I am building a tensorflow model with new estimator high-level api. My model looks like below screenshot
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In fact, the model is more complex than that due to the model is used to simulate game operation. Classification is responsible for decide whether it is good time for action. Then the regression will give the details about the action. It contains a combination of CNN and RNN.
However, due to the complexity and memory consumption, it is impossible to train and run classification and regression as two network simultaneously. Also, when I create my estimator like:
# Create the Estimator
mnist_classifier = tf.estimator.Estimator(
model_fn=cnn_model_fn, model_dir="/tmp/mnist_convnet_model")
I can only provide one model function for the estimator. Is it possible to train and run two estimator together?
Change your loss function to be a linear combination of regression and classification losses. It will be one estimator with one loss, but multiple inferences.
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I tried image classification using trained model and its working well but some images could not find perfectly in that time have to get that image and label from users so my doubt is..Is it possible to add new data into already trained model?
No, during inference time you use the weights of the trained model for predictions. Which basically means that at the time your model is deployed the capabilities of your image classifier are fixed by the weights. If you wish to improve your model, you would have to retrain your model with the new - data. However, there is another paradigm of learning called "Online Learning" where the model is continuously learning and modifying the weights. In this case your weights are not fixed and your model is continuously updating its weights with each training input. However afaik this is not usually recommended for CNNs, because the backward pass of gradients is computationally intensive and your inference will be slow because of this.
No model can predict with 100% accuracy if it does it's an ideal model. And if you want to add more data to your train model you have to retrain the model with the new data. Having more data is always a good idea. It allows the “data to tell for itself,” instead of relying on assumptions and weak correlations. Presence of more data results in better and accurate models. So if you want to get better accuracy you have to train your model with more data. Without retraining, you can't add data to your trained model.
I'm currently creating a model and while creating it I came with some questions. Does training the same model with the same data multiple times leads to better precision of those objects, since your training it every time? And what could be the issue when sometimes the object gets 90% precision and when I re-run it gets lower precision or even not predicting the right object? Is it because of Tensorflow running on the GPU?
I will guess that you are doing image recognition and that you want to identify images (objects) using a neuronal network made with Keras. You should train it once, but during training you will do several epochs, meaning the algorithm adapts the weights in several rounds (epochs). For each round it goes over all training images. Once trained, you can use the model to identify images/objects.
You can evaluate the accuracy of your trained model over the same training set, but it is better to use a different set (see train_test_split from sklearn for instance).
Training is a stochastic process, meaning that every time you train your network it will be different in the end. Hence, you will get different accurcies. The stochasticity comes from different initial weights or from using stochastic gradient descent methods for instance.
The question does not appear to have anything to do with Keras or TensorFlow but basic understandting of how neuronal networks work. There is no connection to running Tensorflow on the GPU. You will also not get better precision by training with the same objects. If you train your model on a dataset for a very long time (many epochs), you might get into overfitting. Then, the accuracy of your model on this training dataset will be very high, but the model will have low accuracy on other datasets.
A common technique is split your date in train and validation datasets, then repeatedly train your model using EarlyStopping. This will train on the training dataset, then calculate the loss against the validation dataset, and then keep training until no further improvement is seen. You can set a patience parameter to wait for X epochs without an improvement to stop training (and optionally save the best model)
https://machinelearningmastery.com/how-to-stop-training-deep-neural-networks-at-the-right-time-using-early-stopping/
Another trick is image augmentation with ImageDataGenerator which will generate synthetic data for you (rotations, shifts, mirror images, brightness adjusts, noise etc). This can effectively increase the amount of data you have to train with, thus reducing overfitting.
https://machinelearningmastery.com/how-to-configure-image-data-augmentation-when-training-deep-learning-neural-networks/
When using tensorflow to train a neural network I can set the loss function arbitrarily. Is there a way to do the same in sklearn when training a SVM? Let's say I want my classifier to only optimize sensitivity (regardless of the sense of it), how would I do that?
This is not possible with Support Vector Machines, as far as I know. With other models you might either change the loss that is optimized, or change the classification threshold on the predicted probability.
SVMs however minimize the hinge loss, and they do not model the probability of classes but rather their separating hyperplane, so there is not much room for manual adjustements.
If you need to focus on Sensitivity or Specificity, use a different model that allows maximizing that function directly, or that allows predicting the class probabilities (thinking Logistic Regressions, Tree based methods, for example)
Can any one please help me out?
I am working on my thesis work. Its about Predicting Parkinson disease, Since i want to build an LSTM model to adapt independent of patients. Currently i have implemented it using TensorFlow with my own loss function.
Since i am planning to introduce both labeled train and unlabeled train data in every batch of data to train the model. I want to apply my own loss function on this both labeled and unlabeled train data and also want to apply cross entropy loss only on labeled train data. Can i do this in tensorflow?
So my question is, Can i have combination of loss functions in a single model training on different set of train data?
From an implementation perspective, the short answer would be yes. However, I believe your question could be more specific, maybe what you mean is whether you could do it with tf.estimator?
Let me make my question clearer:
I am using python gensim.models.Word2Vec to train a word embedding model. Based on my understanding, the model training is in essence a machine learning issue---to train a neural network via a prediction task. For example, if I select parameters to train a skip-gram model, then the model is trained by predicting context words from target word. Once the model is well-trained, word vectors are just obtained from the model.
If my understanding is correct, so since in fact it is a machine learning process and the training goal is to perform well in the prediction task, there should be a loss function during training and the model is supposed to make the loss as low as possible. So, how to know the model loss value for a given set of parameters? Or is there any other metrics that we can know to understand the model itself?
Hope I have made my question clear. In a word, I don't want to evaluate the model by its outputs as in the Google test set http://word2vec.googlecode.com/svn/trunk/questions-words.txt, but I want to understand the model itself as a simple machine learning problem during its training process. Would this be possible?