One problem with a Tensorflow placeholder is that you can only feed it with a Python list or Numpy array (I think). So you can’t save the state between runs in tuples of LSTMStateTuple. I solved this by saving the state in a tensor like this initial_state = np.zeros((num_layers, 2, batch_size, state_size)) You have two … Read more
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