features = ... # 31933 X 755) numpy array
labels = ...   # 31933 length numpay array
input_size = features.shape[1]
output_size = 15
hidden_size = 100

X = tf.placeholder(tf.float32, shape=(None, input_size), name="X")
y = tf.placeholder(tf.int64, shape=(None), name='y')

with tf.name_scope("nn"):
    hidden = fully_connected(X, hidden_size, scope="hidden")
    output = fully_connected(hidden, output_size, activation_fn=None, scope="output")
with tf.name_scope("loss"):
    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=output)
    loss = tf.reduce_mean(cross_entropy, name='loss')

learning_rate = 0.01

with tf.name_scope("train"):
    optimizer = tf.train.GradientDescentOptimizer(learning_rate)
    training_op = optimizer.minimize(loss)

with tf.name_scope("eval"):
    correct = tf.nn.in_top_k(output, y, 1)
    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))

init = tf.global_variables_initializer()
with tf.Session() as sess:
    init.run()
    for iteration in range(100):
        sess.run(training_op, feed_dict={X: features, y: labels})
        acc_train = accuracy.eval(feed_dict={X: features, y: labels})
        acc_dev = accuracy.eval(feed_dict={X: dev_features, y: dev_labels})
        print(iteration, "Train accuracy:", acc_train, "Dev accuracy:", acc_dev)

Traceback (most recent call last):
  File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1139, in _do_call
    return fn(*args)
  File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1121, in _run_fn
    status, run_metadata)
  File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/contextlib.py", line 66, in __exit__
    next(self.gen)
  File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
    pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: First dimension of predictions 31933 must match length of targets 1408
     [[Node: eval/InTopK = InTopK[T=DT_INT64, k=1, _device="/job:localhost/replica:0/task:0/cpu:0"](nn/output/BiasAdd, _arg_y_0_1)]]