title: tensorflow cnn error: InvalidArgumentError (see above for traceback): logits and labels must be same size
body: <p>I wrote this code by modified from tensorflow official tutorial.
  I had a network as following:</p>
  
  <pre><code>def weight_variable(shape):
      initial = tf.truncated_normal(shape, stddev=0.1)
      return tf.Variable(initial)
  
  def bias_variable(shape):
      initial = tf.constant(0.1, shape=shape)
      return tf.Variable(initial)
  
  def conv2d(x, w):
      return tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME')
  
  def max_pool_2x2(x):
      return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
  
  import tensorflow as tf
  import numpy as np
  
  train_feature = np.array(model.dataset[0])
  train_label = np.array(model.labelset[0])
  print(train_feature.shape)
  print(train_label.shape)
  
  x_placeholder = tf.placeholder(tf.float32, shape=[None, train_feature.shape[1]])
  y_placeholder = tf.placeholder(tf.float32, shape=[None, 8])
  # network structure
  w_conv1 = weight_variable([5, 5, 1, 32])
  b_conv1 = bias_variable([32])
  x_image = tf.reshape(x_placeholder, [-1, 160, 120, 1])
  h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1)
  h_pool1 = max_pool_2x2(h_conv1)
  
  w_conv2 = weight_variable([5, 5, 32, 64])
  b_conv2 = bias_variable([64])
  
  h_conv2 = tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2)
  h_pool2 = max_pool_2x2(h_conv2)
  
  w_fc1 = weight_variable([320, 1024])
  b_fc1 = bias_variable([1024])
  
  h_pool2_flat = tf.reshape(h_pool2, [-1, 320])
  h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)
  
  keep_prob = tf.placeholder(tf.float32)
  h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
  
  w_fc2 = weight_variable([1024, 8])
  b_fc2 = bias_variable([8])
  
  y_conv = tf.matmul(h_fc1_drop, w_fc2) + b_fc2
  
  cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv, y_placeholder))
  train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
  sess = tf.InteractiveSession()
  sess.run(tf.global_variables_initializer())
  batch = (train_feature, np.eye(8)[train_label])
  train_step.run(feed_dict={x_placeholder: batch[0], y_placeholder: batch[1], keep_prob: 0.5})
  </code></pre>
  
  <p>Error is following:</p>
  
  <pre><code>cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv, y_placeholder))
  File "/Users/lintseju/anaconda/lib/python3.5/site-packages/tensorflow/python/ops/nn_ops.py", line 1449, in softmax_cross_entropy_with_logits
  precise_logits, labels, name=name)
  File "/Users/lintseju/anaconda/lib/python3.5/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 2265, in _softmax_cross_entropy_with_logits
  features=features, labels=labels, name=name)
  File "/Users/lintseju/anaconda/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 759, in apply_op
  op_def=op_def)
  File "/Users/lintseju/anaconda/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2240, in create_op
  original_op=self._default_original_op, op_def=op_def)
  File "/Users/lintseju/anaconda/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1128, in __init__
  self._traceback = _extract_stack()
  
  InvalidArgumentError (see above for traceback): logits and labels must be same size: logits_size=[2400,8] labels_size=[10,8]
   [[Node: SoftmaxCrossEntropyWithLogits = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](Reshape_2, Reshape_3)]]
  </code></pre>
  
  <p>train_feature is (10, 19200) numpy array, and train label is (10,) numpy array. Anyone know why logits_size=[2400,8] ?</p>