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Kaggle's Dataset is really noisy and it is really tough to get very good result with simple models. You have to do lots of preproccesing ,Data Augmentations and larger network for many more epoch to get a good result. Things you can do : Go to notebook section of your competetion and look out for some preprocessing and augmentations and apply them. Use some ...


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ELMo does not lookup the embeddings from a pre-precomputed table as Word2Vec and GloVe. Embeddings from ELMo are hidden states of an LSTM-based language model, i.e., they are computed on the fly when you give a sentence to the network. ELMo even does use standard word embeddings as the LSTM input. Words are treated as character sequences and those are ...


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To build on the previous answer: In transfer learning, the goal is to use a pre-trained model and tweak the model to then specialise it to suit a certain task. So, what we do is, as SrJ has eluded to, keep the main model's architecture in tact. So this would be the 6 CNN layers (and possibly the three linear layers, if they were also involved in pre-training)...


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You should retrain the last linear layers and keep the CNN layers unchanged.


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As per my understanding,, in the both scenerio above when learning the object type and color what you are doing is a multitask learning. That is you are teaching your model to do two task simultaneously, 1. Predicting Object type(Which fruit/shape) and 2. Predicing color. So it is not like TL rather Multi task learning. 1. TL does have some source task.But ...


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Use the imagenet weights. Freeze the basemodel to prevent it from getting trained on your data. You might be overfitting the trainset.Try to not use the whole MobileNet model, maybe only the first 10-15 layers.


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So, I believe the question you are asking is how to re-train the model on a single day's worth of data, rather than training on all 180 day's worth of data. It definitely seems reasonable that you have seen overfitting, this can be due to the complexity of the model (related to depth of network). So changing the model's architecture to suit this ...


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It looks to me like the saved model your loading from tf_hub isn't compatible with the shape you're specifying here: What you're sending to keras looks like it results in: Got: Positional arguments (4 total): * Tensor("inputs:0", shape=(None, 1200, 1200), dtype=float32) The saved model looks like it's expecting: TensorSpec(shape=(None, None,...


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