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| import numpy as np | |
| def compute_di_score(features,Y): | |
| ''' | |
| Computes DI Score of a Neural Network Output | |
| For convolution layer, the DI score of each channel is computed | |
| For fully connected layer, the DI score of each node is computed | |
| Input: | |
| features: shape: convolution: Ne,H,W,Nf | |
| fully connected: Ne,Nf | |
| Ne = number of example | |
| Nf = number of channels/filters | |
| Y: shape: Ne,n_class | |
| One hot encoded array. Each row contains one example. | |
| Output: | |
| dis: shape: (Nf,), di sore of each of the filters. | |
| ''' | |
| fshape = len(features.shape) | |
| #Taking the features to Appropriate Shape | |
| if fshape==4: | |
| Ne,H,W,Nf = features.shape | |
| features = features.transpose(3,0,1,2) #Nf, Ne, H,W | |
| features = features.reshape(Nf,Ne,-1) #Nf, Ne, HxW | |
| features = features.transpose(0,2,1) # Nf, HxW, Ne | |
| d = H*W | |
| elif fshape==2: | |
| Ne,Nf = features.shape | |
| features = features.transpose(1,0) #Nf, Ne | |
| features = np.expand_dims(features,axis=1) | |
| d = 1 | |
| #Normalizing the Y | |
| Y = Y.T | |
| N_number = np.sqrt(np.array([np.sum(y_one_hot,axis=1)]).T) | |
| Y = Y/N_number | |
| dis = np.zeros(Nf) | |
| rho = 10**-8 | |
| for i in range(Nf): | |
| X = features[i,:,:] | |
| dis[i] = np.trace(np.linalg.inv(X.dot(X.T)+rho*np.eye(d)).dot(X).dot(Y.T).dot(Y).dot(X.T)) | |
| return dis |
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