all()

    sess = tf.InteractiveSession()
    writer = tf.summary.FileWriter("./data", sess.graph)
    tf.global_variables_initializer().run()

    # Train
    for i in range(epochs):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
         _, summary = sess.run([train_step,summary_op], feed_dict={input_data: batch_xs, y_: batch_ys})
     writer.add_summary(summary)

     if i % 10 ==0:
          test_xs, test_ys = mnist.train.next_batch(batch_size)
          test_accuracy = sess.run(accuracy, feed_dict = {input_data : test_xs, y_ : test_ys})
    writer.close()
    return test_accuracy

if __name__ =="__main__":
print(train_and_test(500, 200, 0.001, 10000, 100))

我每 10 个步骤都会使用一批随机测试数据来测试模型。 问题出在夏日作家身上。 for 循环内的 sess.run() 抛出以下错误。

    Traceback (most recent call last):

  File "<ipython-input-18-78c88c8e6471>", line 1, in <module>
    runfile('C:/Users/Suman 
Nepal/Documents/Projects/MNISTtensorflow/mnist.py', wdir='C:/Users/Suman 
Nepal/Documents/Projects/MNISTtensorflow')

  File "C:\Users\Suman Nepal\Anaconda3\lib\site-
packages\spyder\utils\site\sitecustomize.py", line 880, in runfile
execfile(filename, namespace)

  File "C:\Users\Suman Nepal\Anaconda3\lib\site-
packages\spyder\utils\site\sitecustomize.py", line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)

  File "C:/Users/Suman Nepal/Documents/Projects/MNISTtensorflow/mnist.py", line 68, in <module>
    print(train_and_test(500, 200, 0.001, 100, 100))

  File "C:/Users/Suman Nepal/Documents/Projects/MNISTtensorflow/mnist.py", line 58, in train_and_test
    _, summary = sess.run([train_step,summary_op], feed_dict={input_data: batch_xs, y_: batch_ys})

  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 789, in run
    run_metadata_ptr)

  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 997, in _run
feed_dict_string, options, run_metadata)

  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1132, in _do_run
target_list, options, run_metadata)

  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1152, in _do_call
raise type(e)(node_def, op, message)

InvalidArgumentError: Shape [-1,784] has negative dimensions
 [[Node: first_layer_5/input = Placeholder[dtype=DT_FLOAT, shape=[?,784], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

Caused by op 'first_layer_5/input', defined at:
  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\spyder\utils\ipython\start_kernel.py", line 231, in <module>
main()
  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\spyder\utils\ipython\start_kernel.py", line 227, in main
kernel.start()
  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\ipykernel\kernelapp.py", line 477, in start
ioloop.IOLoop.instance().start()
  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\zmq\eventloop\ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\tornado\ioloop.py", line 888, in start
handler_func(fd_obj, events)
  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\tornado\stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 440, in _handle_events
self._handle_recv()
  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\zmq\eventloop\zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\tornado\stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
 File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
 File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 235, in dispatch_shell
handler(stream, idents, msg)
  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\ipykernel\kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\ipykernel\ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\ipykernel\zmqshell.py", line 533, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2717, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2827, in run_ast_nodes
if self.run_code(code, result):
  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py", line 2881, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-8-78c88c8e6471>", line 1, in <module>
runfile('C:/Users/Suman Nepal/Documents/Projects/MNISTtensorflow/mnist.py', wdir='C:/Users/Suman Nepal/Documents/Projects/MNISTtensorflow')
  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 880, in runfile
execfile(filename, namespace)
  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\spyder\utils\site\sitecustomize.py", line 102, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
  File "C:/Users/Suman Nepal/Documents/Projects/MNISTtensorflow/mnist.py", line 86, in <module>
  File "C:/Users/Suman Nepal/Documents/Projects/MNISTtensorflow/mnist.py", line 12, in train_and_test
   input_data = tf.placeholder(tf.float32, [None, 784], name = "input")
  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\tensorflow\python\ops\array_ops.py", line 1530, in placeholder
return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)
  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 1954, in _placeholder
name=name)
  File "C:\Users\Suman Nepal\Anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 767, in apply_op
op_def=op_def)
  File "C:\Users\Suman Nepal\Anaconda3\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:\Users\Suman Nepal\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 1269, in __init__
self._traceback = _extract_stack()

InvalidArgumentError (see above for traceback): Shape [-1,784] has negative dimensions
     [[Node: first_layer_5/input = Placeholder[dtype=DT_FLOAT, shape=[?,784], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

如果我删除了所有摘要编写者和摘要，则模型运行良好。 你能帮我找出这里的问题吗？我尝试操纵张量的形状但一无所获。

来自原始海报的已删除答案的一条评论：

我实际上在下面构建了一个神经网络with tf.Graph() as g。我删除了交互式会话并开始会话with tf.Session(g) as sess。它解决了问题。

图表g没有以这种方式标记为默认图，因此会话 (tf.InteractiveSession在原始代码中）将使用另一个图表来代替。

请注意，由于相同的错误消息，我偶然发现了这里。就我而言，我无意中遇到了这样的事情：

input_data = tf.placeholder(tf.float32, shape=(None, 50))
input_data = tf.tanh(input_data)
session.run(..., feed_dict={input_data: ...})

IE。我没有喂占位符。似乎其他一些张量运算可能会导致此令人困惑的错误，因为内部未定义的维度表示为 -1。