The purpose of the activation function is to introduce non-linearity into the network in turn, this allows you to model a response variable (aka target variable, class label, or score) that varies non-linearly with its explanatory variables non-linear means that the output cannot be reproduced from a linear combination of the inputs (which is not … Read more
I am adding another answer because this is the cleanest way in order to compute these metrics correctly on your test set (as of 22nd of March 2020). The first thing you need to do is to create a custom callback, in which you send your test data: import tensorflow as tf from tensorflow.keras.callbacks import … Read more
SVMs were originally designed for binary classification. They have then been extended to handle multi-class problems. The idea is to decompose the problem into many binary-class problems and then combine them to obtain the prediction. One approach called one-against-all, builds as many binary classifiers as there are classes, each trained to separate one class from … Read more
According to the official libsvm documentation (Section 7): LIBSVM implements the “one-against-one” approach for multi-class classification. If k is the number of classes, then k(k-1)/2 classifiers are constructed and each one trains data from two classes. In classification we use a voting strategy: each binary classification is considered to be a voting where votes can … Read more
seasonal_decompose() requires a freq that is either provided as part of the DateTimeIndex meta information, can be inferred by pandas.Index.inferred_freq or else by the user as an integer that gives the number of periods per cycle. e.g., 12 for monthly (from docstring for seasonal_mean): def seasonal_decompose(x, model=”additive”, filt=None, freq=None): “”” Parameters ———- x : array-like … Read more
Confusion matrix needs both labels & predictions as single-digits, not as one-hot encoded vectors; although you have done this with your predictions using model.predict_classes(), i.e. rounded_predictions = model.predict_classes(test_images, batch_size=128, verbose=0) rounded_predictions[1] # 2 your test_labels are still one-hot encoded: test_labels[1] # array([0., 0., 1., 0., 0., 0., 0., 0., 0., 0.], dtype=float32) So, you should … Read more
Here’s a complete example, using the following functions from the Bioinformatics Toolbox: SVMTRAIN, SVMCLASSIFY, CLASSPERF, CROSSVALIND. load fisheriris %# load iris dataset groups = ismember(species,’setosa’); %# create a two-class problem %# number of cross-validation folds: %# If you have 50 samples, divide them into 10 groups of 5 samples each, %# then train with 9 … Read more
You can wrap the loss function as a inner function and pass your input tensor to it (as commonly done when passing additional arguments to the loss function). def custom_loss_wrapper(input_tensor): def custom_loss(y_true, y_pred): return K.binary_crossentropy(y_true, y_pred) + K.mean(input_tensor) return custom_loss input_tensor = Input(shape=(10,)) hidden = Dense(100, activation=’relu’)(input_tensor) out = Dense(1, activation=’sigmoid’)(hidden) model = Model(input_tensor, out) … Read more
It is not actually difficult to demonstrate why using the whole dataset (i.e. before splitting to train/test) for selecting features can lead you astray. Here is one such demonstration using random dummy data with Python and scikit-learn: import numpy as np from sklearn.feature_selection import SelectKBest from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics … Read more
This assumes the module is already installed. Please refer to their documentation if not. Disclaimer This answer is not meant to be the right or only way of training the object detection module. This is simply I sharing my experience and what has worked for me. I’m open to suggestions and learning more about this … Read more