How to get most informative features for scikit-learn classifiers?
The classifiers in machine learning packages like liblinear and nltk offer a method show_most_informative_features(), which is really helpful for debugging features:
machine-learning
scikit-learn & statsmodels – which R-squared is correct?
I’d like to choose the best algorithm for future. I found some solutions, but I didn’t understand which R-Squared value is correct.
How to implement the Softmax function in Python
From the Udacity’s deep learning class, the softmax of y_i is simply the exponential divided by the sum of exponential of the whole Y vector:
Deep-Learning Nan loss reasons
Perhaps too general a question, but can anyone explain what would cause a Convolutional Neural Network to diverge?
How to apply gradient clipping in TensorFlow?
Considering the example code.
How does Keras calculate the accuracy?
If there’s a binary classification problem, the labels are 0 and 1.
I know the prediction is a floating-point number because p is the probability of belonging to that class.
gradient descent using python and numpy
def gradient(X_norm,y,theta,alpha,m,n,num_it): temp=np.array(np.zeros_like(theta,float)) for i in range(0,num_it): h=np.dot(X_norm,theta) #temp[j]=theta[j]-(alpha/m)*( np.sum( (h-y)*X_norm[:,j][np.newaxis,:] ) ) temp[0]=theta[0]-(alpha/m)*(np.sum(h-y)) temp[1]=theta[1]-(alpha/m)*(np.sum((h-y)*X_norm[:,1])) theta=temp return theta X_norm,mean,std=featureScale(X) #length of X (number of rows) m=len(X) X_norm=np.array([np.ones(m),X_norm]) n,m=np.shape(X_norm) num_it=1500 alpha=0.01 theta=np.zeros(n,float)[:,np.newaxis] X_norm=X_norm.transpose() theta=gradient(X_norm,y,theta,alpha,m,n,num_it) print theta My theta from the above code is 100.2 100.2, but it should be 100.2 61.09 in matlab which is correct. … Read more
Keras Sequential model input layer
When creating a Sequential model in Keras, I understand you provide the input shape in the first layer. Does this input shape then make an implicit input layer?
Dummy variables when not all categories are present
I have a set of dataframes where one of the columns contains a categorical variable. I’d like to convert it to several dummy variables, in which case I’d normally use get_dummies.
How to concatenate two layers in keras?
I have an example of a neural network with two layers. The first layer takes two arguments and has one output. The second should take one argument as result of the first layer and one additional argument. It should looks like this: