InvalidArgumentError：ロジットとラベルのサイズが同じでなければなりません：logits_size = [1,2] labels_size = [1,1]

Question

私はいくつかのエラーをスローしましたが、このエラーは見たことがありません私はいくつかの研究を行う前と後で、私はまだ問題が何であるか、それを解決する方法を正確には分かりません。

私は、ある時点でデータを再形成する必要があると推測していますが、なぜこれが問題であるのか、[1,2]と[1,1]のサイズが実際に何を意味するのか分かりません。

スクリプトへのデータ入力は[128×128×128 ndarray、バイナリラベル]

私が使用しているコードは：

import tensorflow as tf 
import numpy as np 
import os 
import math 

# input arrays 
x = tf.placeholder(tf.float32, [None, 128, 128, 128, 1]) 
# labels 
y = tf.placeholder(tf.float32, None) 
# learning rate 
lr = tf.placeholder(tf.float32) 

##### Code for ConvNet is here ##### 

# Data 
INPUT_FOLDER = 'data/cubed_data/pp/labelled' 
images = os.listdir(INPUT_FOLDER) 
images.sort() 

td = [] 
count = 1 
for i in images: 
    im = np.load(INPUT_FOLDER + "/" + i) 
    data = im[0] 
    data = np.reshape(data, (128, 128, 128, 1)) 
    label = im[1] 
    lbd = [data, label] 
    td.append(lbd) 
test_data = td[:100] 
train_data = td[100:] 

cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=fc3l, labels=y) 

correct_prediction = tf.equal(tf.argmax(probs, 1), tf.argmax(y, 0)) 
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 

train_step = tf.train.AdamOptimizer(lr).minimize(cross_entropy) 

# init 
init = tf.initialize_all_variables() 
sess = tf.Session() 
sess.run(init) 

def training_step(i, update_test_data, update_train_data): 

    for a in range(len(train_data)): 

     batch = train_data[a] 
     batch_x = batch[0] 
     batch_y = batch[1] 

     # learning rate decay 
     max_learning_rate = 0.003 
     min_learning_rate = 0.0001 
     decay_speed = 2000.0 
     learning_rate = min_learning_rate + (max_learning_rate - min_learning_rate) * math.exp(-i/decay_speed) 

     if update_train_data: 
      a, c = sess.run([accuracy, cross_entropy], {x: [batch_x], y: [batch_y]}) 
      print(str(i) + ": accuracy:" + str(a) + " loss: " + str(c) + " (lr:" + str(learning_rate) + ")") 


     if update_test_data: 
      a, c = sess.run([accuracy, cross_entropy], {x: [test_data[0]], y: [test_data[1]]}) 
     print(str(i) + ": ********* epoch " + " ********* test accuracy:" + str(a) + " test loss: " + str(c)) 

     sess.run(train_step, {x: [batch_x], y: [batch_y], lr: learning_rate}) 

for q in range(10000 + 1): 
    training_step(q, q % 100 == 0, q % 20 == 0) 

..with：

Invalid argument: logits and labels must be same size: logits_size=[1,2] labels_size=[1,1] 
    [[Node: SoftmaxCrossEntropyWithLogits = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](Reshape, Reshape_1)]] 
Traceback (most recent call last): 
    File "/home/entelechy/tfenv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 972, in _do_call 
    return fn(*args) 
    File "/home/entelechy/tfenv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 954, in _run_fn 
    status, run_metadata) 
    File "/usr/lib/python3.5/contextlib.py", line 66, in __exit__ 
    next(self.gen) 
    File "/home/entelechy/tfenv/lib/python3.5/site-packages/tensorflow/python/framework/errors.py", line 463, in raise_exception_on_not_ok_status 
    pywrap_tensorflow.TF_GetCode(status)) 
tensorflow.python.framework.errors.InvalidArgumentError: logits and labels must be same size: logits_size=[1,2] labels_size=[1,1] 
    [[Node: SoftmaxCrossEntropyWithLogits = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](Reshape, Reshape_1)]] 
    [[Node: Reshape_2/_7 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_233_Reshape_2", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]] 

During handling of the above exception, another exception occurred: 

Traceback (most recent call last): 
    File "tfvgg.py", line 293, in <module> 
    training_step(q, q % 100 == 0, q % 20 == 0) 
    File "tfvgg.py", line 282, in training_step 
    a, c = sess.run([accuracy, cross_entropy], {x: [batch_x], y: [batch_y]}) 
    File "/home/entelechy/tfenv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 717, in run 
    run_metadata_ptr) 
    File "/home/entelechy/tfenv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 915, in _run 
    feed_dict_string, options, run_metadata) 
    File "/home/entelechy/tfenv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 965, in _do_run 
    target_list, options, run_metadata) 
    File "/home/entelechy/tfenv/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 985, in _do_call 
    raise type(e)(node_def, op, message) 
tensorflow.python.framework.errors.InvalidArgumentError: logits and labels must be same size: logits_size=[1,2] labels_size=[1,1] 
    [[Node: SoftmaxCrossEntropyWithLogits = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](Reshape, Reshape_1)]] 
    [[Node: Reshape_2/_7 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_233_Reshape_2", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]] 

Caused by op 'SoftmaxCrossEntropyWithLogits', defined at: 
    File "tfvgg.py", line 254, in <module> 
    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=fc3l, labels=y) 
    File "/home/entelechy/tfenv/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py", line 676, in softmax_cross_entropy_with_logits 
    precise_logits, labels, name=name) 
    File "/home/entelechy/tfenv/lib/python3.5/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 1744, in _softmax_cross_entropy_with_logits 
    features=features, labels=labels, name=name) 
    File "/home/entelechy/tfenv/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 749, in apply_op 
    op_def=op_def) 
    File "/home/entelechy/tfenv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2380, in create_op 
    original_op=self._default_original_op, op_def=op_def) 
    File "/home/entelechy/tfenv/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1298, in __init__ 
    self._traceback = _extract_stack() 

InvalidArgumentError (see above for traceback): logits and labels must be same size: logits_size=[1,2] labels_size=[1,1] 
    [[Node: SoftmaxCrossEntropyWithLogits = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"](Reshape, Reshape_1)]] 
    [[Node: Reshape_2/_7 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_233_Reshape_2", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]] 

Answer 1

詳細を見てみると、ラベルが1つのクラスのバイナリである場合、完全に接続された3番目の層の出力が2つのクラスであることがわかりました。最後に完全に接続されたレイヤーのコードを単一のクラスを説明するように変更しました。このエラーは解決されました。