python - InvalidArgumentError in Tensorflow -

i`m trying create neural network using tensorflow tools.

sizeofrow = len(data[0]) x = tensorflow.placeholder("float", shape=[none, sizeofrow]) y = tensorflow.placeholder("float")  def neuralnetworktrain(x):   prediction = neuralnetworkmodel(x)   # using softmax function, normalize values range(0,1)   cost = tensorflow.reduce_mean(tensorflow.nn.softmax_cross_entropy_with_logits(prediction, y)) 

this part net have got error:

invalidargumenterror (see above traceback): logits , labels must same size: logits_size=[500,2] labels_size=[1,500]  [[node: softmaxcrossentropywithlogits = softmaxcrossentropywithlogits[t=dt_float, _device="/job:localhost/replica:0/task:0/cpu:0"](reshape, reshape_1)]] 

someone know what`s wrong?

edit: have got code:

for temp in range(int(len(data) / batchsize)):     ex, ey = takenextbatch(i) # takes 500 examples     += 1     # to-do : fix bug here     temp, cos = sess.run([optimizer, cost], feed_dict= {x:ex, y:ey})  

this error typeerror: unhashable type: 'list'

well, error quite self-describing.

logits , labels must same size: logits_size=[500,2] labels_size=[1,500]

so, first, labels should transposed have size 500, 1 , second, softmax_cross_entropy_with_logits expects labels presented in form of probability distribution (e.g. [[0.1, 0.9], [1.0, 0.0]]).

if know classes exclusive (which case), should switch using sparse_softmax_cross_entropy_with_logits.