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The idea of splitting 80%-20% is to use as much data as possible for training while keeping enough (labeled) data in the test set in order to reliably evaluate the performance. It's fine to use a smaller test set if it's big enough, but if it's too small the evaluation might not reflect the real performance of the model. Your idea of using future test data ...


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It seems like you are adding nodes to your computational graph in this line: layer = layer.assign(evolved_layer). The assign-operation is just as multiplication or addition a node in the graph and you construct a new one in every step. Call tf.Graph.finalize() after defining your model. This will raise an error whenever a node is added to the graph and help ...


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With BERT I am assuming you are using finally the embeddings for your task. Solution 1: Once you have embeddings, you can use them as features and with your other features and then build a new model for the task. Solution 2: Here you will play with the network. Now here left one is the normal BERT, in the right we have another MLP network to deal with ...


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please make your data like this format to work with flow_from_directory.


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after creating the model you can create another model as below ( I created model till 8 layers) model = Model(vgg19.input, vgg19.layers[8].output) model.save('./style_transfer/st.h5') You can also use post-training Quantization techniques to reduce the size of the model to deploy in mobile/IoT devices. please check tensorflow documentation here


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My doubt is whether the resize would now change the position of my object according to annotation? Yes, it will. should i annotate on the resized images(resize them myslef before training?) No, you should annotate at the original size. You solve this by applying the corresponding transformations on your bounding boxas well. So if you resize your ...


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Your way seems to be correct. I would suggest one other way that you might want to try (maybe it won't make your life better, but still): Since you want your data points to basically live on a sphere, you could train the angles in spherical coordinates. Fix the radius to $\sqrt{n}$ and this way your network has to learn one dimension fewer. I'm not sure how ...


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keras will differentiate automatically by looking at the graph of operations you use as part of your custom loss function. Since you are using only operations that keras "knows about" aka that exist as operations in TensorFlow keras will automatically a graph of operations to backpropagate against. If you however have a custom loss function that uses an ...


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You are using the function softmax_cross_entropy_with_logits which, according to Tensorflow's documentation, has the following specification for logits, logits: Per-label activations, typically a linear output. These activation energies are interpreted as unnormalized log probabilities. Hence, you should pass the activations before the non-linearity ...


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After a lot of research, I came to the conclusion that this is doable, but would require a lot of work. Essentially, the steps to achieve such a transformation are as follows: Use this version of the model exporter (or any other solution) to re-export the savedmodel with its variables (by default, the Model Zoo does not give the variables, only an ...


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Assuming you have tried alternatives like storing it in csr matrices in scipy, you can move away from padding to avoid memory issues by declaring your batch_size=1 and may be create batches using a groupby for equal length sequences. If you are using keras, then please check Masking.


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The 80-20 split is just a heuristic. If you want to be precise about it, you can analyse it statistically. Say you have a 0-1 classifier, and you expect the accuracy to be around $\delta\in[0,1]$, then it's not too hard to show that you need $k=1/(\delta \epsilon^2)$ test samples to measure you accuracy up to a factor $1\pm\epsilon$. This means that if ...


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