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When to get invalidargumenterror when using incompatible shapes?
Even when you set the batch size as 1, you would get the error “InvalidArgumentError: Incompatible shapes” while evaluating the model. It should have raised an error at the time of training process.
Is there an incompatible shapes error in keras?
Running keras in Rstudio, adam optimizer, binary_crossentropy for loss, and sparse_categorical_accuracy metric. The Incompatible shapes error is always [batchsize*final_layer_units] vs. [batchsize]. My code below is not reproducible, but I’m not sure if the resulting error message will be helpful.
What does invalidargumenterror mean in Python 2.7?
Even when you set the batch size as 1, you would get the error “InvalidArgumentError: Incompatible shapes” while evaluating the model. It should have raised an error at the time of training process. I am able to execute the code for python 3.x. It’s the issue with python 2.7 version.
Why is my model shape not valid in Python?
The problem is that your model input batch_input_shape is fixed. The length of your test length is 1257 and cannot be divisible by 32. It should be changed as follows: You should modify test shape before the model evaluate test. Of course, you have to reshape predicted and y_test before inverse_transform.
How to fix Stack Overflow invalidargumenterror [ 32 ]?
Changing maxpool2d to globalaevaragepool solved the issue. If you want to stick to maxpool, then change it’s output shape. Thanks for contributing an answer to Stack Overflow! Please be sure to answer the question. Provide details and share your research! But avoid … Asking for help, clarification, or responding to other answers.
Why is batchsize 32 not an invalid argument?
The cause I think should be my dataset is larger, and if max_iters is still set to 200, it nearly can not complete one epoch with batchsize 32. This issue is not with the batch size. You will always have this problem anytime but rather you have targets which are greater than outputs_per_step*max_iters.