def plot_loss_acc_history(fit_history, optimizer_name): plotter = LossAccPlotter( title=optimizer + ': Loss and Accuracy Performance', save_to_filepath='loss_acc_plots/' + optimizer + '.png', show_regressions=True, show_averages=False, show_loss_plot=True, show_acc_plot=True, show_plot_window=True, x_label="Epoch") num_epochs = len(fit_history['acc']) for epoch in range(num_epochs): acc_train = fit_history['acc'][epoch] loss_train = fit_history['loss'][epoch] acc_val = fit_history['val_acc'][epoch] loss_val = fit_history['val_loss'][epoch] plotter.add_values(epoch, loss_train=loss_train, acc_train=acc_train, loss_val=loss_val, acc_val=acc_val, redraw=False) plotter.redraw() plotter.block()
def plot_losses(data, suffix, fi_len, batch_per_epoch, **kwargs): # plot_data = {'X': [], 'Y': [], 'legend': []} other_loss = OrderedDict() # plot settings save_to_filepath = os.path.join("{}_log".format(data), "{}_plot_losses.png".format(suffix)) plotter = LossAccPlotter(title="{} loss over time".format(suffix), save_to_filepath=save_to_filepath, show_regressions=False, show_averages=False, show_other_loss=True, show_log_loss=True, show_loss_plot=True, show_err_plot=True, show_plot_window=False, epo_max=1000, x_label="Epoch") ## load loss data log_path = kwargs["log_path"] log_file2 = open(log_path, "r") st_time = time.time() i = 0 for li in log_file2: li_or = li.split(" | ") if len(li_or) == 1: continue iter = li_or[0].split("\t")[0][1:] loss_train = str2flo(li_or[0].split(":")[1].split(",")[0]) err_train = str2flo(li_or[0].split(",")[1]) loss_val = str2flo(li_or[1].split(":")[1].split(",")[0]) err_val = str2flo(li_or[1].split(",")[1]) for li2 in li_or: if "loss" not in li2: continue # pdb.set_trace() key = li2.split(": ")[0] value = str2flo(li2.split(": ")[1]) if key == 'vi loss': value *= 1e-2 other_loss[key] = value float_epoch = str2flo(iter) / batch_per_epoch plotter.add_values(float_epoch, loss_train=loss_train, loss_val=loss_val, err_train=err_train, err_val=err_val, redraw=False, other_loss=other_loss) i += 1 time_str = "{}\r".format( calculate_remaining(st_time, time.time(), i, fi_len)) # print(time_string, end = '\r') sys.stdout.write(time_str) sys.stdout.flush() sys.stdout.write("\n") sys.stdout.flush() log_file2.close() plotter.redraw() # save as image
def plot_loss_err(data, suffix, fi_len, ema, batch_per_epoch, **kwargs): ## load loss data log_path = kwargs["log_path"] log_file2 = open(log_path, "r") st_time = time.time() # plot settings save_to_filepath = os.path.join("{}_log".format(data), "{}_plot_loss_err.png".format(suffix)) plotter = LossAccPlotter(title="{} loss over time".format(suffix), save_to_filepath=save_to_filepath, show_regressions=True, show_averages=True, show_loss_plot=True, show_err_plot=True, show_ema_plot=ema, show_plot_window=False, x_label="Epoch") i = 0 for li in log_file2: li_or = li.split(" | ") if len(li_or) == 1: continue iter = li_or[0].split("\t")[0][1:] loss_train = str2flo(li_or[0].split(":")[1].split(",")[0]) err_train = str2flo(li_or[0].split(",")[1]) loss_val = str2flo(li_or[1].split(":")[1].split(",")[0]) err_val = str2flo(li_or[1].split(",")[1]) ema_err_train = ema_err_val = None if ema: ema_err_train = li_or[3].split(":")[1].split(",")[0] ema_err_val = li_or[3].split(",")[1] if "None" not in ema_err_train: ema_err_train = str2flo(ema_err_train) ema_err_val = str2flo(ema_err_val) else: ema_err_train = ema_err_val = None float_epoch = str2flo(iter) / batch_per_epoch plotter.add_values(float_epoch, loss_train=loss_train, loss_val=loss_val, err_train=err_train, err_val=err_val, ema_err_train=ema_err_train, ema_err_val=ema_err_val, redraw=False) i += 1 time_str = "{}\r".format( calculate_remaining(st_time, time.time(), i, fi_len)) sys.stdout.write(time_str) sys.stdout.flush() sys.stdout.write("\n") log_file2.close() plotter.redraw() # save as image
def plotResults(title, a_train, c_train, a_val, c_val): plotter = LossAccPlotter(title=title, show_averages=False, save_to_filepath=plot_loc + "lossAcc_{}.png".format(title), show_plot_window=show_not_save) for e in range(len(a_train)): plotter.add_values(e, loss_train=c_train[e], acc_train=a_train[e], loss_val=c_val[e], acc_val=a_val[e], redraw=False) plotter.redraw() plotter.block()
def plot_image(title, filename, training, validation): plotter = LossAccPlotter(title, save_to_filepath=os.path.join( input_dir, filename), show_regressions=False, show_averages=False, show_loss_plot=True, show_acc_plot=True, show_plot_window=False, x_label="Iteration") # Store counter for next validation row to plot next_validation_row_index_to_plot = 0 for row in range(len(training)): training_record = training[row] training_iter = training_record[1] loss_train = training_record[2] # Plot Both Training and Validation Record if len(validation) > next_validation_row_index_to_plot and \ validation[next_validation_row_index_to_plot][1] == training_iter: val_record = validation[next_validation_row_index_to_plot] next_validation_row_index_to_plot = next_validation_row_index_to_plot + 1 loss_val = val_record[2] if len(val_record) == 3: plotter.add_values(training_iter, loss_train=loss_train, loss_val=loss_val, redraw=False) elif len(val_record) > 3: inst_acc_val = val_record[4] inst_acc_train = training_record[4] plotter.add_values(training_iter, loss_train=loss_train, loss_val=loss_val, acc_train=inst_acc_train, acc_val=inst_acc_val, redraw=False) else: # Plot Training Record only if len(training_record) == 3: plotter.add_values(training_iter, loss_train=loss_train, redraw=False) elif len(training_record) > 3: # Valid value inst_acc_train = training_record[4] plotter.add_values(training_iter, loss_train=loss_train, acc_train=inst_acc_train, redraw=False) plotter.redraw() plotter.block()
def show_chart(loss_train, loss_val, acc_train, acc_val, lap=None, title=None): """Shows a plot using the LossAccPlotter and all provided values. Args: loss_train: y-values of the loss function of the training dataset. loss_val: y-values of the loss function of the validation dataset. acc_train: y-values of the accuracy of the training dataset. acc_val: y-values of the accuracy of the validation dataset. lap: A LossAccPlotter-Instance or None. If None then a new LossAccPlotter will be instantiated. (Default is None.) title: The title to use for the plot, i.e. LossAccPlotter.title . """ lap = LossAccPlotter() if lap is None else lap # set the plot title, which will be shown at the very top of the plot if title is not None: lap.title = title # add loss train line/values for idx in range(loss_train.shape[0]): lt_val = loss_train[idx] if loss_train[idx] != -1.0 else None lap.add_values(idx, loss_train=lt_val, redraw=False) # add loss validation line/values for idx in range(loss_val.shape[0]): lv_val = loss_val[idx] if loss_val[idx] != -1.0 else None lap.add_values(idx, loss_val=lv_val, redraw=False) # add accuracy training line/values for idx in range(acc_train.shape[0]): at_val = acc_train[idx] if acc_train[idx] != -1.0 else None lap.add_values(idx, acc_train=at_val, redraw=False) # add accuracy validation line/values for idx in range(acc_val.shape[0]): av_val = acc_val[idx] if acc_val[idx] != -1.0 else None lap.add_values(idx, acc_val=av_val, redraw=False) # redraw once after adding all values, because that's significantly # faster than redrawing many times lap.redraw() # block at the end so that the plot does not close immediatly. print("Close the chart to continue.") lap.block()
def main(): """Run various checks on the LossAccPlotter. They all follow the same pattern: Generate some random data (lines) to display. Then display them (using various settings). """ print("") print("------------------") print("1 datapoint") print("------------------") # generate example values for: loss train, loss validation, accuracy train # and accuracy validation (loss_train, loss_val, acc_train, acc_val) = create_values(1) # generate a plot showing the example values show_chart(loss_train, loss_val, acc_train, acc_val, title="A single datapoint") print("") print("------------------") print("150 datapoints") print("Saved to file 'plot.png'") print("------------------") (loss_train, loss_val, acc_train, acc_val) = create_values(150) show_chart(loss_train, loss_val, acc_train, acc_val, lap=LossAccPlotter(save_to_filepath="plot.png"), title="150 datapoints, saved to file 'plot.png'") print("") print("------------------") print("150 datapoints") print("No accuracy chart") print("------------------") (loss_train, loss_val, _, _) = create_values(150) show_chart(loss_train, loss_val, np.array([]), np.array([]), lap=LossAccPlotter(show_acc_plot=False), title="150 datapoints, no accuracy chart") print("") print("------------------") print("150 datapoints") print("No loss chart") print("------------------") (_, _, acc_train, acc_val) = create_values(150) show_chart(np.array([]), np.array([]), acc_train, acc_val, lap=LossAccPlotter(show_loss_plot=False), title="150 datapoints, no loss chart") print("") print("------------------") print("150 datapoints") print("No accuracy chart") print("------------------") (loss_train, loss_val, _, _) = create_values(150) show_chart(loss_train, loss_val, np.array([]), np.array([]), lap=LossAccPlotter(show_acc_plot=False), title="150 datapoints, no accuracy chart") print("") print("------------------") print("150 datapoints") print("Only validation values (no training lines)") print("------------------") (_, loss_val, _, acc_val) = create_values(150) show_chart(np.array([]), loss_val, np.array([]), acc_val, title="150 datapoints, only validation (no training)") print("") print("------------------") print("150 datapoints") print("No regressions") print("------------------") (loss_train, loss_val, acc_train, acc_val) = create_values(150) show_chart(loss_train, loss_val, acc_train, acc_val, lap=LossAccPlotter(show_regressions=False), title="150 datapoints, regressions deactivated") print("") print("------------------") print("150 datapoints") print("No averages") print("------------------") (loss_train, loss_val, acc_train, acc_val) = create_values(150) show_chart(loss_train, loss_val, acc_train, acc_val, lap=LossAccPlotter(show_averages=False), title="150 datapoints, averages deactivated") print("") print("------------------") print("150 datapoints") print("x-index 5 of loss_train should create a warning as its set to NaN") print("------------------") (loss_train, loss_val, acc_train, acc_val) = create_values(150) # this should create a warning when LossAccPlotter.add_values() gets called. loss_train[5] = float("nan") show_chart( loss_train, loss_val, acc_train, acc_val, title="150 datapoints, one having value NaN (loss train at x=5)") print("") print("------------------") print("1000 datapoints training") print("100 datapoints validation") print("------------------") nb_points_train = 1000 nb_points_val = 100 (loss_train, loss_val, acc_train, acc_val) = create_values(nb_points_train) # set 9 out of 10 values of the validation arrays to -1.0 (Which will be # interpreted as None in show_chart(). Numpy doesnt support None directly, # only NaN, which is already used before to check whether the Plotter # correctly creates a warning if any data point is NaN.) all_indices = np.arange(0, nb_points_train - 1, 1) keep_indices = np.arange(0, nb_points_train - 1, int(nb_points_train / nb_points_val)) set_to_none_indices = np.delete(all_indices, keep_indices) loss_val[set_to_none_indices] = -1.0 acc_val[set_to_none_indices] = -1.0 show_chart( loss_train, loss_val, acc_train, acc_val, title="1000 training datapoints, but only 100 validation datapoints") print("") print("------------------") print("5 datapoints") print("slowly added, one by one") print("------------------") (loss_train, loss_val, acc_train, acc_val) = create_values(5) lap = LossAccPlotter(title="5 datapoints, slowly added one by one") for idx in range(loss_train.shape[0]): lap.add_values(idx, loss_train=loss_train[idx], loss_val=loss_val[idx], acc_train=acc_train[idx], acc_val=acc_val[idx], redraw=True) sleep(1.0) print("Close the chart to continue.") lap.block()
def main(): """Run various checks on the LossAccPlotter. They all follow the same pattern: Generate some random data (lines) to display. Then display them (using various settings). """ print("") print("------------------") print("1 datapoint") print("------------------") # generate example values for: loss train, loss validation, accuracy train # and accuracy validation (loss_train, loss_val, acc_train, acc_val) = create_values(1) # generate a plot showing the example values show_chart(loss_train, loss_val, acc_train, acc_val, title="A single datapoint") print("") print("------------------") print("150 datapoints") print("Saved to file 'plot.png'") print("------------------") (loss_train, loss_val, acc_train, acc_val) = create_values(150) show_chart(loss_train, loss_val, acc_train, acc_val, lap=LossAccPlotter(save_to_filepath="plot.png"), title="150 datapoints, saved to file 'plot.png'") print("") print("------------------") print("150 datapoints") print("No accuracy chart") print("------------------") (loss_train, loss_val, _, _) = create_values(150) show_chart(loss_train, loss_val, np.array([]), np.array([]), lap=LossAccPlotter(show_acc_plot=False), title="150 datapoints, no accuracy chart") print("") print("------------------") print("150 datapoints") print("No loss chart") print("------------------") (_, _, acc_train, acc_val) = create_values(150) show_chart(np.array([]), np.array([]), acc_train, acc_val, lap=LossAccPlotter(show_loss_plot=False), title="150 datapoints, no loss chart") print("") print("------------------") print("150 datapoints") print("No accuracy chart") print("------------------") (loss_train, loss_val, _, _) = create_values(150) show_chart(loss_train, loss_val, np.array([]), np.array([]), lap=LossAccPlotter(show_acc_plot=False), title="150 datapoints, no accuracy chart") print("") print("------------------") print("150 datapoints") print("Only validation values (no training lines)") print("------------------") (_, loss_val, _, acc_val) = create_values(150) show_chart(np.array([]), loss_val, np.array([]), acc_val, title="150 datapoints, only validation (no training)") print("") print("------------------") print("150 datapoints") print("No regressions") print("------------------") (loss_train, loss_val, acc_train, acc_val) = create_values(150) show_chart(loss_train, loss_val, acc_train, acc_val, lap=LossAccPlotter(show_regressions=False), title="150 datapoints, regressions deactivated") print("") print("------------------") print("150 datapoints") print("No averages") print("------------------") (loss_train, loss_val, acc_train, acc_val) = create_values(150) show_chart(loss_train, loss_val, acc_train, acc_val, lap=LossAccPlotter(show_averages=False), title="150 datapoints, averages deactivated") print("") print("------------------") print("150 datapoints") print("x-index 5 of loss_train should create a warning as its set to NaN") print("------------------") (loss_train, loss_val, acc_train, acc_val) = create_values(150) # this should create a warning when LossAccPlotter.add_values() gets called. loss_train[5] = float("nan") show_chart(loss_train, loss_val, acc_train, acc_val, title="150 datapoints, one having value NaN (loss train at x=5)") print("") print("------------------") print("1000 datapoints training") print("100 datapoints validation") print("------------------") nb_points_train = 1000 nb_points_val = 100 (loss_train, loss_val, acc_train, acc_val) = create_values(nb_points_train) # set 9 out of 10 values of the validation arrays to -1.0 (Which will be # interpreted as None in show_chart(). Numpy doesnt support None directly, # only NaN, which is already used before to check whether the Plotter # correctly creates a warning if any data point is NaN.) all_indices = np.arange(0, nb_points_train-1, 1) keep_indices = np.arange(0, nb_points_train-1, int(nb_points_train / nb_points_val)) set_to_none_indices = np.delete(all_indices, keep_indices) loss_val[set_to_none_indices] = -1.0 acc_val[set_to_none_indices] = -1.0 show_chart(loss_train, loss_val, acc_train, acc_val, title="1000 training datapoints, but only 100 validation datapoints") print("") print("------------------") print("5 datapoints") print("slowly added, one by one") print("------------------") (loss_train, loss_val, acc_train, acc_val) = create_values(5) lap = LossAccPlotter(title="5 datapoints, slowly added one by one") for idx in range(loss_train.shape[0]): lap.add_values(idx, loss_train=loss_train[idx], loss_val=loss_val[idx], acc_train=acc_train[idx], acc_val=acc_val[idx], redraw=True) sleep(1.0) print("Close the chart to continue.") lap.block()
class LSTM: def __init__(self, args, data, tuning): self.FLAGS = args self.data = data self.tuning = tuning self.embedding_init = embed(self.data) self.model = LSTMClassifier(self.FLAGS, self.embedding_init) logits = self.model.inference() self.train_loss = self.model.loss(logits) self.train_op = self.model.training(self.train_loss[0]) pred = self.model.inference(forward_only=True) self.test_loss = self.model.loss(pred, forward_only=True) # Visualizing loss function and accuracy during training over epochs self.plotter = LossAccPlotter(title="Training plots", save_to_filepath="../img/lstm_plot.png", show_regressions=False, show_averages=False, show_loss_plot=True, show_acc_plot=True, show_plot_window=True, x_label="Epoch") self.init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) print("Network initialized..") def train_lstm(self, FLAGS, data): model = self.model scoring_list = [] best_eval_score = [] with tf.Session(config=tf.ConfigProto( allow_soft_placement=FLAGS.allow_soft_placement, log_device_placement=FLAGS.log_device_placement)) as sess: t0 = time.time() saver = tf.train.Saver() writer = tf.summary.FileWriter("../output") if FLAGS.restore and FLAGS.checkpoint_file: print() print("Loading model from '%s' .." % FLAGS.checkpoint_file) print() saver.restore(sess, FLAGS.checkpoint_file) # feed = {model.x: data.dev_x, model.y: data.dev_y, model.seq_len: data.dev_size} feed = { model.x: data.test_x, model.y: data.test_y, model.seq_len: data.test_size } test_loss_value, test_score = self.final_test(sess, feed) return test_score else: sess.run(self.init) coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(sess=sess, coord=coord) np.random.seed(FLAGS.random_state) for epoch in range(FLAGS.max_epoch): data.reset_batch_pointer() total_loss = 0.0 total_acc = 0.0 for step in range(data.num_batches): x, y, seq_length, _, _, _, _, _ = data.next_batch() feed = { model.x: x, model.y: y, model.seq_len: seq_length } sess.run(self.train_op, feed) current_loss, current_acc, _ = sess.run( self.train_loss, feed) total_loss += current_loss total_acc += current_acc if not self.tuning: print() print( "Epoch {:2d}: Training loss = {:.6f}; Training Accuracy = {:.5f}" .format(epoch + 1, total_loss / data.num_batches, total_acc / data.num_batches)) feed = { model.x: data.dev_x, model.y: data.dev_y, model.seq_len: data.dev_size } dev_loss_value, dev_score, early_stop, eval_summary = self.eval(sess, feed, saver, FLAGS.early_stopping_rounds, \ scoring_list, False, FLAGS.scoring_metrics) best_eval_score.append(dev_score) # str_summary_type = 'train' # loss_summ = tf.scalar_summary("{0}_loss".format(str_summary_type), self.total_train_loss.assign(total_loss/data.num_batches)) # acc_summ = tf.scalar_summary("{0}_accuracy".format(str_summary_type), self.total_train_acc.assign(total_acc/data.num_batches)) # train_summary = tf.merge_summary([loss_summ, acc_summ]) # writer.add_summary(train_summary, epoch) # writer.add_summary(eval_summary, epoch) self.plotter.add_values( epoch, loss_train=total_loss / data.num_batches, acc_train=total_acc / data.num_batches, loss_val=dev_loss_value, acc_val=dev_score[0]) if early_stop: print('Early stopping after %s epoches...' % str(epoch)) best_eval_score = max(best_eval_score,key=itemgetter(1)) if FLAGS.scoring_metrics=='3classf1' else \ max(best_eval_score,key=itemgetter(0)) if FLAGS.scoring_metrics=='accuracy' \ else max(best_eval_score,key=itemgetter(2)) print( "Final dev loss = {:.5f}; Dev Accuracy = {:.5f}; 3-class F1 = {:.5f}; 2-class F1 = {:.5f}" .format(dev_loss_value, best_eval_score[0], best_eval_score[1], best_eval_score[2])) if not self.tuning: t1 = time.time() print("time taken: %f mins" % ((t1 - t0) / 60)) break writer.close() feed = { model.x: data.test_x, model.y: data.test_y, model.seq_len: data.test_size } test_loss_value, test_score = self.final_test(sess, feed) coord.request_stop() coord.join(threads) # if not FLAGS.restore: # self.plotter.block() return test_score, best_eval_score def eval(self, session, feed, saver, early_stopping_rounds, early_stopping_metric_list, early_stopping_metric_minimize=False, metrics='accuracy'): test_loss_value, acc_test, pred, eval_summary = session.run( self.test_loss, feed) f1_3class, f1_2class = fscores(self.data.dev_y, pred) if not self.tuning: print( "*** Validation Loss = {:.6f}; Validation Accuracy = {:.5f}; 3-class F1 = {:.5f}; 2-class F1 = {:.5f}" .format(test_loss_value, acc_test, f1_3class, f1_2class)) print() early_stop = False early_stopping_score = -1 if metrics == 'accuracy': early_stopping_score = acc_test early_stopping_metric_list.append(acc_test) elif metrics == '3classf1': early_stopping_score = f1_3class early_stopping_metric_list.append(f1_3class) elif metrics == '2classf1': early_stopping_score = f1_2class early_stopping_metric_list.append(f1_2class) assert early_stopping_score > 0 if (not self.FLAGS.restore) and (early_stopping_metric_minimize ): # For minimising the eval score if all(early_stopping_score <= i for i in early_stopping_metric_list): saver.save(session, self.FLAGS.checkpoint_file) best_eval_score = (acc_test, f1_3class, f1_2class) if early_stopping_metric_list[::-1].index( min(early_stopping_metric_list)) > early_stopping_rounds: early_stop = True return (test_loss_value, (acc_test, f1_3class, f1_2class), early_stop) elif not (self.FLAGS.restore and early_stopping_metric_minimize ): # For maximising the eval score if all(early_stopping_score >= i for i in early_stopping_metric_list): saver.save(session, self.FLAGS.checkpoint_file) best_eval_score = (acc_test, f1_3class, f1_2class) if early_stopping_metric_list[::-1].index( max(early_stopping_metric_list)) > early_stopping_rounds: early_stop = True return (test_loss_value, (acc_test, f1_3class, f1_2class), early_stop, eval_summary) def final_test(self, session, feed): tf.train.Saver().restore(session, self.FLAGS.checkpoint_file) test_loss_value, acc_test, pred, _ = session.run(self.test_loss, feed) true_labels = self.data.test_y f1_3class, f1_2class = fscores(true_labels, pred) print( "****** Final test Loss = {:.6f}; Test Accuracy = {:.5f}; 3-class F1 = {:.5f}; 2-class F1 = {:.5f}" .format(test_loss_value, acc_test, f1_3class, f1_2class)) print() return (test_loss_value, (acc_test, f1_3class, f1_2class))
def train(instrument, batch_size, latent_dim, epochs, mode=None): data_loader = load_data(instrument, batch_size) generator = Generator(latent_dim) generator.apply(weights_init) discriminator = Discriminator() discriminator.apply(weights_init) # print("Generator's state_dict:") # for param_tensor in generator.state_dict(): # print(param_tensor, "\t", generator.state_dict()[param_tensor].size()) # print("Discriminator's state_dict:") # for param_tensor in discriminator.state_dict(): # print(param_tensor, "\t", discriminator.state_dict()[param_tensor].size()) g_optimizer = optim.Adam(generator.parameters(), lr=0.002, betas=(0.5, 0.999)) d_optimizer = optim.Adam(discriminator.parameters(), lr=0.002, betas=(0.5, 0.999)) loss = nn.BCELoss() if not os.path.exists("./plots/" + instrument): os.makedirs("./plots/" + instrument) plotter = LossAccPlotter(save_to_filepath="./loss/" + instrument + "/loss.png", show_regressions=False, show_acc_plot=False, show_averages=False, show_plot_window=True, x_label="Epoch") if not os.path.exists("./model/" + instrument): os.makedirs("./model/" + instrument) if not os.path.exists("./loss/" + instrument): os.makedirs("./loss/" + instrument) epoch = 0 d_loss_list = [] g_loss_list = [] while True: for num_batch, real_data in enumerate(data_loader): ############################ # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z))) ########################### for i in range(1): real_data_d = next(iter(data_loader)) size = real_data_d.size(0) real_data_t = torch.ones(size, 239, 4) for i in range(size): for j in range(239): real_data_t[i][j] = torch.log( real_data_d[i, j + 1, :] / real_data_d[i, j, 3]) y_real = Variable(torch.ones(size, 1, 1)) y_fake = Variable(torch.zeros(size, 1, 1)) real_data = Variable(real_data_t.float()) fake_data = Variable( torch.from_numpy( np.random.normal(0, 0.2, (size, latent_dim, 1))).float()) fake_gen = generator(fake_data).detach() prediction_real = discriminator(real_data) loss_real = loss(prediction_real, y_real) prediction_fake = discriminator(fake_gen) loss_fake = loss(prediction_fake, y_fake) d_loss = loss_real + loss_fake d_optimizer.zero_grad() d_loss.backward() d_optimizer.step() ############################ # (2) Update G network: maximize log(D(G(z))) ########################### size = real_data.size(0) y_real = Variable(torch.ones(size, 1, 1)) fake_data = Variable( torch.from_numpy( np.random.normal(0, 0.2, (size, latent_dim, 1))).float()) fake_gen = generator(fake_data) prediction = discriminator(fake_gen) g_loss = loss(prediction, y_real) g_optimizer.zero_grad() g_loss.backward() g_optimizer.step() if num_batch % 20 == 0: tmp = torch.ones(size, 239, 4) data = real_data if num_batch == 0 else fake_gen for batch in range(size): for t in range(238, 0, -1): data[batch, t, 0] = torch.sum( data[batch, 0:t, 3]) + data[batch, t, 0] data[batch, t, 1] = torch.sum( data[batch, 0:t, 3]) + data[batch, t, 1] data[batch, t, 2] = torch.sum( data[batch, 0:t, 3]) + data[batch, t, 2] data[batch, t, 3] = torch.sum(data[batch, 0:t + 1, 3]) data = torch.exp(data) tmp = tmp * data print("epoch: %d, num_batch: %d, d-loss: %.4f, g-loss: %.4f" % (epoch, num_batch, d_loss.data.numpy(), g_loss.data.numpy())) visualize(instrument, tmp, epoch, num_batch) plotter.add_values(epoch, loss_train=g_loss.item(), loss_val=d_loss.item()) d_loss_list.append(d_loss.item()) g_loss_list.append(g_loss.item()) if epoch % 10 == 0: torch.save( generator, "./model/" + instrument + "/generator_epoch_" + str(epoch) + ".model") torch.save( discriminator, "./model/" + instrument + "/discriminator_epoch_" + str(epoch) + ".model") d_loss_np = np.array(d_loss_list) np.save( "./loss/" + instrument + "/d_loss_epoch_" + str(epoch) + ".npy", d_loss_np) g_loss_np = np.array(g_loss_list) np.save( "./loss/" + instrument + "/g_loss_epoch_" + str(epoch) + ".npy", g_loss_np) epoch += 1 if mode == "test" and epoch == epochs: break
x = Variable(x) x.to_gpu() # forward print("Computing the loss") loss = model(x, t) now = time.time() - start print("batch: %d input size: %dx%d learning rate: %f loss: %f time: %f" % (batch, input_height, input_width, optimizer.lr, loss.data, now)) print("/////////////////////////////////////") now = time.time() - start #print("[batch %d (%d images)] learning rate: %f, loss: %f, accuracy: %f, time: %f" % (batch+1, (batch+1) * batch_size, optimizer.lr, loss.data, accuracy.data, now)) #to avoid the first loss very high errors (due to??) if batch > 500 : plotter.add_values(batch,loss_train=loss.data) # backward and optimize model.cleargrads() loss.backward() print("Updating the weights") optimizer.update() if (batch+1) %1500 == 0: model_file = "%s/%s.model" % (backup_path, batch+1) print("saving model to %s" % (model_file)) serializers.save_hdf5(model_file, model) serializers.save_hdf5(backup_file, model)
loss_train = [1.78287,29.1449,7.78841e-07,13.6862,6.53347,0,20.7715,13.6161,22.2351,0.738925,13.0908,0.777441,0.0472846,5.02116,13.1627,18.9485,0.210569,11.624,2.08745,0.0386455,2.96811,3.9002,12.5514,1.50999e-07,8.83681,0.614701,4.8374,2.94422,5.65934,3.79716,2.42393e-07,3.76043,11.6713,19.8563,2.29324,9.05517,2.73297,0.315229,9.68727,6.09771,7.70933,1.86227,3.1735,0.521857,0.00618037,6.86501,6.27954,14.0736,0.000924774,9.39426,0.769922,0.0245002,1.96274,4.7008,6.90015,8.01472e-05,6.67905,1.29383,1.29645,1.74596,4.9427,6.84285,0.333404,4.63594,5.06641,15.5398,2.11646,5.02996,4.77384,0.979819,4.49098,7.96129,4.60795,1.99647,1.46992,0.838725,0.406617,4.2825,5.30763,13.3822,0.679725,6.00381,1.31804,1.93113e-05,1.47814,7.09132,6.39708,0.0904769,3.79986,2.07594,4.28488e-05,1.09619,3.30311,5.84919,6.51683e-07,5.58052,1.29333,13.7187,1.89587,4.39399,2.13659,0.00336869,3.21854,1.88764,5.14016,0.896579,1.84339,1.04903,0.000184667,5.43554,3.35623,12.1924,0.385686,9.45011,1.85467,0.0246067,0.831138,6.914,6.23844,0.795699,1.95747,0.68692,0.845176,1.0877,3.0746,8.70655,0.00318328,4.42509,0.29063,5.10376,0.989006,2.7661,0.830882,0.000447012,4.70886,5.90386,5.171,0.329779,1.71858,2.65224,0.0874849,5.12659,6.53526,8.14225,0.289866,6.99529,1.06769,0.429264,3.2046,3.89446,3.86922,0.525132,0.974393,0.388117,0.0872195,1.61082,3.31041,4.96678,2.1021e-06,6.17585,0.433333,1.33485,2.73664,1.35853,1.71845,1.46236e-05,2.19909,8.86633,12.8545,0.482935,4.31387,1.73655,0.000135461,4.80087,3.72879,6.01963,0.697551,0.589483,1.58111,1.24803,0.380378,1.94606,2.44027,0.332729,0.58631,1.66398,1.51738,0.381653,1.24591,1.21926,0.089968,0.137374,1.49212,0.414554,1.49662,1.86038,0.586595,0.479933,0.0956203,2.13886,4.08323,0.97873,5.89219,3.0324,2.33401,0.915041,1.16065,0.654674,2.64473,1.67508,2.13872,2.07879,1.037,1.61363,3.01924,0.00194727,0.213232,0.990046,1.19904,0.647825,2.37541,2.722,0.868884,0.498483,1.77989,0.176762,0.312136,1.8974,0.0906598,0.87929,0.000377702,3.5025,2.83095,0.756712,3.14806,1.67057,1.04358,0.0455291,1.58032,0.451919,0.70461,0.853231,1.32766,0.306014,0.0767751,1.2256,3.54072,0.125499,1.10718,1.34228,0.00331049,1.22306,1.02556,1.12424,0.730394,0.000257952,2.43206,0.320216,0.00270821,1.38399,1.13758,0.64275,0.304758,0.425686,1.60879,2.12142,2.41066,1.52521,1.14838,0.618358,1.23101,0.211262,0.401648,4.50295,1.65174,0.887649,0.564754,0.714206,0.780962,1.40717,1.56299,1.00119,0.5446,0.309131,2.22433,0.346811,1.47716,0.125209,1.53101,0.985111,0.162529,1.16698,1.59133,0.423444,0.249217,1.22514,0.15732,1.65533,0.683983,0.313613,0.000591039,0.752796,2.10528,0.359425,0.798952,2.03783,2.00352,0.838242,2.56902,0.731644,0.999082,1.72261,1.6797,0.316591,1.43042,0.481146,1.16165,1.44683,1.09819,0.282121,1.14727,0.982391,1.25662,1.93189,1.45891,0.00163681,0.0745168,1.0052,0.106571,1.25017,0.732631,0.272068,0.521974,0.153365,1.46922,0.216946,0.993699,4.56478,1.23891,0.54856,1.1685,2.60148,0.901606,0.762153,0.673157,0.661107,0.747385,0.583753,1.78587,2.27985,0.537414,0.519801,1.3492,1.27548,0.854889,1.06354,2.40819,0.0574775,0.351584,0.94144,0.277534,0.487703,1.59348,0.273829,0.208106,0.0106441,2.37391,2.91178,2.06921,2.95586,1.41766,1.6503,0.569348,1.00752,0.000979348,0.962174,1.35048,0.720073,0.849725,0.281346,0.397568,2.12944,0.184289,0.210439,0.167841,0.123331,0.523199,1.53868,2.51282,0.220077,0.138104,0.349864,0.346058,0.0392949,1.60673,0.736784,0.365483,0.0697581,2.10601,1.64587,0.284739,2.53066,0.317422,0.39104,0.837833,1.14931,0.00133174,0.364254,1.47253,1.49312,0.970698,0.148329,0.710574,1.1011,0.103337,2.04584,0.840574,0.738854,1.38096,0.886936,2.68339,1.03406,0.561051,1.037,0.180332,0.0567668,0.405977,2.51247,2.46447,0.266659,1.18302,1.21353,1.54037,0.431412,0.585637,0.470308,0.822154,1.09625,1.08513,0.924982,0.730777,0.0290513,2.8241,1.0741,0.0278113,1.62353,1.72484,0.291436,0.0615084,0.0688936,0.109038,0.426259,0.676662,0.0296513,1.46585,0.614088,0.818202,0.623282,0.7245,2.95407,0.801136,0.383492,0.82544,1.04582,1.31102,1.3451,1.89279,0.466176,0.880792,0.567139,1.3591,1.19211,0.356127,0.446713,1.63157,1.74161,0.198557,0.573037,1.82283,0.181068,0.71155,0.183613,0.596359,0.56774,1.02815,0.475456,0.434795,0.303731,1.76786,0.147978,0.567042,3.59143,0.237016,1.07907,0.0650334,0.766743,0.613197,1.66673,0.436797,1.13999] loss_val = [15.9075,7.81171,7.9644,3.49974,3.15895,4.38955,4.15697,6.17876,3.4366,2.46914,3.41426,3.23049,5.30429,3.19208,2.0779,2.56253,3.2619,5.04907,3.0515,2.65809,2.08003,2.77029,3.90597,2.83559,2.96545,1.90817,2.23167,3.83688,3.1674,3.25176,2.02111,1.89907,3.05517,2.24733,3.50986,1.36536,1.36045,1.30455,1.27106,1.29183,1.22332,1.25968,1.20373,1.21101,1.2103,1.16807,1.19542,1.14616,1.15033,1.14135,1.09643,1.12605,1.08845,1.10505,1.09303,1.07197,1.10399,1.05564,1.09022,1.05491,1.03748,1.06291,1.02575,1.04434,1.01115,1.00698,1.02612,0.975953,1.02462,0.981691,1.01163,0.996219,0.988349,0.991318,0.985924,0.985863,0.986974,0.981544,0.98296,0.979165,0.980247,0.988261,0.988905,0.982036,1.04112,1.05818,0.986896,1.00126,1.02393,0.982019,1.03436,1.07273,0.982022,0.98988,0.994627,0.982016,0.99697,0.982016,1.00086,0.982427] accu_val = [0.509524,0.519048,0.62381,0.72619,0.769047,0.72619,0.709524,0.669048,0.692857,0.802381,0.778571,0.757143,0.704762,0.695238,0.821428,0.795238,0.740476,0.711905,0.7,0.795238,0.828571,0.769048,0.757143,0.723809,0.780953,0.82619,0.816667,0.761905,0.72381,0.773809,0.807143,0.838095,0.795238,0.802381,0.771429,0.864286,0.861905,0.869048,0.869048,0.866667,0.871429,0.871429,0.871429,0.871429,0.869048,0.871429,0.869048,0.869048,0.871429,0.871429,0.87381,0.871429,0.873809,0.873809,0.871429,0.87381,0.873809,0.871429,0.871429,0.871429,0.87381,0.87381,0.87381,0.87381,0.87381,0.87381,0.876191,0.873809,0.876191,0.876191,0.87381,0.87381,0.878572,0.878571,0.878572,0.878571,0.878571,0.87619,0.876191,0.876191,0.87381,0.87381,0.87381,0.878571,0.871429,0.87381,0.876191,0.87619,0.876191,0.878571,0.87619,0.87381,0.878571,0.876191,0.873809,0.878571,0.873809,0.878571,0.87619,0.878571] plotter = LossAccPlotter( title="Text/No-Text classifier loss and accuracy graph", save_to_filepath="./cnn_acc_loss.png", show_regressions=False, show_averages=True, show_loss_plot=True, show_acc_plot=True, show_plot_window=False, x_label="Iteration Count") # add them all for iteration in range(20000): if iteration%40 == 0: # deactivate redrawing after each update plotter.add_values(iteration, loss_train=loss_train[iteration/40], redraw=False) if iteration%200 == 0: # deactivate redrawing after each update plotter.add_values(iteration, loss_val=loss_val[iteration/200], acc_val = accu_val[iteration/200], redraw=False) # redraw once at the end plotter.redraw() plotter.block()
def main(): rand.seed() #env = gym.make('Asteroids-v0') env = gym.make('Breakout-v0') num_actions = env.action_space.n #print(num_actions) plotter = LossAccPlotter(title="mem256_perGame",show_acc_plot=True,save_to_filepath="/mem256_perGame.png",show_loss_plot=False) plotter2 = LossAccPlotter(title="mem256_100",show_acc_plot=True,save_to_filepath="/mem256_100.png",show_loss_plot=False) #plotter.save_plot("./mem256.png") observation = env.reset() #observation = downsample(observation) #reward = 0 action = 0 total_reward = 0 total_reward2 = 0 env.render() prev_obs = [] curr_obs = [] D = [] step = 0 rate = 1 sess, output_net, x, cost, trainer, mask, reward, nextQ = initialize() #load(sess) startPrinting = False for i in range(5): observation, rw, done, info = env.step(action) # pass in 0 for action observation = convert_to_small_and_grayscale(observation) prev_obs = deepcopy(curr_obs) curr_obs = obsUpdate(curr_obs,observation) #e = [rw, action, deepcopy(prev_obs), deepcopy(curr_obs)] #D.append(e) action = 1 #print(i) print("Entering mini-loop") for _ in range(10): step +=1 #print(step) if done: observation = env.reset() if (len(D) > 256): D.pop() if step % 1000 == 0: rate = rate / 2 if (rate < 0.05): rate=0.05 #if step % 1000 == 0: #save(sess) action = magic(curr_obs, sess, output_net, x,step,rate, False) #change this to just take in curr_obs, sess, and False #action = env.action_space.sample() env.render() observation, rw, done, info = env.step(action) # take a random action #print(action, rw, step) observation = convert_to_small_and_grayscale(observation) e = [rw, action, deepcopy(prev_obs), deepcopy(curr_obs)] D.append(e) prev_obs = deepcopy(curr_obs) curr_obs = obsUpdate(curr_obs,observation) print("Entering full loop") while step < 10001: step +=1 #print(step) if done: print("saving to plot....") plot_reward = total_reward plotter.add_values(step, acc_train=plot_reward) total_reward = 0 observation = env.reset() if (len(D) > 256): D.pop() if step % 100 == 0: print(step,"steps have passed") save(sess, step) rate = rate / 2 startPrinting = True if (rate < 0.05): rate=0.05 print("saving to plot2....") plot_reward = total_reward2/100 plotter2.add_values(step, acc_train=plot_reward) total_reward2 = 0 #print(step,"steps have passed") if step % 500 == 0: plotter.save_plot("./mem256_perGame.png") plotter2.save_plot("./mem256_100.png") action = magic(curr_obs, sess, output_net, x,step,rate, startPrinting) #change this to just take in curr_obs, sess, and False #action = env.action_space.sample() env.render() observation, rw, done, info = env.step(action) # take a random action #print(action, rw, step) observation = convert_to_small_and_grayscale(observation) e = [rw, action, deepcopy(prev_obs), deepcopy(curr_obs)] D.insert(0,e) prev_obs = deepcopy(curr_obs) curr_obs = obsUpdate(curr_obs,observation) update_q_function(D, sess, output_net, x, cost, trainer, mask, reward, nextQ) total_reward = total_reward + rw total_reward2 = total_reward2 + rw plotter.block() plotter2.block()
def train_model(num_tasks, models, dataloaders, dataset_sizes, criterion, optimizers, schedulers, epochs=15): since = time.time() num_tasks = num_tasks use_gpu = torch.cuda.is_available() final_outputs = [ ] #this is the variable to accumulate the outputs of all columns for each task middle_outputs = [ ] #this is the variable for keeping outputs of current task in each column #we iterate for each task for task_id in range(num_tasks): #everytime we do a new task, we empty the final outputs final_outputs[:] = [] # we now iterate for each previous column until the one of our task for i in range(0, task_id + 1): #we save the weights with best results best_model_wts = copy.deepcopy(models[task_id][i].state_dict()) model = models[task_id][i] optimizer = optimizers[i] scheduler = schedulers[i] # if it's not the column corresponding to the task, do not train if task_id != i: #this is the case for "previous" columns so we only need to pass data, not train num_epochs = 1 middle_outputs[:] = [] else: num_epochs = epochs dataloader = dataloaders[i] dataset_size = dataset_sizes[i] best_acc = 0.0 # let's add a plotter for loss and accuracy plotter = LossAccPlotter() for epoch in range(num_epochs): print('Epoch {}/{}'.format(epoch, num_epochs - 1)) print('-' * 10) # Each epoch has a training and validation phase for phase in ['train', 'val']: if phase == 'train': scheduler.step() model.train(True) # Set model to training mode else: model.train(False) # Set model to evaluate mode running_loss = 0.0 running_corrects = 0 # Iterate over data. #tqdm shows a progression bar for data in tqdm(dataloader[phase]): # get the inputs inputs, labels = data if inputs.type() != int and labels.type() != int: # wrap them in Variable if use_gpu: inputs = Variable(inputs.cuda()) labels = Variable(labels.cuda()) else: inputs, labels = Variable(inputs), Variable( labels) # zero the parameter gradients optimizer.zero_grad() # forward outputs, middle_outputs = model( inputs, final_outputs) #we save the outputs of this column in middle outputs and we have previous columns in final _, preds = torch.max(outputs.data, 1) loss = criterion(outputs, labels) # backward + optimize only if in training phase if phase == 'train': loss.backward() optimizer.step() # statistics running_loss += loss.data[0] * inputs.size(0) running_corrects += torch.sum(preds == labels.data) epoch_loss = running_loss / dataset_size[phase] epoch_acc = running_corrects / dataset_size[phase] print('{} Loss: {:.4f} Acc: {:.4f}'.format( phase, epoch_loss, epoch_acc)) if phase == 'train': plotter.add_values(epoch, loss_train=epoch_loss, acc_train=epoch_acc, redraw=False) else: plotter.add_values(epoch, loss_val=epoch_loss, acc_val=epoch_acc, redraw=False) # deep copy the model if phase == 'val' and epoch_acc > best_acc: best_acc = epoch_acc best_model_wts = copy.deepcopy(model.state_dict()) print() plotter.redraw() if task_id != i: final_outputs.append(middle_outputs) #we add the ouput of this column to final outputs plotter.save_plot('plots%d.%d.png' % (task_id, i)) print('Best val Acc: {:4f}'.format(best_acc)) # load best model weights models[task_id][i].load_state_dict(best_model_wts) time_elapsed = time.time() - since print('Training complete in {:.0f}m {:.0f}s'.format( time_elapsed // 60, time_elapsed % 60)) return models
for i in range(len(t)): one_hot_t.append(t[i][0]["one_hot_label"]) #one_hot_t.append(one_hot_t_special) one_hot_t = np.array(one_hot_t, dtype=np.float32) one_hot_t = Variable(one_hot_t) one_hot_t.to_gpu(0) y, loss, accuracy = model(x, one_hot_t) print("y result", y) now = time.time() - start print( "[batch %d (%d images)] learning rate: %f, loss: %f, accuracy: %f, time: %f" % (batch + 1, (batch + 1) * batch_size, optimizer.lr, loss.data, accuracy.data, now)) plotter.add_values(batch, loss_train=loss.data, acc_train=accuracy.data) model.cleargrads() #model.zerograds() loss.backward() optimizer.lr = learning_rate * ( 1 - batch / max_batches)**lr_decay_power # Polynomial decay learning rate optimizer.update() # save model if (batch) % 1000 == 0: model_file = "%s/%s.model" % (backup_path, batch + 1) print("saving model to %s" % (model_file)) serializers.save_hdf5(model_file, model) serializers.save_hdf5(backup_file, model)
def train(ngram, name, bar, drop_out, dataset, is_cuda=False, edges=False): plotter = LossAccPlotter(title="This is an example plot", save_to_filepath="/tmp/my_plot.png", show_regressions=True, show_averages=True, show_loss_plot=True, show_acc_plot=True, show_plot_window=False, x_label="Epoch") print('load data helper.') data_helper = DataHelper(mode='train') b_size = len(data_helper.label) print('*' * 100) print('train set total:', b_size) if os.path.exists(os.path.join('.', name + '.pkl')) and name != 'temp_model': print('load model from file') model = torch.load(os.path.join('.', name + '.pkl')) else: print('new model') if name == 'temp_model': name == 'temp_model' edges_weights, edges_mappings, count = cal_PMI() ## -----------------************************** import the datahelper class to get the vocab-5 doc*****************------------------------ ## class_num = len(data_helper.labels_str) is changed, consider just a score model = Model(class_num=data_helper.labels_str, hidden_size_node=200, vocab=data_helper.vocab, n_gram=ngram, drop_out=drop_out, edges_matrix=edges_mappings, edges_num=count, trainable_edges=edges, pmi=edges_weights, cuda=is_cuda) ### --------------------------------------- ### print(model) if is_cuda: print('cuda') model.cuda() loss_func = torch.nn.MSELoss() loss_mae = torch.nn.L1Loss(reduction='sum') optim = torch.optim.Adam(model.parameters(), weight_decay=1e-3) iter = 0 if bar: pbar = tqdm.tqdm(total=NUM_ITER_EVAL) best_acc = 0.0 last_best_epoch = 0 start_time = time.time() total_loss = 0.0 total_correct = 0 total = 0 accuracy = 0.0 num_epoch = 500 weight_decays = 1e-4 for content, label, epoch in data_helper.batch_iter( batch_size=32, num_epoch=num_epoch): improved = '' model.train() pred = model(content) pred_sq = torch.squeeze(pred, 1) l2_reg = 0.5 * weight_decays * ( model.seq_edge_w.weight.to('cpu').detach().numpy()**2).sum() loss = loss_func(pred_sq, label.float()) + l2_reg #-------------------------------------------# error = loss_mae(pred_sq.cpu().data, label.cpu()) accuracy += error total += len(pred) ##batch size = len(label) total_loss += (loss.item() * len(pred)) total_correct += loss.item() optim.zero_grad() loss.backward() optim.step() iter += 1 if bar: pbar.update() if iter % NUM_ITER_EVAL == 0: if bar: pbar.close() val_acc, val_loss = dev(model) if val_acc < best_acc: best_acc = val_acc last_best_epoch = epoch improved = '* ' torch.save(model, name + '.pkl') msg = 'Epoch: {0:>6} Iter: {1:>6}, Train Loss: {5:>7.2}, Train Error: {6:>7.2}' \ + 'Val Acc: {2:>7.2}, Time: {3}{4}, val error:{7:>7.2}' \ # + ' Time: {5} {6}' print( msg.format(epoch, iter, val_acc, get_time_dif(start_time), improved, total_correct / (NUM_ITER_EVAL), float(accuracy) / float(total), val_loss)) plotter.add_values(epoch, loss_train=total_correct / (NUM_ITER_EVAL), acc_train=float(accuracy) / float(total), loss_val=val_loss, acc_val=best_acc) total_loss = 0.0 total_correct = 0 accuracy = 0.0 total = 0 if bar: pbar = tqdm.tqdm(total=NUM_ITER_EVAL) plotter.block() return name
train_acc.append(None) val_acc_top1.append(None) val_acc_top5.append(None) count_acc += 1 print("Start plotting") # print(len(loss)) # print(train_acc) # print(val_acc_top1) # print(val_acc_top5) plot_title = 'Loss and Accuracy Plotted Using ' + num_classes + ' Keywords (' + num_train_per_class + ' training videos each) and minibatch size of ' + batch_size plotter = LossAccPlotter(title=plot_title, save_to_filepath=num_classes + '_' + num_train_per_class + '_' + batch_size + '.png', show_regressions=False, show_averages=False, show_loss_plot=True, show_acc_plot=True, show_plot_window=True, x_label="Epoch") for epoch in range(len(loss)): plotter.add_values(epoch, loss_train=loss[epoch], acc_train=train_acc[epoch], acc_val=val_acc_top1[epoch]) plotter.block() print("Finished plotting")
if option.is_gd(): print("\tOptimization: Gradient Descent") elif option.is_sgd(): print("\tOptimization: Stochastic Gradient Descent") if option.is_linear(): print("\tActivation Function: Linear") elif option.is_sigmoid(): print("\tActivation Function: Sigmoid") if option.is_l2norm(): print("\tRegularization: L2Norm") elif option.is_dropout(): print("\tRegularization: Drop out") net = Network(training, test, option) if read_weights_from_file == "y": loaded = np.load('weights.npz') net.set_hid_weights(loaded['hid_weights']).set_out_weights( loaded['out_weights']).set_hid_bias( loaded['hid_bias']).set_out_bias(loaded['out_bias']) plotter = LossAccPlotter(show_acc_plot=False) for i in range(NUM_OF_ITER): training_loss = 0 if read_weights_from_file != "y": training_loss = net.train(i == NUM_OF_ITER - 1) test_loss = net.test(i == NUM_OF_ITER - 1) plotter.add_values(i, loss_train=training_loss, loss_val=test_loss) plotter.block()