`output_dim` should be a positive integer. Given: (param1).
Package:
tensorflow
158813

Exception Class:
ValueError
Raise code
output_dim,
kernel_initializer='gaussian',
scale=None,
trainable=False,
name=None,
**kwargs):
if output_dim <= 0:
raise ValueError(
'`output_dim` should be a positive integer. Given: {}.'.format(
output_dim))
if isinstance(kernel_initializer, str):
if kernel_initializer.lower() not in _SUPPORTED_RBF_KERNEL_TYPES:
raise ValueError(
'Unsupported kernel type: \'{}\'. Supported kernel types: {}.'
.format(kernel_initializer, _SUPPORTED_RBF_KERNEL_TYPES))
if
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Links to the raise (1)
https://github.com/tensorflow/tensorflow/blob/ba1d02cfc7730048a39e4ba2ad9d3e3863e7cb2f/tensorflow/python/keras/layers/kernelized.py#L154See also in the other packages (1)
(✅️ Fixed)
keras/output-dim-should-be-a-positive-in
Ways to fix
RandomFourierFeatures is a layer that projects its inputs into a random feature space. When initializing this layer the parameter output_dim
should be a positive integer. If a negative integer is given it causes this error.
Reproducing the error:
from tensorflow.keras.layers.experimental import RandomFourierFeatures
random_features_layer = RandomFourierFeatures(output_dim=-500,
scale=10)
Output error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-26-071d2f2887cb> in <module>()
1 from tensorflow.keras.layers.experimental import RandomFourierFeatures
----> 2 random_features_layer = RandomFourierFeatures(output_dim=-500,scale=10)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/layers/kernelized.py in __init__(self, output_dim, kernel_initializer, scale, trainable, name, **kwargs)
154 raise ValueError(
155 '`output_dim` should be a positive integer. Given: {}.'.format(
--> 156 output_dim))
157 if isinstance(kernel_initializer, str):
158 if kernel_initializer.lower() not in _SUPPORTED_RBF_KERNEL_TYPES:
ValueError: `output_dim` should be a positive integer. Given: -500.
Fixed version of the code:
from tensorflow.keras.layers.experimental import RandomFourierFeatures
random_features_layer = RandomFourierFeatures(output_dim=500,
scale=10)
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