optimizer | The finest Windows Optimizer

All rows need the same data size, of course some values can be empty in csv.

This seems to be a bug in Tensorflow 2.7 when using model.save combined with the parameter save_format="tf", which is set by default. The layers RandomFlip, RandomRotation, RandomZoom, and RandomContrast are causing the problems, since they are not serializable. Interestingly, the Rescaling layer can be saved without any problems. A workaround would be to simply save your model with the older Keras H5 format model.save("test", save_format='h5'):

Just remove the "extractCss": true from your production environment, it will resolve the problem.

The reason about it is extractCss is deprecated, and it's value is true by default. See more here: Extracting CSS into JS with Angular 11 (deprecated extractCss)

Allocation elision is an optimization that is outside of and in addition to the as-if rule. Another optimization with the same properties is copy elision (not to be confused with mandatory elision, since C++17): Is it legal to elide a non-trivial copy/move constructor in initialization?.

If the hyperspectral dataset is given to you as a large image with many channels, I suppose that the classification of each pixel should depend on the pixels around it (otherwise I would not format the data as an image, i.e. without grid structure). Given this assumption, breaking up the input picture into 1x1 parts is not a good idea as you are loosing the grid structure.

I further suppose that the order of the channels is arbitrary, which implies that convolution over the channels is probably not meaningful (which you however did not plan to do anyways).

Instead of reformatting the data the way you did, you may want to create a model that takes an image as input and also outputs an "image" containing the classifications for each pixel. I.e. if you have 10 classes and take a (145, 145, 200) image as input, your model would output a (145, 145, 10) image. In that architecture you would not have any fully-connected layers. Your output layer would also be a convolutional layer.

That however means that you will not be able to keep your current architecture. That is because the tasks for MNIST/CIFAR10 and your hyperspectral dataset are not the same. For MNIST/CIFAR10 you want to classify an image in it's entirety, while for the other dataset you want to assign a class to each pixel (while most likely also using the pixels around each pixel).

Some further ideas:

• If you want to turn the pixel classification task on the hyperspectral dataset into a classification task for an entire image, maybe you can reformulate that task as "classifying a hyperspectral image as the class of it's center (or top-left, or bottom-right, or (21th, 104th), or whatever) pixel". To obtain the data from your single hyperspectral image, for each pixel, I would shift the image such that the target pixel is at the desired location (e.g. the center). All pixels that "fall off" the border could be inserted at the other side of the image.
• If you want to stick with a pixel classification task but need more data, maybe split up the single hyperspectral image you have into many smaller images (e.g. 10x10x200). You may even want to use images of many different sizes. If you model only has convolution and pooling layers and you make sure to maintain the sizes of the image, that should work out.

using OneHotEncoder is not the way to go here, it's better to extract the features from the column time as separate features like year, month, day, hour, minutes etc... and give these columns as input to your model.

Need to down-grade to Keras 1.0.2 If works then great, otherwise I will tell other solution.

I solved with numpy downgrade to 1.18.5

TensorFlow has 2 execution modes: eager execution, and graph mode. TensorFlow default behavior, since version 2, is to default to eager execution. Eager execution is great as it enables you to write code close to how you would write standard python. It's easier to write, and it's easier to debug. Unfortunately, it's really not as fast as graph mode.

So the idea is, once the function is prototyped in eager mode, to make TensorFlow execute it in graph mode. For that you can use tf.function. tf.function compiles a callable into a TensorFlow graph. Once the function is compiled into a graph, the performance gain is usually quite important. The recommended approach when developing in TensorFlow is the following:

• Debug in eager mode, then decorate with @tf.function.
• Don't rely on Python side effects like object mutation or list appends.
• tf.function works best with TensorFlow ops; NumPy and Python calls are converted to constants.

I would add: think about the critical parts of your program, and which ones should be converted first into graph mode. It's usually the parts where you call a model to get a result. It's where you will see the best improvements.

You can find more information in the following guides:

• Better performance with tf.function
• Introduction to graphs and tf.function

So, there are at least two things you can change in your code to make it run quite faster:

1. The first one is to not use model.predict on a small amount of data. The function is made to work on a huge dataset or on a generator. (See this comment on Github). Instead, you should call the model directly, and for performance enhancement, you can wrap the call to the model in a tf.function.

Model.predict is a top-level API designed for batch-predicting outside of any loops, with the fully-features of the Keras APIs.

1. The second one is to make your training step a separate function, and to decorate that function with @tf.function.

So, I would declare the following things before your training loop: