All the cool bearded gurus in what’s left of AI research use Lisp 🙂 There are two big camps: Common Lisp and Scheme. They have different syntax, etc. Lots of good stuff written for both. Java is a very popular all-purpose language but a lot of the interesting stuff in AI / Functional Programming, such …
Change a = dataset[i:(i + look_back), 0] To a = dataset[i:(i + look_back), :] If you want the 3 features in your training data. Then use model.add(LSTM(4, input_shape=(look_back,3))) To specify that you have look_back time steps in your sequence, each with 3 features. It should run EDIT : Indeed, sklearn.preprocessing.MinMaxScaler()‘s function : inverse_transform() takes an …
For starters, a neural network can model any function (not just linear functions) Have a look at this – http://neuralnetworksanddeeplearning.com/chap4.html. A Neural Network has got non linear activation layers which is what gives the Neural Network a non linear element. The function for relating the input and the output is decided by the neural network …
Based on what you said it sounds like you need a larger batch_size, and of course there are implications with that which could impact the steps_per_epoch and number of epochs. To solve for jumping-around A larger batch size will give you a better gradient and will help to prevent jumping around You may also want …
Change index = output.data.numpy().argmax() to index = output.cpu().data.numpy().argmax() This means data is first moved to cpu and then converted to numpy array.
So the learning rate is stored in optim.param_groups[i][‘lr’]. optim.param_groups is a list of the different weight groups which can have different learning rates. Thus, simply doing: for g in optim.param_groups: g[‘lr’] = 0.001 will do the trick. **Alternatively**, as mentionned in the comments, if your learning rate only depends on the epoch number, you can …
Use eval() from keras.backend: import keras.backend as K from keras.models import Sequential from keras.layers import Dense model = Sequential() model.add(Dense(1, input_shape=(1,))) model.add(Dense(1)) model.compile(loss=”mse”, optimizer=”adam”) print(K.eval(model.optimizer.lr)) Output: 0.001
You can do it. But the nice thing about batchnorm, in addition to activation distribution stabilization, is that the mean and std deviation are likely migrate as the network learns. Effectively, setting the batchnorm right after the input layer is a fancy data pre-processing step. It helps, sometimes a lot (e.g. in linear regression). But …
if x <= 0, output is 0. if x > 0, output is 1 The ReLU function is defined as: For x > 0 the output is x, i.e. f(x) = max(0,x) So for the derivative f ‘(x) it’s actually: if x < 0, output is 0. if x > 0, output is 1. The …