invalidargumenterror: expected dimension in the range [-1, 1) but got 1

For the people coming for Tensorflow serving or Estimator loading, this error occurs because the values in the feature dictionary need to be in batches.

data = {
   "signature_name": "predict",
   "inputs": {
      k: [v]
      for k,
      v in inputs.items()
   }
}

The source code generating this error reads as follows:

OP_REQUIRES(context, axis >= 0 && axis < input_dims,
   errors::InvalidArgument("Expected dimension in the range [",
      -input_dims, ", ", input_dims,
      "), but got ", dim));

For code like

tf.equal(tf.argmax(y, 1), tf.argmax(labels, 1))

which is often used when calculating accuracy, you can change to

tf.equal(tf.argmax(y, -1), tf.argmax(labels, -1))

according to the source code:

// tensorflow/compiler/tf2xla/kernels/index_ops_cpu.cc:58
OP_REQUIRES(ctx, axis >= 0 && axis < input_dims,
   errors::InvalidArgument("Expected dimension in the range [",
      -input_dims, ", ", input_dims,
      "), but got ", dim));

I solved this problem. Check the expression of batch_labels

#
if use one hot code use
# y_true_cls = tf.argmax(y_true, dimension = 1)

#
if not one hot code use
y_true_cls = y_true

Suggestion : 2

解决：Expected dimension in the range [-1, 1), but got 1 ayumifire: 老铁，问下咋改

做分类任务的时候，经常会使用交叉熵函数作为loss来计算：形式如下

def evaluation(logits, labels):
   with tf.variable_scope('accuracy') as scope:
   correct_prediction = tf.equal(tf.argmax(labels, 1), tf.argmax(logits, 1))
correct = tf.cast(correct_prediction, tf.float32)
accuracy = tf.reduce_mean(correct)

return accuracy

如下。。。。。。。。。

correct_prediction = tf.equal(tf.argmax(labels, -1), tf.argmax(logits, -1))

Suggestion : 3

Last updated 2022-06-28 UTC.

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.errors.InvalidArgumentError

tf.errors.InvalidArgumentError(
   node_def, op, message, * args
)

Example:

tf.reshape([1, 2, 3], (2, ))
Traceback(most recent call last):

   InvalidArgumentError: ...

Suggestion : 4

[r/python] Python Tensorflow tf.concat raising strange error when concatenating 2 tensors,I don't know if this is the appropriate subreddit or if i should post it in the python subreddit also but when attempting to run my implementation for my thesis i get this strange error.,The code that is causing this error is below.,I'm a bot, bleep, bloop. Someone has linked to this thread from another place on reddit:

Traceback (most recent call last):
File "D:\School\Last_Year\thesis_actual\thesis\thesis_prototype\implementation.py", line 314, in <module>
   new_tensor = tf.concat([tensor,array_tensor],axis=2)
   File "D:\School\Last_Year\thesis_actual\thesis\thesis_prototype\venv\lib\site-packages\tensorflow\python\util\dispatch.py", line 180, in wrapper
   return target(*args, **kwargs)
   File "D:\School\Last_Year\thesis_actual\thesis\thesis_prototype\venv\lib\site-packages\tensorflow\python\ops\array_ops.py", line 1271, in concat
   return gen_array_ops.concat_v2(values=values, axis=axis, name=name)
   File "D:\School\Last_Year\thesis_actual\thesis\thesis_prototype\venv\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 1378, in concat_v2
   _six.raise_from(_core._status_to_exception(e.code, message), None)
   File "<string>", line 3, in raise_from
      tensorflow.python.framework.errors_impl.InvalidArgumentError: ConcatOp : Expected concatenating dimensions in the range [0, 0), but got 2 [Op:ConcatV2] name: concat

The code that is causing this error is below.

new_validation = copy.deepcopy(validation)
float_validation = [
   []
]
for data in new_validation:
   new_ident = float(data[0])
new_converted_values = []
for values in data[1][0]:
   new_converted_values.append(float(values))
new_converted_values = np.asarray(new_converted_values)
float_validation.append((new_ident, new_converted_values))
float_validation.pop(0)

tensor = None
array_tensor = None
dummy_tensor = tf.convert_to_tensor(0.0, dtype = tf.float32)
dummy_tensor_2 = tf.convert_to_tensor(0.0, dtype = tf.float32)
dataset = tf.data.Dataset.from_tensors(tensors = (dummy_tensor, dummy_tensor_2))
for row in float_validation:
   print(row[0])
tensor = tf.convert_to_tensor(row[0], dtype = tf.float32)
array_tensor = tf.convert_to_tensor(row[1], dtype = tf.float32)
new_tensor = tf.concat([tensor, array_tensor], axis = 2)
# dataset = dataset.concatenate(tf.data.Dataset.from_tensors(tensors = (tensor, array_tensor)))
dataset = dataset.concatenate(tf.data.Dataset.from_tensors(tensors = (new_tensor)))

Suggestion : 5

The MySQL server writes some error messages to its error log, and sends others to client programs. , Message: Slave is not configured or failed to initialize properly. You must at least set --server-id to enable either a master or a slave. Additional error messages can be found in the MySQL error log. , In addition to the errors in the following list, the server can also produce error messages that have error codes in the range from 1 to 999. See Chapter 4, Global Error Message Reference , InnoDB reports this error when a table cannot be created. If the error message refers to error 150, table creation failed because a foreign key constraint was not correctly formed. If the error message refers to error −1, table creation probably failed because the table includes a column name that matched the name of an internal InnoDB table.

% s = column name %
   s = location of column(
      for example, "field list")

Examples:

mysql > SELECT i FROM t INNER JOIN t AS t2;
ERROR 1052(23000): Column 'i' in field list is ambiguous

mysql > SELECT * FROM t LEFT JOIN t AS t2 ON i = i;
ERROR 1052(23000): Column 'i' in on clause is ambiguous

Qualify the column with the appropriate table name:

mysql > SELECT t2.i FROM t INNER JOIN t AS t2;

Qualify the column with the appropriate table name:

mysql > SELECT t2.i FROM t INNER JOIN t AS t2;

Modify the query to avoid the need for qualification:

mysql > SELECT * FROM t LEFT JOIN t AS t2 USING(i);