InvalidArgumentError: logits and labels must be same size: logits_size=[1,2] labels_size=[1,1]

Question

I've worked through a few other errors that've been thrown, but I've never seen this error before and even after doing some research I'm still not sure exactly what the issue is here or how to solve it.

I'm guessing reshaping the data at some point is required, but what i don't get is why this is an issue, or what the sizes of [1,2] and [1,1] actually mean.

The data input into the script is [128 x 128 x 128 ndarray, binary label]

The code I'm using is:

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

After taking a closer look I found that the issue was that the output of the 3rd fully-connected layer was 2 classes, when the labels are binary for a single class. Changed code in last fully connected layer to account for the single class, and this error was solved.