def train_model(img, input_mask, output_mask): # generate data inputs, outputs = generate_data_from_image(img, input_mask, output_mask, 120000) # split data into training and validation sets data_train = inputs[:, :100000], outputs[:, :100000] data_valid = inputs[:, 100000:], outputs[:, 100000:] # compute normalizing transformation pre = WhiteningPreconditioner(*data_train) # intialize model model = MCGSM(dim_in=data_train[0].shape[0], dim_out=data_train[1].shape[0], num_components=8, num_scales=4, num_features=30) # fit parameters model.initialize(*pre(*data_train)) model.train(*chain(pre(*data_train), pre(*data_valid)), parameters={ 'verbosity': 1, 'max_iter': 1000, 'threshold': 1e-7, 'val_iter': 5, 'val_look_ahead': 10, 'num_grad': 20, }) return model, pre
def train_model(img, input_mask, output_mask): # generate data inputs, outputs = generate_data_from_image( img, input_mask, output_mask, 120000) # split data into training and validation sets data_train = inputs[:, :100000], outputs[:, :100000] data_valid = inputs[:, 100000:], outputs[:, 100000:] # compute normalizing transformation pre = WhiteningPreconditioner(*data_train) # intialize model model = MCGSM( dim_in=data_train[0].shape[0], dim_out=data_train[1].shape[0], num_components=8, num_scales=4, num_features=30) # fit parameters model.initialize(*pre(*data_train)) model.train(*chain(pre(*data_train), pre(*data_valid)), parameters={ 'verbosity': 1, 'max_iter': 1000, 'threshold': 1e-7, 'val_iter': 5, 'val_look_ahead': 10, 'num_grad': 20, }) return model, pre
def main(argv): # load image and turn into grayscale img = rgb2gray(imread('media/newyork.png')) # generate data inputs, outputs = generate_data_from_image( img, input_mask, output_mask, 220000) # split data into training, test, and validation sets inputs = split(inputs, [100000, 200000], 1) outputs = split(outputs, [100000, 200000], 1) data_train = inputs[0], outputs[0] data_test = inputs[1], outputs[1] data_valid = inputs[2], outputs[2] # compute normalizing transformation pre = WhiteningPreconditioner(*data_train) # intialize model model = MCGSM( dim_in=data_train[0].shape[0], dim_out=data_train[1].shape[0], num_components=8, num_scales=4, num_features=32) # fit parameters model.initialize(*pre(*data_train)) model.train(*chain(pre(*data_train), pre(*data_valid)), parameters={ 'verbosity': 1, 'max_iter': 1000, 'threshold': 1e-7, 'val_iter': 5, 'val_look_ahead': 10, 'num_grad': 20, }) # evaluate model print 'Average log-likelihood: {0:.4f} [bit/px]'.format( -model.evaluate(data_test[0], data_test[1], pre)) # synthesize a new image img_sample = sample_image(img, model, input_mask, output_mask, pre) imwrite('newyork_sample.png', img_sample, cmap='gray', vmin=min(img), vmax=max(img)) # save model with open('image_model.pck', 'wb') as handle: dump({ 'model': model, 'input_mask': input_mask, 'output_mask': output_mask}, handle, 1) return 0
def robust_linear_regression(x, y, num_scales=3, max_iter=1000): """ Performs linear regression with Gaussian scale mixture residuals. $$y = ax + b + \\varepsilon,$$ where $\\varepsilon$ is assumed to be Gaussian scale mixture distributed. @type x: array_like @param x: list of one-dimensional inputs @type y: array_like @param y: list of one-dimensional outputs @type num_scales: int @param num_scales: number of Gaussian scale mixture components @type max_iter: int @param max_iter: number of optimization steps in parameter search @rtype: tuple @return: slope and y-intercept """ x = asarray(x).reshape(1, -1) y = asarray(y).reshape(1, -1) # preprocess inputs m = mean(x) s = std(x) x = (x - m) / s # preprocess outputs using simple linear regression C = cov(x, y) a = C[0, 1] / C[0, 0] b = mean(y) - a * mean(x) y = y - (a * x + b) # robust linear regression model = MCGSM(dim_in=1, dim_out=1, num_components=1, num_scales=num_scales, num_features=0) model.initialize(x, y) model.train(x, y, parameters={'train_means': True, 'max_iter': max_iter}) a = (a + float(model.predictors[0])) / s b = (b + float(model.means)) - a * m return a, b
def main(argv): # load image and turn into grayscale img = rgb2gray(imread('media/newyork.png')) # generate data inputs, outputs = generate_data_from_image(img, input_mask, output_mask, 220000) # split data into training, test, and validation sets inputs = split(inputs, [100000, 200000], 1) outputs = split(outputs, [100000, 200000], 1) data_train = inputs[0], outputs[0] data_test = inputs[1], outputs[1] data_valid = inputs[2], outputs[2] # compute normalizing transformation pre = WhiteningPreconditioner(*data_train) # intialize model model = MCGSM(dim_in=data_train[0].shape[0], dim_out=data_train[1].shape[0], num_components=8, num_scales=4, num_features=32) # fit parameters model.initialize(*pre(*data_train)) model.train(*chain(pre(*data_train), pre(*data_valid)), parameters={ 'verbosity': 1, 'max_iter': 1000, 'threshold': 1e-7, 'val_iter': 5, 'val_look_ahead': 10, 'num_grad': 20, }) # evaluate model print 'Average log-likelihood: {0:.4f} [bit/px]'.format( -model.evaluate(data_test[0], data_test[1], pre)) # synthesize a new image img_sample = sample_image(img, model, input_mask, output_mask, pre) imwrite('newyork_sample.png', img_sample, cmap='gray', vmin=min(img), vmax=max(img)) # save model with open('image_model.pck', 'wb') as handle: dump( { 'model': model, 'input_mask': input_mask, 'output_mask': output_mask }, handle, 1) return 0