# You will be working with a dataset that contains handwritten digits. # # Load Training Data print 'Loading and Visualizing Data ...' data = scipy.io.loadmat('ex4/ex4data1.mat') X = data['X'] y = data['y'] m, _ = X.shape # Randomly select 100 data points to display rand_indices = np.random.permutation(range(m)) sel = X[rand_indices[0:100], :] displayData(sel) #raw_input("Program paused. Press Enter to continue...") ## ================ Part 2: Loading Parameters ================ # In this part of the exercise, we load some pre-initialized # neural network parameters. print 'Loading Saved Neural Network Parameters ...' # Load the weights into variables Theta1 and Theta2 data = scipy.io.loadmat('ex4/ex4weights.mat') Theta1 = data['Theta1'] Theta2 = data['Theta2'] y = np.squeeze(y)
show() input('Program paused. Press Enter to continue...') ## =============== Part 4: Loading and Visualizing Face Data ============= # We start the exercise by first loading and visualizing the dataset. # The following code will load the dataset into your environment # print('Loading face dataset.') # Load Face dataset data = scipy.io.loadmat('ex7faces.mat') X = data['X'] # Display the first 100 faces in the dataset displayData(X[0:100, :]) input('Program paused. Press Enter to continue...') ## =========== Part 5: PCA on Face Data: Eigenfaces =================== # Run PCA and visualize the eigenvectors which are in this case eigenfaces # We display the first 36 eigenfaces. # print( 'Running PCA on face dataset.\n(this might take a minute or two ...)\n\n') # Before running PCA, it is important to first normalize X by subtracting # the mean value from each feature X_norm, mu, sigma = featureNormalize(X) # Run PCA
#show() #raw_input('Program paused. Press Enter to continue...') ## =============== Part 4: Loading and Visualizing Face Data ============= # We start the exercise by first loading and visualizing the dataset. # The following code will load the dataset into your environment # print 'Loading face dataset.' # Load Face dataset data = scipy.io.loadmat('ex7/ex7faces.mat') X = data['X'] # Display the first 100 faces in the dataset displayData(X[0:100, :]) #raw_input('Program paused. Press Enter to continue...') ## =========== Part 5: PCA on Face Data: Eigenfaces =================== # Run PCA and visualize the eigenvectors which are in this case eigenfaces # We display the first 36 eigenfaces. # print 'Running PCA on face dataset.\n(this might take a minute or two ...)\n\n' # Before running PCA, it is important to first normalize X by subtracting # the mean value from each feature X_norm, mu, sigma = featureNormalize(X) # Run PCA U, S, V = pca(X_norm)
# You will be working with a dataset that contains handwritten digits. # # Load Training Data print('Loading and Visualizing Data ...') data = scipy.io.loadmat('ex4data1.mat') X = data['X'] y = data['y'] m, _ = X.shape # Randomly select 100 data points to display rand_indices = np.random.permutation(range(m)) sel = X[rand_indices[0:100], :] displayData(sel) input("Program paused. Press Enter to continue...") ## ================ Part 2: Loading Parameters ================ # In this part of the exercise, we load some pre-initialized # neural network parameters. print('Loading Saved Neural Network Parameters ...') # Load the weights into variables Theta1 and Theta2 data = scipy.io.loadmat('ex4weights.mat') Theta1 = data['Theta1'] Theta2 = data['Theta2'] y = np.squeeze(y)
# We start the exercise by first loading and visualizing the dataset. # You will be working with a dataset that contains handwritten digits. # Load Training Data print('Loading and Visualizing Data ...') data = scipy.io.loadmat('ex4data1.mat') X = data['X'] y = data['y'].flatten() m = np.size(X, 0) # Randomly select 100 data points to display sel = np.random.permutation(range(m)) sel = sel[:100] displayData(X[sel]) input('Program paused. Press Enter to continue.') # ================ Part 2: Loading Parameters ================ # In this part of the exercise, we load some pre-initialized # neural network parameters. print('\nLoading Saved Neural Network Parameters ...') # Load the weights into variables Theta1 and Theta2 weights = scipy.io.loadmat('ex4weights.mat') Theta1 = weights['Theta1'] Theta2 = weights['Theta2'] # Unroll parameters