logits and labels must be same size: logits_size=[20,2] labels_size=[10,2]

[Xavier]

Hello,

I try to build a structure from the depp mnist example. Here is the rogram I wrote:

def deepnn(x):

  with tf.name_scope('reshape'):

    x_image = tf.reshape(x, [-1, 224, 172, 1])#(x, [-1, 28, 28, 1])

  # First convolutional layer - maps one grayscale image to 32 feature maps.

  with tf.name_scope('conv1'):

    W_conv1 = weight_variable([5, 5, 1, 32])#([5, 5, 1, 32])

    b_conv1 = bias_variable([32])

    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

  # Pooling layer - downsamples by 2X.

  with tf.name_scope('pool1'):

    h_pool1 = max_pool_2x2(h_conv1)

  # Second convolutional layer -- maps 32 feature maps to 64.

  with tf.name_scope('conv2'):

    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)

  # Second pooling layer.

  with tf.name_scope('pool2'):

    h_pool2 = max_pool_2x2(h_conv2)

  # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image

  # is down to 7x7x64 feature maps -- maps this to 1024 features.

  with tf.name_scope('fc1'):

    W_fc1 = weight_variable([28 * 43 * 64, 1024])

    b_fc1 = bias_variable([1024])

    h_pool2_flat = tf.reshape(h_pool2, [-1, 28*43*64])

    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

  # Dropout - controls the complexity of the model, prevents co-adaptation of

  # features.

  with tf.name_scope('dropout'):

    keep_prob = tf.placeholder(tf.float32)

    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

  # Map the 1024 features to 10 classes, one for each digit

  with tf.name_scope('fc2'):

    W_fc2 = weight_variable([1024, 2])

    b_fc2 = bias_variable([2])

    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2

  return y_conv, keep_prob

def conv2d(x, W):

  """conv2d returns a 2d convolution layer with full stride."""

  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

def max_pool_2x2(x):

  """max_pool_2x2 downsamples a feature map by 2X."""

  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],

                        strides=[1, 2, 2, 1], padding='SAME')

def weight_variable(shape):

  """weight_variable generates a weight variable of a given shape."""

  initial = tf.truncated_normal(shape, stddev=0.1)

  return tf.Variable(initial)

def bias_variable(shape):

  """bias_variable generates a bias variable of a given shape."""

  initial = tf.constant(0.1, shape=shape)

  return tf.Variable(initial)

def main(_):

  # Import data

  V0Dataset = dr.read_data_sets(FLAGS.data_dir, one_hot=True)

  # Create the model

  x = tf.placeholder(tf.float32, [None, 38528])#224*172])

  # Define loss and optimizer

  y_ = tf.placeholder(tf.float32, [None, 2])

  print("logits shape {}".format(y_))

  # Build the graph for the deep net

  y_conv, keep_prob = deepnn(x)

  with tf.name_scope('loss'):

    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_,

                                                            logits=y_conv)

  cross_entropy = tf.reduce_mean(cross_entropy)

  with tf.name_scope('adam_optimizer'):

    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

  with tf.name_scope('accuracy'):

    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))

    correct_prediction = tf.cast(correct_prediction, tf.float32)

  accuracy = tf.reduce_mean(correct_prediction)

  graph_location = tempfile.mkdtemp()

  print('Saving graph to: %s' % graph_location)

  train_writer = tf.summary.FileWriter("/tmp/tensorflow/")

  train_writer.add_graph(tf.get_default_graph())

  with tf.Session() as sess:

    sess.run(tf.global_variables_initializer())

    for i in range(100):

      batch = V0Dataset.train.next_batch(10)

      a =  batch[1];

      a = a.reshape(10,2)

      print("a {}".format(a))

      print("y_ {}".format(y_))

      print("y_conv {}".format(y_))

      fd = feed_dict={x: batch[0], y_: a, keep_prob: 0.5}

      print("fd {}".format(fd))

      train_step.run(feed_dict={x: batch[0], y_: a, keep_prob: 0.5})

This is the error I get:

2018-01-17 09:52:53.481856: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA

start 0  batch_size 10

a [[ 0.  1.]

 [ 0.  1.]

 [ 0.  1.]

 [ 0.  1.]

 [ 1.  0.]

 [ 1.  0.]

 [ 0.  1.]

 [ 0.  1.]

 [ 0.  1.]

 [ 0.  1.]]

y_ Tensor("Placeholder_1:0", shape=(?, 2), dtype=float32)

y_conv Tensor("Placeholder_1:0", shape=(?, 2), dtype=float32)

fd {<tf.Tensor 'Placeholder:0' shape=(?, 38528) dtype=float32>: array([[ 0.00392157,  0.00392157,  0.00784314, ...,  0.        ,

         0.        ,  0.        ],

       [ 0.00392157,  0.00392157,  0.00392157, ...,  0.        ,

         0.        ,  0.        ],

       [ 0.00392157,  0.00392157,  0.00392157, ...,  0.        ,

         0.        ,  0.        ],

       ..., 

       [ 0.00392157,  0.00392157,  0.00392157, ...,  0.        ,

         0.        ,  0.        ],

       [ 0.00392157,  0.00784314,  0.00392157, ...,  0.00392157,

         0.        ,  0.        ],

       [ 0.00392157,  0.00392157,  0.00392157, ...,  0.00392157,

         0.        ,  0.        ]], dtype=float32), <tf.Tensor 'Placeholder_1:0' shape=(?, 2) dtype=float32>: array([[ 0.,  1.],

       [ 0.,  1.],

       [ 0.,  1.],

       [ 0.,  1.],

       [ 1.,  0.],

       [ 1.,  0.],

       [ 0.,  1.],

       [ 0.,  1.],

       [ 0.,  1.],

       [ 0.,  1.]], dtype=float32), <tf.Tensor 'dropout/Placeholder:0' shape=<unknown> dtype=float32>: 0.5}

Traceback (most recent call last):

  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1323, in _do_call

    return fn(*args)

  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1302, in _run_fn

    status, run_metadata)

  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/errors_impl.py", line 473, in __exit__

    c_api.TF_GetCode(self.status.status))

tensorflow.python.framework.errors_impl.InvalidArgumentError: logits and labels must be same size: logits_size=[20,2] labels_size=[10,2]

[[Node: loss/SoftmaxCrossEntropyWithLogits = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](loss/Reshape, loss/Reshape_1)]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):

  File "./deep_charging_station_train.py", line 245, in <module>

    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/platform/app.py", line 48, in run

    _sys.exit(main(_sys.argv[:1] + flags_passthrough))

  File "./deep_charging_station_train.py", line 234, in main

    train_step.run(feed_dict={x: batch[0], y_: a, keep_prob: 0.5})

  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 2042, in run

    _run_using_default_session(self, feed_dict, self.graph, session)

  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 4490, in _run_using_default_session

    session.run(operation, feed_dict)

  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 889, in run

    run_metadata_ptr)

  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1120, in _run

    feed_dict_tensor, options, run_metadata)

  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1317, in _do_run

    options, run_metadata)

  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1336, in _do_call

    raise type(e)(node_def, op, message)

tensorflow.python.framework.errors_impl.InvalidArgumentError: logits and labels must be same size: logits_size=[20,2] labels_size=[10,2]

[[Node: loss/SoftmaxCrossEntropyWithLogits = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](loss/Reshape, loss/Reshape_1)]]

Caused by op 'loss/SoftmaxCrossEntropyWithLogits', defined at:

  File "./deep_charging_station_train.py", line 245, in <module>

    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/platform/app.py", line 48, in run

    _sys.exit(main(_sys.argv[:1] + flags_passthrough))

  File "./deep_charging_station_train.py", line 193, in main

    logits=y_conv)

  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py", line 1783, in softmax_cross_entropy_with_logits

    precise_logits, labels, name=name)

  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 4364, in _softmax_cross_entropy_with_logits

    name=name)

  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper

    op_def=op_def)

  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 2956, in create_op

    op_def=op_def)

  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1470, in __init__

    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

InvalidArgumentError (see above for traceback): logits and labels must be same size: logits_size=[20,2] labels_size=[10,2]

[[Node: loss/SoftmaxCrossEntropyWithLogits = SoftmaxCrossEntropyWithLogits[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](loss/Reshape, loss/Reshape_1)]]

I don't understand why there are these errors. This is exactly the same example  as the deep_mnist.py, I just change the input size and multiple the hidden layer to correspond to the input data. 

I need help please .

[omid erfanmanesh]

i have the same problem if you understand please share with me 

[bd.cuth...@gmail.com]

The error is what it says - "logits and labels must be same size: logits_size=[20,2] labels_size=[10,2]"

It's because when you call this line to calculate the cross entropy.


cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv)

It's failing because the logits tensor has a shape of [20,2], but your labels tensor has a shape of [10,2]. They need to be the same shape.

I suspect the error is being caused by this

      a =  batch[1];

      a = a.reshape(10,2)

      fd = feed_dict={x: batch[0], y_: a, keep_prob: 0.5}

y_ is being reshaped from batch[1], but x is the raw unchanged batch[0]

Check the shapes of y_ and x that are in the feed_dict to make sure they have the same shape.

Brett.