Veeranjaneyulu Ch
Veeranjaneyulu Ch
@mortezamg63, I did change in `batch_size` to `64` both in `parameters['batch_size'] `and `mlp_parameters['batch_size']` and now i can see performance almost same. Please find the gist [here](https://colab.research.google.com/gist/chunduriv/9ab96dd069ac8ca434ad62131b581621/16132.ipynb) for reference.Thanks!
@mortezamg63, Glad the issue is resolved for you, please feel free to move this to closed status. Thanks!
When we run the code on the CPU, we get `InvalidArgumentError` as shown below ``` import tensorflow as tf x = tf.random.normal(shape=[1,5,1]) y = tf.keras.layers.Conv1DTranspose(filters=3,kernel_size=[3,],strides=(2,),dilation_rate=(2,),padding="same",) print(y(x).shape) ``` Output: ``` ---------------------------------------------------------------------------...
@old-school-kid, Yes, it is working as expected on `CPU`, but it is a bug on `GPU`.
@ravinderkhatri, In order to reproduce the issue reported here, could you please provide the complete code? Thanks!
@ravinderkhatri, Can you please share `label_map.pbtxt` and other supporting files if any to replicate above issue? Thanks!
@sachinprasadhs, I was able to reproduce the issue on Colab using `TF2.7` and `tf-nightly(2.8.0-dev20211201)`. Please find the [gist here](https://colab.research.google.com/gist/chunduriv/f0cd2210dfa4d1462a0c518c5a370848/15708.ipynb#scrollTo=UKhWhvRLVvmp) for reference.Thanks!
@stefanistrate, In general, `layers.concatenate` can be used to merge all available features into a single large vector. Why are using `tf.keras.layers.Concatenate()` layer? Can you please brief about your use case?
The issue was able to reproduce on colab using `TF-nightly 2.11.0-dev20220921`, Please find the [gist](https://colab.sandbox.google.com/gist/chunduriv/b54d6cbd91bc05996171cae3f302b873/16314.ipynb) here for reference. Thank you
@robertatdm, Sure, Please go ahead and close this issue.