CODEWITHSUNDEEP
pythontensorflow
harunuz
harunuz

Reputation: 137

Shape mismacth: shape of labels is incompatible with shape of logits

I am trying to train a model in TensorFlow. I've got a problem with the labels. Here is my input function:

def my_input_fn():
  filenames = tf.constant(glob.glob("C:/test_proje/*.jpg"))
  labels = tf.constant([0, 0, 1, 1, 1, 1, 1, 0, 0, 0])
  labels = tf.one_hot(labels, 2)
  dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))
  dataset = dataset.map(_parse_function)    
  return dataset

And here is the CNN model

def cnn_model_fn(features, labels, mode):
  """Model function for CNN."""
  print(labels.shape)
  print(labels[0])

  # Input Layer
  input_layer = tf.reshape(features["image"], [-1, 168, 84, 3])

  # Convolutional Layer #1
  conv1 = tf.layers.conv2d(
      inputs=input_layer,
      filters=32,
      kernel_size=[5, 5],
      padding="same",
      activation=tf.nn.relu)

  # Pooling Layer #1
  pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2],      
          strides=2)
  # Convolutional Layer #2 and Pooling Layer #2
  conv2 = tf.layers.conv2d(
      inputs=pool1,
      filters=64,
      kernel_size=[5, 5],
      padding="same",
      activation=tf.nn.relu)
  pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2],     
        strides=2)


  # Dense Layer
  pool2_flat = tf.reshape(pool2, [-1, 42 * 21 * 64])
  dense = tf.layers.dense(inputs=pool2_flat, units=4,  
     activation=tf.nn.relu)
  dropout = tf.layers.dropout(
       inputs=dense, rate=0.4, training=mode == 
      tf.estimator.ModeKeys.TRAIN)

 # Logits Layer
  logits = tf.layers.dense(inputs=dropout, units=2)

  predictions = {
     # Generate predictions (for PREDICT and EVAL mode)
     "classes": tf.argmax(input=logits, axis=1),
     # Add `softmax_tensor` to the graph. It is used for PREDICT and by  
       the
     # `logging_hook`.
     "probabilities": tf.nn.softmax(logits, name="softmax_tensor")
  }

  if mode == tf.estimator.ModeKeys.PREDICT:
  return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)

  # Calculate Loss (for both TRAIN and EVAL modes)
  loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, 
        logits=logits)
  # Configure the Training Op (for TRAIN mode)
  if mode == tf.estimator.ModeKeys.TRAIN:
       optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
  train_op = optimizer.minimize(
       loss=loss,
       global_step=tf.train.get_global_step())
  return tf.estimator.EstimatorSpec(mode=mode, loss=loss, 
           train_op=train_op)

  #  Add evaluation metrics (for EVAL mode)
  eval_metric_ops = {
     "accuracy": tf.metrics.accuracy(
       labels=labels, predictions=predictions["classes"])}
  return tf.estimator.EstimatorSpec(
       mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)

I am getting this error: ValueError: Shape mismatch: The shape of labels (received (2,)) should equal the shape of logits except for the last dimension (received (1, 2)).

When I print the shape of labels before cnn_model_fn, it is (10,2). But when I print it in cnn_model_fn, it suddenly becomes (2,)

Thanks.

Upvotes: 1

Views: 7621

Answers (1)

Burton2000
Burton2000

Reputation: 2072

I think it's your use of sparse_softmax_cross_entropy. You feed it one hot encoded labels which it doesn't want.

Switch to just normal softmax_cross_entropy and see if that works.

https://stackoverflow.com/a/37317322/7431458

Upvotes: 8

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