def callback(weights, iter): if iter % 10 == 0: print("max of weights", np.max(np.abs(weights))) train_preds = undo_norm( pred_fun(weights, train_smiles[:num_print_examples])) cur_loss = loss_fun(weights, train_smiles[:num_print_examples], train_targets[:num_print_examples]) training_curve.append(cur_loss) print( "Iteration", iter, "loss", cur_loss, "train RMSE", rmse(train_preds, train_raw_targets[:num_print_examples]), ) if validation_smiles is not None: validation_preds = undo_norm( pred_fun(weights, validation_smiles)) print( "Validation RMSE", iter, ":", rmse(validation_preds, validation_raw_targets), )
def print_performance(pred_func): train_preds = pred_func(train_inputs) val_preds = pred_func(val_inputs) print("\nPerformance (RMSE) on " + task_params['target_name'] + ":") print("Train:", rmse(train_preds, train_targets)) print("Test: ", rmse(val_preds, val_targets)) print("-" * 80) return rmse(val_preds, val_targets)
def print_performance(pred_func): train_preds = pred_func(train_inputs) val_preds = pred_func(val_inputs) print "\nPerformance (RMSE) on " + task_params['target_name'] + ":" print "Train:", rmse(train_preds, train_targets) print "Test: ", rmse(val_preds, val_targets) print "-" * 80 return rmse(val_preds, val_targets)
def callback(weights, iter): if iter % 10 == 0: print "max of weights", np.max(np.abs(weights)) train_preds = undo_norm(pred_fun(weights, train_smiles)) cur_loss = loss_fun(weights, train_smiles, train_targets) training_curve.append(cur_loss) print "Iteration", iter, "loss", cur_loss, "train RMSE", rmse(train_preds, train_raw_targets), if validation_smiles is not None: validation_preds = undo_norm(pred_fun(weights, validation_smiles)) print "Validation RMSE", iter, ":", rmse(validation_preds, validation_raw_targets),
def callback(weights, iter): if iter % 10 == 0: print "max of weights", np.max(np.abs(weights)) # import pdb; pdb.set_trace() train_preds = undo_norm(pred_fun(weights, train_smiles[:num_print_examples])) cur_loss = loss_fun(weights, train_smiles[:num_print_examples], train_targets[:num_print_examples]) # V: refers to line number #78 i.e. # def loss_fun(weights, smiles, targets) of build_vanilla_net.py training_curve.append(cur_loss) print "Iteration", iter, "loss", cur_loss,\ "train RMSE", rmse(train_preds, train_raw_targets[:num_print_examples]), if validation_smiles is not None: validation_preds = undo_norm(pred_fun(weights, validation_smiles)) print "Validation RMSE", iter, ":", rmse(validation_preds, validation_raw_targets),
def run_conv_experiment(): conv_layer_sizes = [model_params['conv_width']] * model_params['fp_depth'] conv_arch_params = {'num_hidden_features' : conv_layer_sizes, 'fp_length' : model_params['fp_length'], 'normalize' : 1} loss_fun, pred_fun, conv_parser = \ build_conv_deep_net(conv_arch_params, vanilla_net_params, model_params['L2_reg']) num_weights = len(conv_parser) predict_func, trained_weights, conv_training_curve = \ train_nn(pred_fun, loss_fun, num_weights, train_inputs, train_targets, train_params, validation_smiles=val_inputs, validation_raw_targets=val_targets) test_predictions = predict_func(test_inputs) return rmse(test_predictions, test_targets)