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The easiest approach to give more importance to a group of specific training instances is simply using a weight to increase the error loss computed on those specific instances during training. Libraries like sklearn have implemented off the shelf the sample_weight parameter to perform precisely this. The obvious downside of this approach is that you need to ...


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I have found the free Python library remo. It's labeling soft for Image Classification, Image Detection, and Instance Segmentation tasks. Firstly you have to install the library: pip install remo then init and start: python -m remo_app init python -m remo_app The Django server will be hosted and you can start labeling via opening the http://localhost:8123 ...


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The model seems to train just fine (when measured by the MSE loss), accuracy metric is only relevant when the prediction is a true / false type. Arguably the network's structure isn't ideal for this problem, but that is beside the point.


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In simple terms, without this transformation the network doesn't know that 2022-01-01 data likely correlates with the January 1st on previous (and future!) years. And actually it is likely to also correlate with January 3rd, and maybe even December 29th. It is also possible to encode the day of year as float between 0.0 and 1.0 (first day of year and last ...


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I have found the answer to this doubt I had. Here, 0.007709330413490534 = 1 / S, q = input, Z = 3. Basically, this is the formula to quantize the input value. If you pull out 1/S then it becomes clear. Here, is an article related to this topic.


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I was able to run the code without "any" modifications on Tensorflow 2.4.0, just had to replace the imports: import keras from keras.datasets import mnist ... -> import tensorflow.keras as keras from tensorflow.keras.datasets import mnist ... Output: Epoch 1/12 469/469 [==============================] - 4s 7ms/step - loss: 2.2914 - accuracy: 0....


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According to my experience, it is possible to reach 99%+ accuracy on MNIST within a few epochs using a simple CNN. MNIST is really an easy dataset. So, it's likely that you've broken something as you're modifying author's code.


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Sorry for my weak English. Your are using neural network to forecast times series which often have irregular fluctuations. Stock values are volatile and have changing frequency. Applying periodic encodings to original data makes it easier to capture frequency information. Read this paper to understand why it is essential for the neural network to know about ...


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