def run(): train_data, valid_data, tests_data = load_data_dicts(N_train, N_valid, N_tests) parser, pred_fun, loss_fun, frac_err = make_nn_funs(layer_sizes) N_weight_types = len(parser.names) hyperparams = VectorParser() hyperparams['log_param_scale'] = np.full(N_weight_types, init_log_param_scale) hyperparams['log_alphas'] = np.full((N_iters, N_weight_types), init_log_alphas) hyperparams['invlogit_betas'] = np.full((N_iters, N_weight_types), init_invlogit_betas) fixed_hyperparams = VectorParser() fixed_hyperparams['log_L2_reg'] = np.full(N_weight_types, init_log_L2_reg) def primal_optimizer(hyperparam_vect, i_hyper): def indexed_loss_fun(w, L2_vect, i_iter): rs = RandomState((seed, i_hyper, i_iter)) # Deterministic seed needed for backwards pass. idxs = rs.randint(N_train, size=batch_size) return loss_fun(w, train_data['X'][idxs], train_data['T'][idxs], L2_vect) learning_curve_dict = defaultdict(list) def callback(x, v, g, i_iter): if i_iter % thin == 0: learning_curve_dict['learning_curve'].append(loss_fun(x, **train_data)) learning_curve_dict['grad_norm'].append(np.linalg.norm(g)) learning_curve_dict['weight_norm'].append(np.linalg.norm(x)) learning_curve_dict['velocity_norm'].append(np.linalg.norm(v)) cur_hyperparams = hyperparams.new_vect(hyperparam_vect) rs = RandomState((seed, i_hyper)) W0 = fill_parser(parser, np.exp(cur_hyperparams['log_param_scale'])) W0 *= rs.randn(W0.size) alphas = np.exp(cur_hyperparams['log_alphas']) betas = logit(cur_hyperparams['invlogit_betas']) L2_reg = fill_parser(parser, np.exp(fixed_hyperparams['log_L2_reg'])) W_opt = sgd_parsed(grad(indexed_loss_fun), kylist(W0, alphas, betas, L2_reg), parser, callback=callback) return W_opt, learning_curve_dict def hyperloss(hyperparam_vect, i_hyper): W_opt, _ = primal_optimizer(hyperparam_vect, i_hyper) return loss_fun(W_opt, **train_data) hyperloss_grad = grad(hyperloss) initial_hypergrad = hyperloss_grad( hyperparams.vect, 0) parsed_init_hypergrad = hyperparams.new_vect(initial_hypergrad.copy()) avg_hypergrad = initial_hypergrad.copy() for i in xrange(1, N_meta_iter): avg_hypergrad += hyperloss_grad( hyperparams.vect, i) print i parsed_avg_hypergrad = hyperparams.new_vect(avg_hypergrad) parser.vect = None # No need to pickle zeros return parser, parsed_init_hypergrad, parsed_avg_hypergrad
def run(): train_data, valid_data, tests_data = load_data_dicts( N_train, N_valid, N_tests) parser, pred_fun, loss_fun, frac_err = make_nn_funs(layer_sizes) N_weight_types = len(parser.names) N_weights = len(parser.vect) hyperparams = VectorParser() rs = RandomState((seed)) hyperparams['log_L2_reg'] = np.full(N_weights, init_log_L2_reg)\ + rs.randn(N_weights) * init_log_L2_reg_noise hyperparams['log_param_scale'] = np.full(N_weight_types, init_log_param_scale) hyperparams['log_alphas'] = np.full((N_iters, N_weight_types), init_log_alphas) hyperparams['invlogit_betas'] = np.full((N_iters, N_weight_types), init_invlogit_betas) cur_primal_results = {} def primal_optimizer(hyperparam_vect, i_hyper): def indexed_loss_fun(w, L2_vect, i_iter): rs = RandomState( (seed, i_hyper, i_iter)) # Deterministic seed needed for backwards pass. idxs = rs.randint(N_train, size=batch_size) return loss_fun(w, train_data['X'][idxs], train_data['T'][idxs], L2_vect) learning_curve_dict = defaultdict(list) def callback(x, v, g, i_iter): if i_iter % thin == 0: learning_curve_dict['learning_curve'].append( loss_fun(x, **train_data)) learning_curve_dict['grad_norm'].append(np.linalg.norm(g)) learning_curve_dict['weight_norm'].append(np.linalg.norm(x)) learning_curve_dict['velocity_norm'].append(np.linalg.norm(v)) cur_hyperparams = hyperparams.new_vect(hyperparam_vect) rs = RandomState((seed, i_hyper)) W0 = fill_parser(parser, np.exp(cur_hyperparams['log_param_scale'])) W0 *= rs.randn(W0.size) alphas = np.exp(cur_hyperparams['log_alphas']) betas = logit(cur_hyperparams['invlogit_betas']) L2_reg = np.exp(cur_hyperparams['log_L2_reg']) W_opt = sgd_parsed(grad(indexed_loss_fun), kylist(W0, alphas, betas, L2_reg), parser, callback=callback) cur_primal_results['weights'] = getval(W_opt).copy() cur_primal_results['learning_curve'] = getval(learning_curve_dict) return W_opt, learning_curve_dict def hyperloss(hyperparam_vect, i_hyper): W_opt, _ = primal_optimizer(hyperparam_vect, i_hyper) return loss_fun(W_opt, **valid_data) hyperloss_grad = grad(hyperloss) meta_results = defaultdict(list) old_metagrad = [np.ones(hyperparams.vect.size)] def meta_callback(hyperparam_vect, i_hyper, metagrad=None): #x, learning_curve_dict = primal_optimizer(hyperparam_vect, i_hyper) x, learning_curve_dict = cur_primal_results[ 'weights'], cur_primal_results['learning_curve'] cur_hyperparams = hyperparams.new_vect(hyperparam_vect.copy()) for field in cur_hyperparams.names: meta_results[field] = cur_hyperparams[field] meta_results['train_loss'].append(loss_fun(x, **train_data)) meta_results['valid_loss'].append(loss_fun(x, **valid_data)) meta_results['tests_loss'].append(loss_fun(x, **tests_data)) meta_results['test_err'].append(frac_err(x, **tests_data)) meta_results['learning_curves'].append(learning_curve_dict) meta_results['example_weights'] = x if metagrad is not None: meta_results['meta_grad_magnitude'].append( np.linalg.norm(metagrad)) meta_results['meta_grad_angle'].append(np.dot(old_metagrad[0], metagrad) \ / (np.linalg.norm(metagrad)* np.linalg.norm(old_metagrad[0]))) old_metagrad[0] = metagrad print "Meta Epoch {0} Train loss {1:2.4f} Valid Loss {2:2.4f}" \ " Test Loss {3:2.4f} Test Err {4:2.4f}".format( i_hyper, meta_results['train_loss'][-1], meta_results['valid_loss'][-1], meta_results['tests_loss'][-1], meta_results['test_err'][-1]) initial_hypergrad = hyperloss_grad(hyperparams.vect, 0) parsed_init_hypergrad = hyperparams.new_vect(initial_hypergrad.copy()) final_result = adam(hyperloss_grad, hyperparams.vect, meta_callback, N_meta_iter, meta_alpha) meta_callback(final_result, N_meta_iter) parser.vect = None # No need to pickle zeros return meta_results, parser, parsed_init_hypergrad
def run(): train_data, valid_data, tests_data = load_data_dicts(N_train, N_valid, N_tests) parser, pred_fun, loss_fun, frac_err = make_nn_funs(layer_sizes) N_weight_types = len(parser.names) hyperparams = VectorParser() hyperparams['log_param_scale'] = np.full(N_weight_types, init_log_param_scale) hyperparams['log_alphas'] = np.full((N_iters, N_weight_types), init_log_alphas) hyperparams['invlogit_betas'] = np.full((N_iters, N_weight_types), init_invlogit_betas) fixed_hyperparams = VectorParser() fixed_hyperparams['log_L2_reg'] = np.full(N_weight_types, init_log_L2_reg) cur_primal_results = {} def primal_optimizer(hyperparam_vect, i_hyper): def indexed_loss_fun(w, L2_vect, i_iter): rs = RandomState((seed, i_hyper, i_iter)) # Deterministic seed needed for backwards pass. idxs = rs.randint(N_train, size=batch_size) return loss_fun(w, train_data['X'][idxs], train_data['T'][idxs], L2_vect) learning_curve_dict = defaultdict(list) def callback(x, v, g, i_iter): if i_iter % thin == 0: learning_curve_dict['learning_curve'].append(loss_fun(x, **train_data)) learning_curve_dict['grad_norm'].append(np.linalg.norm(g)) learning_curve_dict['weight_norm'].append(np.linalg.norm(x)) learning_curve_dict['velocity_norm'].append(np.linalg.norm(v)) cur_hyperparams = hyperparams.new_vect(hyperparam_vect) rs = RandomState((seed, i_hyper)) W0 = fill_parser(parser, np.exp(cur_hyperparams['log_param_scale'])) W0 *= rs.randn(W0.size) alphas = np.exp(cur_hyperparams['log_alphas']) betas = logit(cur_hyperparams['invlogit_betas']) L2_reg = fill_parser(parser, np.exp(fixed_hyperparams['log_L2_reg'])) W_opt = sgd_parsed(grad(indexed_loss_fun), kylist(W0, alphas, betas, L2_reg), parser, callback=callback) cur_primal_results['weights'] = getval(W_opt).copy() cur_primal_results['learning_curve'] = getval(learning_curve_dict) return W_opt, learning_curve_dict def hyperloss(hyperparam_vect, i_hyper): W_opt, _ = primal_optimizer(hyperparam_vect, i_hyper) return loss_fun(W_opt, **train_data) hyperloss_grad = grad(hyperloss) meta_results = defaultdict(list) old_metagrad = [np.ones(hyperparams.vect.size)] def meta_callback(hyperparam_vect, i_hyper, metagrad=None): x, learning_curve_dict = cur_primal_results['weights'], cur_primal_results['learning_curve'] cur_hyperparams = hyperparams.new_vect(hyperparam_vect.copy()) for field in cur_hyperparams.names: meta_results[field].append(cur_hyperparams[field]) meta_results['train_loss'].append(loss_fun(x, **train_data)) meta_results['valid_loss'].append(loss_fun(x, **valid_data)) meta_results['tests_loss'].append(loss_fun(x, **tests_data)) meta_results['test_err'].append(frac_err(x, **tests_data)) meta_results['learning_curves'].append(learning_curve_dict) meta_results['example_weights'] = x if metagrad is not None: meta_results['meta_grad_magnitude'].append(np.linalg.norm(metagrad)) meta_results['meta_grad_angle'].append(np.dot(old_metagrad[0], metagrad) \ / (np.linalg.norm(metagrad)* np.linalg.norm(old_metagrad[0]))) old_metagrad[0] = metagrad print "Meta Epoch {0} Train loss {1:2.4f} Valid Loss {2:2.4f}" \ " Test Loss {3:2.4f} Test Err {4:2.4f}".format( i_hyper, meta_results['train_loss'][-1], meta_results['valid_loss'][-1], meta_results['train_loss'][-1], meta_results['test_err'][-1]) initial_hypergrad = hyperloss_grad( hyperparams.vect, 0) hypergrads = np.zeros((N_meta_iter, len(initial_hypergrad))) for i in xrange(N_meta_iter): hypergrads[i] = hyperloss_grad( hyperparams.vect, i) print i avg_hypergrad = np.mean(hypergrads, axis=0) parsed_avg_hypergrad = hyperparams.new_vect(avg_hypergrad) parser.vect = None # No need to pickle zeros return parser, parsed_avg_hypergrad
def run(): train_data, valid_data, tests_data = load_data_dicts(N_train, N_valid, N_tests) parser, pred_fun, loss_fun, frac_err = make_nn_funs(layer_sizes) N_weight_types = len(parser.names) hyperparams = VectorParser() hyperparams['log_param_scale'] = np.full(N_weight_types, init_log_param_scale) hyperparams['log_alphas'] = np.full((N_iters, N_weight_types), init_log_alphas) hyperparams['invlogit_betas'] = np.full((N_iters, N_weight_types), init_invlogit_betas) fixed_hyperparams = VectorParser() fixed_hyperparams['log_L2_reg'] = np.full(N_weight_types, init_log_L2_reg) def primal_optimizer(hyperparam_vect, i_hyper): def indexed_loss_fun(w, L2_vect, i_iter): rs = RandomState((seed, i_hyper, i_iter)) # Deterministic seed needed for backwards pass. idxs = rs.randint(N_train, size=batch_size) return loss_fun(w, train_data['X'][idxs], train_data['T'][idxs], L2_vect) learning_curve_dict = defaultdict(list) def callback(x, v, g, i_iter): if i_iter % thin == 0: learning_curve_dict['learning_curve'].append(loss_fun(x, **train_data)) learning_curve_dict['grad_norm'].append(np.linalg.norm(g)) learning_curve_dict['weight_norm'].append(np.linalg.norm(x)) learning_curve_dict['velocity_norm'].append(np.linalg.norm(v)) init_hyperparams = hyperparams.new_vect(hyperparam_vect) rs = RandomState((seed, i_hyper)) W0 = fill_parser(parser, np.exp(init_hyperparams['log_param_scale'])) W0 *= rs.randn(W0.size) alphas = np.exp(init_hyperparams['log_alphas']) betas = logit(init_hyperparams['invlogit_betas']) L2_reg = fill_parser(parser, np.exp(fixed_hyperparams['log_L2_reg'])) W_opt = sgd_parsed(grad(indexed_loss_fun), kylist(W0, alphas, betas, L2_reg), parser, callback=callback) return W_opt, learning_curve_dict def hyperloss(hyperparam_vect, i_hyper): W_opt, _ = primal_optimizer(hyperparam_vect, i_hyper) return loss_fun(W_opt, **train_data) hyperloss_grad = grad(hyperloss) meta_results = defaultdict(list) old_metagrad = [np.ones(hyperparams.vect.size)] def meta_callback(hyperparam_vect, i_hyper, metagrad=None): x, learning_curve_dict = primal_optimizer(hyperparam_vect, i_hyper) cur_hyperparams = hyperparams.new_vect(hyperparam_vect.copy()) for field in cur_hyperparams.names: meta_results[field].append(cur_hyperparams[field]) meta_results['train_loss'].append(loss_fun(x, **train_data)) meta_results['valid_loss'].append(loss_fun(x, **valid_data)) meta_results['tests_loss'].append(loss_fun(x, **tests_data)) meta_results['test_err'].append(frac_err(x, **tests_data)) meta_results['learning_curves'].append(learning_curve_dict) if metagrad is not None: meta_results['meta_grad_magnitude'].append(np.linalg.norm(metagrad)) meta_results['meta_grad_angle'].append(np.dot(old_metagrad[0], metagrad) \ / (np.linalg.norm(metagrad)* np.linalg.norm(old_metagrad[0]))) old_metagrad[0] = metagrad print "Meta Epoch {0} Train loss {1:2.4f} Valid Loss {2:2.4f}" \ " Test Loss {3:2.4f} Test Err {4:2.4f}".format( i_hyper, meta_results['train_loss'][-1], meta_results['valid_loss'][-1], meta_results['train_loss'][-1], meta_results['test_err'][-1]) # Average many gradient evaluations at the initial point. hypergrads = np.zeros((N_gradients_in_average, hyperparams.vect.size)) for i in xrange(N_gradients_in_average): hypergrads[i] = hyperloss_grad(hyperparams.vect, i) print i first_gradient = hypergrads[0] avg_gradient = np.mean(hypergrads, axis=0) # Now do a line search along that direction. parsed_avg_grad = hyperparams.new_vect(avg_gradient) stepsize_scale = stepsize_search_rescale/np.max(np.exp(parsed_avg_grad['log_alphas'].ravel())) stepsizes = np.linspace(-stepsize_scale, stepsize_scale, N_points_in_line_search) for i, stepsize in enumerate(stepsizes): cur_hypervect = hyperparams.vect - stepsize * avg_gradient meta_callback(cur_hypervect, 0) # Use the same random seed every time. parser.vect = None # No need to pickle zeros return meta_results, parser, first_gradient, parsed_avg_grad, stepsizes
meta_results['example_weights'] = x if metagrad is not None: meta_results['meta_grad_magnitude'].append(np.linalg.norm(metagrad)) meta_results['meta_grad_angle'].append(np.dot(old_metagrad[0], metagrad) \ / (np.linalg.norm(metagrad)* np.linalg.norm(old_metagrad[0]))) old_metagrad[0] = metagrad print "Meta Epoch {0} Train loss {1:2.4f} Valid Loss {2:2.4f}" \ " Test Loss {3:2.4f} Test Err {4:2.4f}".format( i_hyper, meta_results['train_loss'][-1], meta_results['valid_loss'][-1], meta_results['train_loss'][-1], meta_results['test_err'][-1]) initial_hypergrad = hyperloss_grad(hyperparams.vect, 0) parsed_init_hypergrad = hyperparams.new_vect(initial_hypergrad.copy()) final_result = adam(hyperloss_grad, hyperparams.vect, meta_callback, N_meta_iter, meta_alpha) meta_callback(final_result, N_meta_iter) parser.vect = None # No need to pickle zeros results = (meta_results, parser, parsed_init_hypergrad) pickle.dump(results, open('results.pkl', 'w')) # -- # Plot results, parser, parsed_init_hypergrad = pickle.load(open('bak/results.pkl')) fig = plt.figure(0) fig.clf() ax = fig.add_subplot(111)
def run(): train_data, valid_data, tests_data = load_data_dicts( N_train, N_valid, N_tests) parser, pred_fun, loss_fun, frac_err = make_nn_funs(layer_sizes) N_weight_types = len(parser.names) hyperparams = VectorParser() hyperparams['log_param_scale'] = np.full(N_weight_types, init_log_param_scale) hyperparams['log_alphas'] = np.full((N_iters, N_weight_types), init_log_alphas) hyperparams['invlogit_betas'] = np.full((N_iters, N_weight_types), init_invlogit_betas) fixed_hyperparams = VectorParser() fixed_hyperparams['log_L2_reg'] = np.full(N_weight_types, init_log_L2_reg) def primal_optimizer(hyperparam_vect, i_hyper): def indexed_loss_fun(w, L2_vect, i_iter): rs = RandomState( (seed, i_hyper, i_iter)) # Deterministic seed needed for backwards pass. idxs = rs.randint(N_train, size=batch_size) return loss_fun(w, train_data['X'][idxs], train_data['T'][idxs], L2_vect) learning_curve_dict = defaultdict(list) def callback(x, v, g, i_iter): if i_iter % thin == 0: learning_curve_dict['learning_curve'].append( loss_fun(x, **train_data)) learning_curve_dict['grad_norm'].append(np.linalg.norm(g)) learning_curve_dict['weight_norm'].append(np.linalg.norm(x)) learning_curve_dict['velocity_norm'].append(np.linalg.norm(v)) init_hyperparams = hyperparams.new_vect(hyperparam_vect) rs = RandomState((seed, i_hyper)) W0 = fill_parser(parser, np.exp(init_hyperparams['log_param_scale'])) W0 *= rs.randn(W0.size) alphas = np.exp(init_hyperparams['log_alphas']) betas = logit(init_hyperparams['invlogit_betas']) L2_reg = fill_parser(parser, np.exp(fixed_hyperparams['log_L2_reg'])) W_opt = sgd_parsed(grad(indexed_loss_fun), kylist(W0, alphas, betas, L2_reg), parser, callback=callback) return W_opt, learning_curve_dict def hyperloss(hyperparam_vect, i_hyper): W_opt, _ = primal_optimizer(hyperparam_vect, i_hyper) return loss_fun(W_opt, **train_data) hyperloss_grad = grad(hyperloss) meta_results = defaultdict(list) old_metagrad = [np.ones(hyperparams.vect.size)] def meta_callback(hyperparam_vect, i_hyper, metagrad=None): x, learning_curve_dict = primal_optimizer(hyperparam_vect, i_hyper) cur_hyperparams = hyperparams.new_vect(hyperparam_vect.copy()) for field in cur_hyperparams.names: meta_results[field].append(cur_hyperparams[field]) meta_results['train_loss'].append(loss_fun(x, **train_data)) meta_results['valid_loss'].append(loss_fun(x, **valid_data)) meta_results['tests_loss'].append(loss_fun(x, **tests_data)) meta_results['test_err'].append(frac_err(x, **tests_data)) meta_results['learning_curves'].append(learning_curve_dict) if metagrad is not None: meta_results['meta_grad_magnitude'].append( np.linalg.norm(metagrad)) meta_results['meta_grad_angle'].append(np.dot(old_metagrad[0], metagrad) \ / (np.linalg.norm(metagrad)* np.linalg.norm(old_metagrad[0]))) old_metagrad[0] = metagrad print "Meta Epoch {0} Train loss {1:2.4f} Valid Loss {2:2.4f}" \ " Test Loss {3:2.4f} Test Err {4:2.4f}".format( i_hyper, meta_results['train_loss'][-1], meta_results['valid_loss'][-1], meta_results['train_loss'][-1], meta_results['test_err'][-1]) # Average many gradient evaluations at the initial point. hypergrads = np.zeros((N_gradients_in_average, hyperparams.vect.size)) for i in xrange(N_gradients_in_average): hypergrads[i] = hyperloss_grad(hyperparams.vect, i) print i first_gradient = hypergrads[0] avg_gradient = np.mean(hypergrads, axis=0) # Now do a line search along that direction. parsed_avg_grad = hyperparams.new_vect(avg_gradient) stepsize_scale = 1000. / np.max( np.exp(parsed_avg_grad['log_alphas'].ravel())) stepsizes = np.linspace(-stepsize_scale, stepsize_scale, N_points_in_line_search) for i, stepsize in enumerate(stepsizes): cur_hypervect = hyperparams.vect + stepsize * avg_gradient meta_callback(cur_hypervect, 0) # Use the same random seed every time. parser.vect = None # No need to pickle zeros return meta_results, parser, first_gradient, avg_gradient, stepsizes
def run(): train_data, valid_data, tests_data = load_data_dicts( N_train, N_valid, N_tests) parser, pred_fun, loss_fun, frac_err = make_nn_funs(layer_sizes) N_weight_types = len(parser.names) hyperparams = VectorParser() hyperparams['log_param_scale'] = np.full(N_weight_types, init_log_param_scale) hyperparams['log_alphas'] = np.full((N_iters, N_weight_types), init_log_alphas) hyperparams['invlogit_betas'] = np.full((N_iters, N_weight_types), init_invlogit_betas) fixed_hyperparams = VectorParser() fixed_hyperparams['log_L2_reg'] = np.full(N_weight_types, init_log_L2_reg) def primal_optimizer(hyperparam_vect, i_hyper): def indexed_loss_fun(w, L2_vect, i_iter): rs = RandomState( (seed, i_hyper, i_iter)) # Deterministic seed needed for backwards pass. idxs = rs.randint(N_train, size=batch_size) return loss_fun(w, train_data['X'][idxs], train_data['T'][idxs], L2_vect) learning_curve_dict = defaultdict(list) def callback(x, v, g, i_iter): if i_iter % thin == 0: learning_curve_dict['learning_curve'].append( loss_fun(x, **train_data)) learning_curve_dict['grad_norm'].append(np.linalg.norm(g)) learning_curve_dict['weight_norm'].append(np.linalg.norm(x)) learning_curve_dict['velocity_norm'].append(np.linalg.norm(v)) cur_hyperparams = hyperparams.new_vect(hyperparam_vect) rs = RandomState((seed, i_hyper)) W0 = fill_parser(parser, np.exp(cur_hyperparams['log_param_scale'])) W0 *= rs.randn(W0.size) alphas = np.exp(cur_hyperparams['log_alphas']) betas = logit(cur_hyperparams['invlogit_betas']) L2_reg = fill_parser(parser, np.exp(fixed_hyperparams['log_L2_reg'])) W_opt = sgd_parsed(grad(indexed_loss_fun), kylist(W0, alphas, betas, L2_reg), parser, callback=callback) return W_opt, learning_curve_dict def hyperloss(hyperparam_vect, i_hyper): W_opt, _ = primal_optimizer(hyperparam_vect, i_hyper) return loss_fun(W_opt, **train_data) hyperloss_grad = grad(hyperloss) initial_hypergrad = hyperloss_grad(hyperparams.vect, 0) parsed_init_hypergrad = hyperparams.new_vect(initial_hypergrad.copy()) avg_hypergrad = initial_hypergrad.copy() for i in xrange(1, N_meta_iter): avg_hypergrad += hyperloss_grad(hyperparams.vect, i) print i parsed_avg_hypergrad = hyperparams.new_vect(avg_hypergrad) parser.vect = None # No need to pickle zeros return parser, parsed_init_hypergrad, parsed_avg_hypergrad