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How to perform Multi-Label Image Classification with EfficientNet

Your code looks correct. In particular, you are correct to use sigmoid activation (since you want multi-hot outputs, using softmax would not make sense) and binary_cross_entropy (this may seem counter-...
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Loss decreases, but Validation Loss just fluctuates

You have 250 images as training set, and you are using a model with millions of parameters... I'm pretty sure that your model is just memorizing the training set, aka you are overfitting. At this ...
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Loss decreases, but Validation Loss just fluctuates

It looks like your model is overfitting: it's learning from the training dataset, but this learning doesn't apply to the test dataset. You can try to reduce the complexity of the model by simplifying ...
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High level-Low Level features in U-NET

To answer your question let's first go through how CNN works. When we give a CNN an input image, it sees an array of numbers that correspond to the pixel intensities of the input image. The intensity ...
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ValueError: cannot reshape array of size 36276416 into shape (96,227,227,1)

It is normal that it can't be reshape, because: 36276416 / (96227227*1) = 36276416 / 4946784 = 7.33333333 which is not an integer result. Maybe there is a problem with some images' size or color ...
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How to deal with different amounts of data every day?

One common option is to aggregate the embeddings of all the headlines. For example, you can compute the average of the embeddings and use the resulting 300-dim vector as input of the model.
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how does convolutional layer work?

Usually, the expected input to a 2D convolutional layer is an image of size (width, height, channels). A filter is applied to each channel of this input image separately and the result is one 2d ...
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how does convolutional layer work?

A single convolutional layer has multiple filters and each one is different from the others. Therefore, a single convolutional layer can detect multiple features.
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