The -v option here is really meant to be used as a way to avoid the overfitting problem (instead of using the whole data for training, perform an N-fold cross-validation training on N-1 folds and testing on the remaining fold, one at-a-time, then report the average accuracy). Thus it only returns the cross-validation accuracy (assuming … Read more
Try this generator: def generator_two_img(X1, X2, y, batch_size): genX1 = gen.flow(X1, y, batch_size=batch_size, seed=1) genX2 = gen.flow(X2, y, batch_size=batch_size, seed=1) while True: X1i = genX1.next() X2i = genX2.next() yield [X1i[0], X2i[0]], X1i[1] Generator for 3 inputs: def generator_three_img(X1, X2, X3, y, batch_size): genX1 = gen.flow(X1, y, batch_size=batch_size, seed=1) genX2 = gen.flow(X2, y, batch_size=batch_size, seed=1) genX3 … Read more
One approach is cross-validation. In essence, you pick a subset of your data and cluster it into k clusters, and you ask how well it clusters, compared with the rest of the data: Are you assigning data points to the same cluster memberships, or are they falling into different clusters? If the memberships are roughly … Read more
The model’s architecture cannot be changed because the weights have been trained for a specific input configuration. Replacing the first layer with your own would pretty much render the rest of the weights useless. — Edit: elaboration suggested by Prune– CNNs are built so that as they go deeper, they can extract high-level features derived … Read more
you can use this formula [(W−K+2P)/S]+1. W is the input volume – in your case 128 K is the Kernel size – in your case 5 P is the padding – in your case 0 i believe S is the stride – which you have not provided. So, we input into the formula: Output_Shape = … Read more
stack Concatenates sequence of tensors along a new dimension. cat Concatenates the given sequence of seq tensors in the given dimension. So if A and B are of shape (3, 4): torch.cat([A, B], dim=0) will be of shape (6, 4) torch.stack([A, B], dim=0) will be of shape (2, 3, 4)
The threshold can be set using clf.predict_proba() for example: from sklearn.tree import DecisionTreeClassifier clf = DecisionTreeClassifier(random_state = 2) clf.fit(X_train,y_train) # y_pred = clf.predict(X_test) # default threshold is 0.5 y_pred = (clf.predict_proba(X_test)[:,1] >= 0.3).astype(bool) # set threshold as 0.3
In keras – while building a sequential model – usually the second dimension (one after sample dimension) – is related to a time dimension. This means that if for example, your data is 5-dim with (sample, time, width, length, channel) you could apply a convolutional layer using TimeDistributed (which is applicable to 4-dim with (sample, … Read more
Transformer in scikit-learn – some class that have fit and transform method, or fit_transform method. Predictor – some class that has fit and predict methods, or fit_predict method. Pipeline is just an abstract notion, it’s not some existing ml algorithm. Often in ML tasks you need to perform sequence of different transformations (find set of … Read more
You get this error because your model is on the GPU, but your data is on the CPU. So, you need to send your input tensors to the GPU. inputs, labels = data # this is what you had inputs, labels = inputs.cuda(), labels.cuda() # add this line Or like this, to stay consistent with … Read more