def main(): init_file = None weights_file = 'nets/weights.pickle' print('loading train/valid data...') X_train, X_valid, y_train, y_valid = utils.load_train_val( 'data/cifar-10-batches-py') print(' X_train.shape = %r' % (X_train.shape, )) print(' y_train.shape = %r' % (y_train.shape, )) print(' X_valid.shape = %r' % (X_valid.shape, )) print(' y_valid.shape = %r' % (y_valid.shape, )) X_train, X_valid, X_mean = utils.normalize(X_train, X_valid) train(X_train, X_valid, y_train, y_valid, weights_file, init_file)
def main(): init_file = None weights_file = 'nets/weights.pickle' print('loading train/valid data...') X_train, X_valid, y_train, y_valid = utils.load_train_val( 'data/cifar-10-batches-py') print(' X_train.shape = %r' % (X_train.shape,)) print(' y_train.shape = %r' % (y_train.shape,)) print(' X_valid.shape = %r' % (X_valid.shape,)) print(' y_valid.shape = %r' % (y_valid.shape,)) X_train, X_valid, X_mean = utils.normalize(X_train, X_valid) train(X_train, X_valid, y_train, y_valid, weights_file, init_file)
def main(): outdir = 'images-predicted' if not isdir(outdir): print('mkdir outdir') makedirs(outdir) init_file = 'nets/weights_check.pickle' print('loading train/valid data...') X_train, _, _, _ = utils.load_train_val('data/cifar-10-batches-py') X_test, _ = utils.load_cifar100_class('data/cifar-100-python/train', 0) X_train, X_test, X_mean = utils.normalize(X_train, X_test) print(' X_train.shape = %r' % (X_train.shape, )) print(' X_test.shape = %r' % (X_test.shape, )) inference(X_test, X_mean, init_file, outdir)