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How to fix type error using tensorflow data API for rejection sampling
How to determine a Python variable's type?Object detection error on tensorflow 1.0, Python3.5, Anaconda and WIndows10How to use Dataset API to read TFRecords file of lists of variant length?How to convert keras float predictions to int?Tensorflow error: ValueError: None values not supportedINFO:tensorflow:Error reported to CoordinatorTensorflow model as service gives errorValue Error Tensorflow While Loading the Machine Learning project in Djangotensorflow tf.maximum(0, x) returns errorValueError: Argument must be a dense tensor when using estimator.predict
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0
I am using the tensorflow data API to try and do some rejection sampling for my unbalanced data set.
I have run the code on my personal computer and it seems to work as I expect it to, however, when I run the code on my University's cluster I get a type error that I can't seem to understand. I have tried recasting and I get the same error.
I am still learning how to use this API and I'm still not 100% clear on if this is the best way to achieve what I want, so I also welcome any advice on how I implemented the rejection sampling (this could very well be the reason why I get error since I don't fully understand yet).
This is how I am loading in the data to the dataset:
data = np.loadtxt("my_data.dat")
features = data[:, 1:10]
labels = data[:, 0]
labels[labels == -1] = 0
assert features.shape[0] == labels.shape[0]
dataset_size = len(features)
dataset = tf.data.Dataset.from_tensor_slices((features.astype('float32'),
labels.astype('int32')))
dataset = dataset.shuffle(buffer_size=dataset_size)
the error occurs when I read here:
train_size = int((2/3.0)*dataset_size)
tr_dataset = dataset.take(train_size)
tr_dataset = (tr_dataset.apply(
tf.contrib.data.rejection_resample(
class_func=lambda _, c: c, target_dist=[0.5, 0.5],
seed=42)).map(lambda a, b: b)).batch(100)
This is the error:
Traceback (most recent call last):
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 510, in _apply_op_helper
preferred_dtype=default_dtype)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1094, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 931, in _TensorTensorConversionFunction
(dtype.name, t.dtype.name, str(t)))
ValueError: Tensor conversion requested dtype int32 for Tensor with dtype int64: 'Tensor("Sum:0", shape=(2,), dtype=int64)'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "test.py", line 185, in <module>
seed=42))).batch(100)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 1109, in apply
dataset = transformation_func(self)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/resampling.py", line 74, in _apply_fn
target_dist_t, class_values_ds)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/resampling.py", line 183, in _estimate_initial_dist_ds
update_estimate_and_tile))
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 1109, in apply
dataset = transformation_func(self)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/scan_ops.py", line 172, in _apply_fn
return _ScanDataset(dataset, initial_state, scan_func)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/scan_ops.py", line 74, in __init__
add_to_graph=False)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 1459, in __init__
self._function._create_definition_if_needed() # pylint: disable=protected-access
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/function.py", line 337, in _create_definition_if_needed
self._create_definition_if_needed_impl()
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/function.py", line 346, in _create_definition_if_needed_impl
self._capture_by_value, self._caller_device)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/function.py", line 863, in func_graph_from_py_func
outputs = func(*func_graph.inputs)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 1392, in tf_data_structured_function_wrapper
ret = func(*nested_args)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/resampling.py", line 176, in update_estimate_and_tile
c, num_examples_per_class_seen)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/resampling.py", line 212, in _estimate_data_distribution
array_ops.one_hot(c, num_classes, dtype=dtypes.int64), 0))
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/ops/gen_math_ops.py", line 297, in add
"Add", x=x, y=y, name=name)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 546, in _apply_op_helper
inferred_from[input_arg.type_attr]))
TypeError: Input 'y' of 'Add' Op has type int64 that does not match type int32 of argument 'x'.
python tensorflow types tensor tensorflow-datasets
asked Mar 22 at 18:21
tinman248tinman248
10318
add a comment |
0
I am using the tensorflow data API to try and do some rejection sampling for my unbalanced data set.
I have run the code on my personal computer and it seems to work as I expect it to, however, when I run the code on my University's cluster I get a type error that I can't seem to understand. I have tried recasting and I get the same error.
I am still learning how to use this API and I'm still not 100% clear on if this is the best way to achieve what I want, so I also welcome any advice on how I implemented the rejection sampling (this could very well be the reason why I get error since I don't fully understand yet).
This is how I am loading in the data to the dataset:
data = np.loadtxt("my_data.dat")
features = data[:, 1:10]
labels = data[:, 0]
labels[labels == -1] = 0
assert features.shape[0] == labels.shape[0]
dataset_size = len(features)
dataset = tf.data.Dataset.from_tensor_slices((features.astype('float32'),
labels.astype('int32')))
dataset = dataset.shuffle(buffer_size=dataset_size)
the error occurs when I read here:
train_size = int((2/3.0)*dataset_size)
tr_dataset = dataset.take(train_size)
tr_dataset = (tr_dataset.apply(
tf.contrib.data.rejection_resample(
class_func=lambda _, c: c, target_dist=[0.5, 0.5],
seed=42)).map(lambda a, b: b)).batch(100)
This is the error:
Traceback (most recent call last):
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 510, in _apply_op_helper
preferred_dtype=default_dtype)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1094, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 931, in _TensorTensorConversionFunction
(dtype.name, t.dtype.name, str(t)))
ValueError: Tensor conversion requested dtype int32 for Tensor with dtype int64: 'Tensor("Sum:0", shape=(2,), dtype=int64)'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "test.py", line 185, in <module>
seed=42))).batch(100)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 1109, in apply
dataset = transformation_func(self)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/resampling.py", line 74, in _apply_fn
target_dist_t, class_values_ds)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/resampling.py", line 183, in _estimate_initial_dist_ds
update_estimate_and_tile))
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 1109, in apply
dataset = transformation_func(self)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/scan_ops.py", line 172, in _apply_fn
return _ScanDataset(dataset, initial_state, scan_func)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/scan_ops.py", line 74, in __init__
add_to_graph=False)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 1459, in __init__
self._function._create_definition_if_needed() # pylint: disable=protected-access
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/function.py", line 337, in _create_definition_if_needed
self._create_definition_if_needed_impl()
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/function.py", line 346, in _create_definition_if_needed_impl
self._capture_by_value, self._caller_device)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/function.py", line 863, in func_graph_from_py_func
outputs = func(*func_graph.inputs)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 1392, in tf_data_structured_function_wrapper
ret = func(*nested_args)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/resampling.py", line 176, in update_estimate_and_tile
c, num_examples_per_class_seen)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/resampling.py", line 212, in _estimate_data_distribution
array_ops.one_hot(c, num_classes, dtype=dtypes.int64), 0))
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/ops/gen_math_ops.py", line 297, in add
"Add", x=x, y=y, name=name)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 546, in _apply_op_helper
inferred_from[input_arg.type_attr]))
TypeError: Input 'y' of 'Add' Op has type int64 that does not match type int32 of argument 'x'.
python tensorflow types tensor tensorflow-datasets
asked Mar 22 at 18:21
tinman248tinman248
10318
Does the versions of tf differ from your local and your cluster? And from line 176 and 212 of resample.py pointed out in your error trace, I think converting to int32 is the problem for the second exception.
– kvish
Mar 23 at 0:33
@kvish Yes, on my local its 1.13 and on my cluster its 1.10. I have tried not casting and I get the error that the only supported types are uint8, int32, int64. I have tried using the "other" supported types and I get the same error as in the post. I don't really know where to go from here
– tinman248
Mar 26 at 11:59
UPDATE: I have upgraded the tensorflow version on the cluster to 1.13 (the version on my local machine) and I get the same error...
– tinman248
Mar 26 at 12:28
this is very weird. Could this be an issue in the sense that you might need to pass dtype compatible with numpy or tf? As in you can use tf.cast to cast your data types, or use np.int32 etc?
– kvish
Mar 26 at 21:01
add a comment |
0
0
0
I am using the tensorflow data API to try and do some rejection sampling for my unbalanced data set.
I have run the code on my personal computer and it seems to work as I expect it to, however, when I run the code on my University's cluster I get a type error that I can't seem to understand. I have tried recasting and I get the same error.
I am still learning how to use this API and I'm still not 100% clear on if this is the best way to achieve what I want, so I also welcome any advice on how I implemented the rejection sampling (this could very well be the reason why I get error since I don't fully understand yet).
This is how I am loading in the data to the dataset:
data = np.loadtxt("my_data.dat")
features = data[:, 1:10]
labels = data[:, 0]
labels[labels == -1] = 0
assert features.shape[0] == labels.shape[0]
dataset_size = len(features)
dataset = tf.data.Dataset.from_tensor_slices((features.astype('float32'),
labels.astype('int32')))
dataset = dataset.shuffle(buffer_size=dataset_size)
the error occurs when I read here:
train_size = int((2/3.0)*dataset_size)
tr_dataset = dataset.take(train_size)
tr_dataset = (tr_dataset.apply(
tf.contrib.data.rejection_resample(
class_func=lambda _, c: c, target_dist=[0.5, 0.5],
seed=42)).map(lambda a, b: b)).batch(100)
This is the error:
Traceback (most recent call last):
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 510, in _apply_op_helper
preferred_dtype=default_dtype)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1094, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 931, in _TensorTensorConversionFunction
(dtype.name, t.dtype.name, str(t)))
ValueError: Tensor conversion requested dtype int32 for Tensor with dtype int64: 'Tensor("Sum:0", shape=(2,), dtype=int64)'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "test.py", line 185, in <module>
seed=42))).batch(100)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 1109, in apply
dataset = transformation_func(self)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/resampling.py", line 74, in _apply_fn
target_dist_t, class_values_ds)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/resampling.py", line 183, in _estimate_initial_dist_ds
update_estimate_and_tile))
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 1109, in apply
dataset = transformation_func(self)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/scan_ops.py", line 172, in _apply_fn
return _ScanDataset(dataset, initial_state, scan_func)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/scan_ops.py", line 74, in __init__
add_to_graph=False)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 1459, in __init__
self._function._create_definition_if_needed() # pylint: disable=protected-access
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/function.py", line 337, in _create_definition_if_needed
self._create_definition_if_needed_impl()
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/function.py", line 346, in _create_definition_if_needed_impl
self._capture_by_value, self._caller_device)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/function.py", line 863, in func_graph_from_py_func
outputs = func(*func_graph.inputs)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 1392, in tf_data_structured_function_wrapper
ret = func(*nested_args)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/resampling.py", line 176, in update_estimate_and_tile
c, num_examples_per_class_seen)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/resampling.py", line 212, in _estimate_data_distribution
array_ops.one_hot(c, num_classes, dtype=dtypes.int64), 0))
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/ops/gen_math_ops.py", line 297, in add
"Add", x=x, y=y, name=name)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 546, in _apply_op_helper
inferred_from[input_arg.type_attr]))
TypeError: Input 'y' of 'Add' Op has type int64 that does not match type int32 of argument 'x'.
python tensorflow types tensor tensorflow-datasets
asked Mar 22 at 18:21
tinman248tinman248
10318
I am using the tensorflow data API to try and do some rejection sampling for my unbalanced data set.
I have run the code on my personal computer and it seems to work as I expect it to, however, when I run the code on my University's cluster I get a type error that I can't seem to understand. I have tried recasting and I get the same error.
I am still learning how to use this API and I'm still not 100% clear on if this is the best way to achieve what I want, so I also welcome any advice on how I implemented the rejection sampling (this could very well be the reason why I get error since I don't fully understand yet).
This is how I am loading in the data to the dataset:
data = np.loadtxt("my_data.dat")
features = data[:, 1:10]
labels = data[:, 0]
labels[labels == -1] = 0
assert features.shape[0] == labels.shape[0]
dataset_size = len(features)
dataset = tf.data.Dataset.from_tensor_slices((features.astype('float32'),
labels.astype('int32')))
dataset = dataset.shuffle(buffer_size=dataset_size)
the error occurs when I read here:
train_size = int((2/3.0)*dataset_size)
tr_dataset = dataset.take(train_size)
tr_dataset = (tr_dataset.apply(
tf.contrib.data.rejection_resample(
class_func=lambda _, c: c, target_dist=[0.5, 0.5],
seed=42)).map(lambda a, b: b)).batch(100)
This is the error:
Traceback (most recent call last):
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 510, in _apply_op_helper
preferred_dtype=default_dtype)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1094, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 931, in _TensorTensorConversionFunction
(dtype.name, t.dtype.name, str(t)))
ValueError: Tensor conversion requested dtype int32 for Tensor with dtype int64: 'Tensor("Sum:0", shape=(2,), dtype=int64)'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "test.py", line 185, in <module>
seed=42))).batch(100)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 1109, in apply
dataset = transformation_func(self)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/resampling.py", line 74, in _apply_fn
target_dist_t, class_values_ds)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/resampling.py", line 183, in _estimate_initial_dist_ds
update_estimate_and_tile))
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 1109, in apply
dataset = transformation_func(self)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/scan_ops.py", line 172, in _apply_fn
return _ScanDataset(dataset, initial_state, scan_func)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/scan_ops.py", line 74, in __init__
add_to_graph=False)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 1459, in __init__
self._function._create_definition_if_needed() # pylint: disable=protected-access
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/function.py", line 337, in _create_definition_if_needed
self._create_definition_if_needed_impl()
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/function.py", line 346, in _create_definition_if_needed_impl
self._capture_by_value, self._caller_device)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/function.py", line 863, in func_graph_from_py_func
outputs = func(*func_graph.inputs)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/data/ops/dataset_ops.py", line 1392, in tf_data_structured_function_wrapper
ret = func(*nested_args)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/resampling.py", line 176, in update_estimate_and_tile
c, num_examples_per_class_seen)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/contrib/data/python/ops/resampling.py", line 212, in _estimate_data_distribution
array_ops.one_hot(c, num_classes, dtype=dtypes.int64), 0))
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/ops/gen_math_ops.py", line 297, in add
"Add", x=x, y=y, name=name)
File "/home/user/.conda/envs/tensorflowcpu/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 546, in _apply_op_helper
inferred_from[input_arg.type_attr]))
TypeError: Input 'y' of 'Add' Op has type int64 that does not match type int32 of argument 'x'.
python tensorflow types tensor tensorflow-datasets
python tensorflow types tensor tensorflow-datasets
asked Mar 22 at 18:21
tinman248tinman248
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asked Mar 22 at 18:21
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asked Mar 22 at 18:21
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asked Mar 22 at 18:21
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Does the versions of tf differ from your local and your cluster? And from line 176 and 212 of resample.py pointed out in your error trace, I think converting to int32 is the problem for the second exception.
– kvish
Mar 23 at 0:33
@kvish Yes, on my local its 1.13 and on my cluster its 1.10. I have tried not casting and I get the error that the only supported types are uint8, int32, int64. I have tried using the "other" supported types and I get the same error as in the post. I don't really know where to go from here
– tinman248
Mar 26 at 11:59
UPDATE: I have upgraded the tensorflow version on the cluster to 1.13 (the version on my local machine) and I get the same error...
– tinman248
Mar 26 at 12:28
this is very weird. Could this be an issue in the sense that you might need to pass dtype compatible with numpy or tf? As in you can use tf.cast to cast your data types, or use np.int32 etc?
– kvish
Mar 26 at 21:01
add a comment |
Does the versions of tf differ from your local and your cluster? And from line 176 and 212 of resample.py pointed out in your error trace, I think converting to int32 is the problem for the second exception.
– kvish
Mar 23 at 0:33
@kvish Yes, on my local its 1.13 and on my cluster its 1.10. I have tried not casting and I get the error that the only supported types are uint8, int32, int64. I have tried using the "other" supported types and I get the same error as in the post. I don't really know where to go from here
– tinman248
Mar 26 at 11:59
UPDATE: I have upgraded the tensorflow version on the cluster to 1.13 (the version on my local machine) and I get the same error...
– tinman248
Mar 26 at 12:28
this is very weird. Could this be an issue in the sense that you might need to pass dtype compatible with numpy or tf? As in you can use tf.cast to cast your data types, or use np.int32 etc?
– kvish
Mar 26 at 21:01
Does the versions of tf differ from your local and your cluster? And from line 176 and 212 of resample.py pointed out in your error trace, I think converting to int32 is the problem for the second exception.
– kvish
Mar 23 at 0:33
Does the versions of tf differ from your local and your cluster? And from line 176 and 212 of resample.py pointed out in your error trace, I think converting to int32 is the problem for the second exception.
– kvish
Mar 23 at 0:33
@kvish Yes, on my local its 1.13 and on my cluster its 1.10. I have tried not casting and I get the error that the only supported types are uint8, int32, int64. I have tried using the "other" supported types and I get the same error as in the post. I don't really know where to go from here
– tinman248
Mar 26 at 11:59
@kvish Yes, on my local its 1.13 and on my cluster its 1.10. I have tried not casting and I get the error that the only supported types are uint8, int32, int64. I have tried using the "other" supported types and I get the same error as in the post. I don't really know where to go from here
– tinman248
Mar 26 at 11:59
UPDATE: I have upgraded the tensorflow version on the cluster to 1.13 (the version on my local machine) and I get the same error...
– tinman248
Mar 26 at 12:28
UPDATE: I have upgraded the tensorflow version on the cluster to 1.13 (the version on my local machine) and I get the same error...
– tinman248
Mar 26 at 12:28
this is very weird. Could this be an issue in the sense that you might need to pass dtype compatible with numpy or tf? As in you can use tf.cast to cast your data types, or use np.int32 etc?
– kvish
Mar 26 at 21:01
this is very weird. Could this be an issue in the sense that you might need to pass dtype compatible with numpy or tf? As in you can use tf.cast to cast your data types, or use np.int32 etc?
– kvish
Mar 26 at 21:01
add a comment |
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Does the versions of tf differ from your local and your cluster? And from line 176 and 212 of resample.py pointed out in your error trace, I think converting to int32 is the problem for the second exception.
– kvish
Mar 23 at 0:33
@kvish Yes, on my local its 1.13 and on my cluster its 1.10. I have tried not casting and I get the error that the only supported types are uint8, int32, int64. I have tried using the "other" supported types and I get the same error as in the post. I don't really know where to go from here
– tinman248
Mar 26 at 11:59
UPDATE: I have upgraded the tensorflow version on the cluster to 1.13 (the version on my local machine) and I get the same error...
– tinman248
Mar 26 at 12:28
this is very weird. Could this be an issue in the sense that you might need to pass dtype compatible with numpy or tf? As in you can use tf.cast to cast your data types, or use np.int32 etc?
– kvish
Mar 26 at 21:01