I am running Keras Tuner(Hyperband) since Random search does not find optimal solution, I would like to know how we can control the number of models and epochs to run. In Randomsearch we can clearly give it in max trials and execution per trial but I don't find this parameter in Hyperband. I could see max_epoch in hyperband but how it is being used if I specify epochs=100 while doing tuner.search
Can you please help me on this.
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I'm trying to train the model on a MagnaTagAtune dataset. Is the model properly trained? What is the problem, does anyone know? Will waiting solve the problem?
The results are shown in the image.
enter image description here
Thank you pseudo_random_here for your answer. Your tips were helpful, but the problem was still there.
Unfortunately, changing the learning rate did not work. Now, after your advice, I will use the SGD optimizer with a learning rate of 0.1. I even used another model that was for this but the problem was not solved.
from keras.optimizers import SGD
opt = SGD(lr=0.1)
model.compile(loss = "categorical_crossentropy", optimizer = opt)
Short answer: I would say your val_loss is too high and waiting is unlikely to solve your problem
Explanation: I believe there are two possibilities here:
Your architecture is not suitable for the data
Your learning rate is too small
PS. It would help a lot if you were to provide info on what architecture of NNs you are using, what loss function we are looking at and what exactly is it that you are predicting?
I have the following LSTM network(Fig 1) for predicting the Bitcoin Price. The input is every hour close price of Bitcoin. I am facing some issues and any advice is appreciated.
Earlier on the same network, my RMSE on testing and training set was 6.71 and 7.41 RMSE. I recompiled the whole code and there was an abrupt increase to 233.51 for the training set and 345.56 for the testing set. Can anyone help me with finding out the reason behind this?
Also, How to improve the accuracy of my network as it very low in every iteration?
How should I decide the parameters for my LSTM network. (units, epochs, batch_size, time_steps to input)
Thank you in advance for any help extended.
Your question requires a lot more information. For example, data size, timesteps lookback, data preprocessing procedure, etc. But I would recommend you to debug your problem with the following method. First, check whether your input/output data are processed properly or not. Then, try to train the simpler model apart from LSTM as it could result in overfitting. But sometimes, if the input signal is too random, it is normal that your model results would highly fluctuate as there's no correlation in data.
PS. never use the Machine Learning model to predict stock price. It never works.
I'm new to deep learning.
I have build a small architecture and compiling it using Adam optimizer as shown below:
model.compile(optimizer=Adam(learning_rate=0.0001), loss="mse")
#Train it by providing training images
model.fit(x, x, epochs=10, batch_size=16)
Now i'm aware of all type of decay where I can change learning rate at some epoch, but is there a way where I can change my learning rate automatically once my loss stop decreasing.
PS: It might be silly ques but please don't be harsh on me as I'm new!
You can use the Callbacks API in Keras.
It provides the following classes in keras.callbacks to alter learning rate on each epoch:
1. LearningRateScheduler
You can create your own learning rate schedule as a function of epoch.
Then pass the callback object to the callbacks argument to the fit method in a list.
For example, say your callback object is called lr_callback, then you would use:
model.fit(train_X, train_y, epochs=10, callbacks=[lr_callback]
Refer: keras.callbacks.LearningRateScheduler
2. ReduceLROnPlateau
This reduces the learning rate once your learning rate stops decreasing by min_delta amount. You can also set the patience and other useful parameters.
You can pass the callback to the fit method in the same way as done above.
Refer: keras.callbacks.ReduceLROnPlateau
The usage for both the callbacks is detailed quite well in the docs, which I have linked above already.
Alternatively, you could define your own callback to schedule the learning rate, if the above do not satisfy your requirements.
As you use the Adam optimizer, the learning rate is automatically adjusted depending on the gradient. So the more the gradient reaches a global minimum, the smaller the learning rate gets, so it does not "jump" over the global minimum. I am not sure, what you want to achieve, but the learning_rate you define in Adam() is just a start learning rate.
For more information on Adam and optimizers, I recommend the book hands-on machine learning of Aurélien Géron
I am new to neural networks so I tried my first neural network which is pretty close to one at keras learn page,given below:
https://github.com/aakarsh1011/Neural-Network/blob/master/MNSIT%20classification.ipynb
Kindlly look at the ending where I red a random image and tried to predict it which comes out as a bag, and when trained at epocs=5 it predicted it as a sandal.
Is something wrong with my code or labeling.
UPDATE - Being new to the field I didn't know the importance of epochs so I asked this question, I was afraid that I don't over-fit the model or train train too much. But there is no definite way to do this, it's all try and error. GOOD LUCK!
First of all, as far as I can see, your code is correct. Your model predicting the wrong item can be caused by the model not being trained for long enough. I would highly recommend you to set epochs=100 and you will be able to see the model's accuracy rise. You should generally always try to give your model as many epochs as possible for training. It will simply take some time. Try out some different numbers of epochs to find the one not taking too long, but still giving an acceptable result.
I was trying to find, how many epochs was the pretrained Alexnet model (available from torchvision) trained for on Imagenet and also what learning rate was used? I tried checking the checkpoint keys to see if any epoch info was stored.
Any suggestions on how to find it out?
According to this comment on GitHub by a PyTorch team member, most of the training was done with a variant of https://github.com/pytorch/examples/tree/master/imagenet. All the models were trained on Imagenet. According to the file:
The default learning rate schedule starts at 0.1 and decays by a factor of 10 every 30 epochs, though they recommend using 0.01 for Alexnet as initial learning rate.
The default value for epochs is 90.