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I am guessing that the green dots represent the accuracies for classes the network knows how to classify, but I am confused. # Saving the model net.save(file='cnn.model') # Inputting the data data = imread('sample.jpg') X = data.reshape((500, 200, 3)) Y = data.reshape((200, 5)) # Training the neural network net.train(X, Y) # Saving the model net.save(file='cnn.model') # Testing the network Y_pred = net.predict(X_test) cm = confusion_matrix(Y_pred, Y) # Printing the confusion matrix print(cm) # ** CONFUSION MATRIX RESULTS ** [[ 46.5 0. 0. 1.5] [ 0. 43. 0. 0. 1.5] [ 0. 0. 44.9 0. 2.1] [ 0. 0. 0. 46.5 0. 0. 0. 1.5] [ 0. 0. 0. 0. 50. 0. 2.5 0. 4. ]] A: The first three sub-matrices of a confusion matrix are the actual classifications as output by the neural network. The green lines in those sub-matrices are the samples that the network was actually able to classify correctly. The last sub-matrix represents the confusion of the networks between classes. In your case, the 50% line represents "no-classification" by the networks. Hence 50% of your samples were actually not classified. A novel RYR1 mutation in the alpha 1s1 subunit of the muscle ryanodine receptor causes malignant hyperthermia susceptibility in a Dutch family. Malignant hyperthermia (MH) is an inherited muscle disorder in which excessive intracellular calcium triggers contraction-relaxation cycles and finally generalized System Requirements: PLAYSTATION®3 SYSTEM (SEN PS3 system) NEXT-GENERATION GAMING -Built-in motion sensor (located on the PlayStation®camera) -Built-in microphone -Wireless controller -Built-in stereo headset with mic -Built-in PlayStation®VR (required for PlayStation®VR compatible games) -Built-in Speaker PREFERRED PLATFORM DEVICE -DualShock®4 controller (PS4®/PS VR compatible) -
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