Update ConfusionMatrix.png, inceptionv3.ipynb, acc_train.txt, acc_valid.txt,...

Update ConfusionMatrix.png, inceptionv3.ipynb, acc_train.txt, acc_valid.txt, helper_evaluation.py, helper_plotting.py, loss_train.txt, loss_valid.txt, stats.txt

Update ConfusionMatrix.png, inceptionv3.ipynb, acc_train.txt, acc_valid.txt,...
f117210d · Lanka Naga Sai Deep · 78c48c04 · f117210d · f117210d · f117210d
Commit f117210d authored 3 years ago by Lanka Naga Sai Deep
--- a/ConfusionMatrix.png
+++ b/ConfusionMatrix.png
--- a/acc_train.txt
+++ b/acc_train.txt
+60.59137725830078,71.66165161132812,76.8459701538086,81.20057678222656,84.1463394165039,85.36028289794922,87.16448974609375,88.48980712890625,89.44759368896484,90.41095733642578,91.26294708251953,91.27407836914062,91.45783996582031,92.3432388305664,92.52700805664062,93.01703643798828,93.25091552734375,93.71310424804688,93.8578872680664,93.65184783935547,94.06949615478516,94.53168487548828,94.26995849609375,94.40917205810547,94.62635040283203,94.77669525146484,94.9883041381836,95.22775268554688,95.1776351928711,95.48390197753906,95.51174926757812,95.52288055419922,95.62311553955078,95.84585571289062,95.82915496826172,95.84585571289062,95.95166015625,96.10201263427734,96.24679565429688,96.35816955566406,96.18553924560547,96.21895599365234,96.28577423095703,96.62545776367188,96.50294494628906,96.7646713256836,96.80364990234375,96.90388488769531,96.90388488769531,96.93729400634766,96.53636169433594,96.73126220703125,96.88160705566406,96.7646713256836,96.63102722167969,96.74796295166016,96.88160705566406,97.0263900756836,97.13219451904297,96.97627258300781,97.06536865234375,97.11548614501953,97.34379577636719,97.13776397705078,97.43846130371094,97.26026916503906,97.42733001708984,97.44403076171875,97.51085662841797,97.6556396484375,97.42176055908203,97.65007019042969,97.60552215576172,97.61109161376953,97.50528717041016,97.7837142944336,97.74473571777344,97.80599212646484,97.80042266845703,97.7892837524414,97.96190643310547,97.93963623046875,98.01202392578125,98.1623764038086,98.1623764038086,97.90621948242188,98.28488159179688,98.29045104980469,97.94520568847656,98.28488159179688,98.26818084716797,98.46864318847656,98.37954711914062,98.39625549316406,98.31272888183594,98.2960205078125,98.40739440917969,98.53546905517578,98.32386016845703,98.53546905517578,
\ No newline at end of file
--- a/acc_valid.txt
+++ b/acc_valid.txt
+72.81468963623047,74.82518005371094,54.16666793823242,53.263404846191406,42.16200637817383,88.5489501953125,57.7214469909668,30.09906768798828,86.85897827148438,84.73193359375,88.92774200439453,89.7727279663086,89.51049041748047,69.87179565429688,88.78205108642578,86.21794891357422,91.20046997070312,91.52098083496094,88.92774200439453,92.97785949707031,61.188812255859375,89.07342529296875,89.13170623779297,93.0361328125,82.22611236572266,91.89977264404297,82.10955810546875,92.22028350830078,93.85198211669922,92.22028350830078,94.2599105834961,93.26923370361328,91.05477905273438,93.06526947021484,92.62820434570312,94.11421966552734,93.4149169921875,93.9102554321289,93.61888122558594,94.34732055664062,94.49301147460938,92.5407943725586,93.4149169921875,94.2599105834961,94.46387481689453,94.28904724121094,94.93006896972656,94.05594635009766,94.2016372680664,94.72611236572266,94.23077392578125,94.37645721435547,93.79370880126953,94.43473052978516,95.10489654541016,93.96853637695312,94.90093231201172,94.43473052978516,95.10489654541016,93.47319793701172,94.23077392578125,94.60955810546875,93.18182373046875,94.28904724121094,95.22145080566406,94.46387481689453,93.50233459472656,93.706298828125,94.55128479003906,94.34732055664062,94.31818389892578,94.72611236572266,93.24009704589844,94.78438568115234,94.69696807861328,94.84265899658203,94.81352233886719,92.89044189453125,94.81352233886719,94.66783142089844,94.55128479003906,94.6386947631836,94.81352233886719,94.55128479003906,94.28904724121094,95.22145080566406,94.93006896972656,94.98834991455078,94.5804214477539,95.01748657226562,94.37645721435547,95.16316986083984,95.134033203125,94.93006896972656,94.6386947631836,93.76457214355469,94.49301147460938,95.04662322998047,94.52214813232422,94.60955810546875,
\ No newline at end of file
--- a/helper_evaluation.py
+++ b/helper_evaluation.py
+# imports from installed libraries
+import os
+import numpy as np
+import random
+import torch
+from distutils.version import LooseVersion as Version
+from itertools import product
+from tqdm import tqdm
+
+def set_all_seeds(seed):
+    os.environ["PL_GLOBAL_SEED"] = str(seed)
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+
+def set_deterministic():
+    if torch.cuda.is_available():
+        torch.backends.cudnn.benchmark = False
+        torch.backends.cudnn.deterministic = True
+
+    if torch.__version__ <= Version("1.7"):
+        torch.set_deterministic(True)
+    else:
+        torch.use_deterministic_algorithms(True)
+
+
+def compute_accuracy(model, data_loader, device):
+
+    with torch.no_grad():
+
+        correct_pred, num_examples = 0, 0
+
+        for i, (features, targets) in enumerate(tqdm(data_loader)):
+
+            features = features.to(device)
+            targets = targets.float().to(device)
+
+            logits = model(features)
+            _, predicted_labels = torch.max(logits, 1)
+
+            num_examples += targets.size(0)
+            correct_pred += (predicted_labels == targets).sum()
+    return correct_pred.float()/num_examples * 100
+
+
+def compute_confusion_matrix(model, data_loader, device):
+
+    all_targets, all_predictions = [], []
+    with torch.no_grad():
+
+        for i, (features, targets) in enumerate(data_loader):
+
+            features = features.to(device)
+            targets = targets
+            logits = model(features)
+            _, predicted_labels = torch.max(logits, 1)
+            all_targets.extend(targets.to('cpu'))
+            all_predictions.extend(predicted_labels.to('cpu'))
+
+    all_predictions = all_predictions
+    all_predictions = np.array(all_predictions)
+    all_targets = np.array(all_targets)
+        
+    class_labels = np.unique(np.concatenate((all_targets, all_predictions)))
+    if class_labels.shape[0] == 1:
+        if class_labels[0] != 0:
+            class_labels = np.array([0, class_labels[0]])
+        else:
+            class_labels = np.array([class_labels[0], 1])
+    n_labels = class_labels.shape[0]
+    lst = []
+    z = list(zip(all_targets, all_predictions))
+    for combi in product(class_labels, repeat=2):
+        lst.append(z.count(combi))
+    mat = np.asarray(lst)[:, None].reshape(n_labels, n_labels)
+    return mat
\ No newline at end of file
--- a/helper_plotting.py
+++ b/helper_plotting.py
+# imports from installed libraries
+import os
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+
+def plot_training_loss(minibatch_loss_list, num_epochs, iter_per_epoch,
+                       results_dir=None, averaging_iterations=100):
+
+    plt.figure()
+    ax1 = plt.subplot(1, 1, 1)
+    ax1.plot(range(len(minibatch_loss_list)),
+             (minibatch_loss_list), label='Minibatch Loss')
+
+    if len(minibatch_loss_list) > 1000:
+        ax1.set_ylim([
+            0, np.max(minibatch_loss_list[1000:])*1.5
+            ])
+    ax1.set_xlabel('Iterations')
+    ax1.set_ylabel('Loss')
+
+    ax1.plot(np.convolve(minibatch_loss_list,
+                         np.ones(averaging_iterations,)/averaging_iterations,
+                         mode='valid'),
+             label='Running Average')
+    ax1.legend()
+
+    ###################
+    # Set scond x-axis
+    ax2 = ax1.twiny()
+    newlabel = list(range(num_epochs+1))
+
+    newpos = [e*iter_per_epoch for e in newlabel]
+
+    ax2.set_xticks(newpos[::10])
+    ax2.set_xticklabels(newlabel[::10])
+
+    ax2.xaxis.set_ticks_position('bottom')
+    ax2.xaxis.set_label_position('bottom')
+    ax2.spines['bottom'].set_position(('outward', 45))
+    ax2.set_xlabel('Epochs')
+    ax2.set_xlim(ax1.get_xlim())
+    ###################
+
+    plt.tight_layout()
+
+    if results_dir is not None:
+        image_path = os.path.join(results_dir, 'plot_training_loss.pdf')
+        plt.savefig(image_path)
+
+
+def plot_accuracy(train_acc_list, valid_acc_list, results_dir):
+
+    num_epochs = len(train_acc_list)
+
+    plt.plot(np.arange(1, num_epochs+1),
+             train_acc_list, label='Training')
+    plt.plot(np.arange(1, num_epochs+1),
+             valid_acc_list, label='Validation')
+
+    plt.xlabel('Epoch')
+    plt.ylabel('Accuracy')
+    plt.legend()
+
+    plt.tight_layout()
+
+    if results_dir is not None:
+        image_path = os.path.join(
+            results_dir, 'plot_acc_training_validation.pdf')
+        plt.savefig(image_path)
+
+
+def show_examples(model, data_loader, unnormalizer=None, class_dict=None):
+    
+        
+    for batch_idx, (features, targets) in enumerate(data_loader):
+
+        with torch.no_grad():
+            features = features
+            targets = targets
+            logits = model(features)
+            predictions = torch.argmax(logits, dim=1)
+        break
+
+    fig, axes = plt.subplots(nrows=3, ncols=5,
+                             sharex=True, sharey=True)
+    
+    if unnormalizer is not None:
+        for idx in range(features.shape[0]):
+            features[idx] = unnormalizer(features[idx])
+    nhwc_img = np.transpose(features, axes=(0, 2, 3, 1))
+    
+    if nhwc_img.shape[-1] == 1:
+        nhw_img = np.squeeze(nhwc_img.numpy(), axis=3)
+
+        for idx, ax in enumerate(axes.ravel()):
+            ax.imshow(nhw_img[idx], cmap='binary')
+            if class_dict is not None:
+                ax.title.set_text(f'P: {class_dict[predictions[idx].item()]}'
+                                  f'\nT: {class_dict[targets[idx].item()]}')
+            else:
+                ax.title.set_text(f'P: {predictions[idx]} | T: {targets[idx]}')
+            ax.axison = False
+
+    else:
+
+        for idx, ax in enumerate(axes.ravel()):
+            ax.imshow(nhwc_img[idx])
+            if class_dict is not None:
+                ax.title.set_text(f'P: {class_dict[predictions[idx].item()]}'
+                                  f'\nT: {class_dict[targets[idx].item()]}')
+            else:
+                ax.title.set_text(f'P: {predictions[idx]} | T: {targets[idx]}')
+            ax.axison = False
+    plt.tight_layout()
+    plt.show()
+
+
+def plot_confusion_matrix(conf_mat,
+                          hide_spines=False,
+                          hide_ticks=False,
+                          figsize=None,
+                          cmap=None,
+                          colorbar=False,
+                          show_absolute=True,
+                          show_normed=False,
+                          class_names=None):
+
+    if not (show_absolute or show_normed):
+        raise AssertionError('Both show_absolute and show_normed are False')
+    if class_names is not None and len(class_names) != len(conf_mat):
+        raise AssertionError('len(class_names) should be equal to number of'
+                             'classes in the dataset')
+
+    total_samples = conf_mat.sum(axis=1)[:, np.newaxis]
+    normed_conf_mat = conf_mat.astype('float') / total_samples
+
+    fig, ax = plt.subplots(figsize=figsize)
+    ax.grid(False)
+    if cmap is None:
+        cmap = plt.cm.Blues
+
+    if figsize is None:
+        figsize = (len(conf_mat)*1.25, len(conf_mat)*1.25)
+
+    if show_normed:
+        matshow = ax.matshow(normed_conf_mat, cmap=cmap)
+    else:
+        matshow = ax.matshow(conf_mat, cmap=cmap)
+
+    if colorbar:
+        fig.colorbar(matshow)
+
+    for i in range(conf_mat.shape[0]):
+        for j in range(conf_mat.shape[1]):
+            cell_text = ""
+            if show_absolute:
+                cell_text += format(conf_mat[i, j], 'd')
+                if show_normed:
+                    cell_text += "\n" + '('
+                    cell_text += format(normed_conf_mat[i, j], '.2f') + ')'
+            else:
+                cell_text += format(normed_conf_mat[i, j], '.2f')
+            ax.text(x=j,
+                    y=i,
+                    s=cell_text,
+                    va='center',
+                    ha='center',
+                    color="white" if normed_conf_mat[i, j] > 0.5 else "black")
+    
+    if class_names is not None:
+        tick_marks = np.arange(len(class_names))
+        plt.xticks(tick_marks, class_names, rotation=90)
+        plt.yticks(tick_marks, class_names)
+        
+    if hide_spines:
+        ax.spines['right'].set_visible(False)
+        ax.spines['top'].set_visible(False)
+        ax.spines['left'].set_visible(False)
+        ax.spines['bottom'].set_visible(False)
+    ax.yaxis.set_ticks_position('left')
+    ax.xaxis.set_ticks_position('bottom')
+    if hide_ticks:
+        ax.axes.get_yaxis().set_ticks([])
+        ax.axes.get_xaxis().set_ticks([])
+
+    plt.xlabel('predicted label')
+    plt.ylabel('true label')
+    return fig, ax
\ No newline at end of file
--- a/inceptionv3.ipynb
+++ b/inceptionv3.ipynb
--- a/loss_train.txt
+++ b/loss_train.txt
+1.1035438804806341,0.7226075545878446,0.5927857020558719,0.4855417087383751,0.42466082714937775,0.3899900257610249,0.33965107956475976,0.3088414678495286,0.2893916949011927,0.26239258625865075,0.24367613917504974,0.236287816383571,0.23417071717115065,0.21140513792742907,0.20538419038578473,0.19501896397021903,0.18287320016237196,0.17879659134323164,0.171295881071395,0.17367128944810054,0.16889888298814962,0.15593305252522763,0.16150809311312375,0.14889712126819024,0.14728439946149172,0.14183516678818942,0.14214308556186903,0.13260898183909905,0.13150178402533158,0.12626396362086523,0.1243195203532862,0.12209497421210111,0.12278669627672056,0.11644445447141527,0.11643929763509502,0.11314545146687183,0.11227715401390395,0.1071929339383833,0.10536505841217854,0.1025733296694737,0.1052557724369013,0.10263119807659637,0.10232017596648244,0.09797717922651777,0.0962465678795243,0.09185824797468396,0.08947356818272058,0.09256357114334521,0.08656281119721079,0.08749571777711337,0.09165445734263657,0.09221583802063996,0.08847045124473309,0.08789863544174668,0.09500338172477096,0.09031265778825509,0.08620208682385942,0.08300438247726585,0.08226122751776815,0.08579505991390575,0.08214815120575526,0.07662939100897298,0.07747971859184274,0.07793970416078122,0.07176851012418552,0.07633045756118612,0.07321558582663341,0.07049886439466706,0.06958261082941226,0.06615839365227791,0.07073798872735443,0.06759932948314919,0.06808185780105781,0.0662040773194995,0.06631172326392323,0.060300969062300605,0.0631895532683778,0.06198645626303477,0.06146746539060868,0.061250413641016646,0.055994985836952726,0.05786731395727391,0.05508038126183864,0.05333144986953854,0.05195518812915562,0.05557021407121344,0.04761978305685298,0.04825140143763275,0.05780107012915506,0.0488213468202301,0.04786794898898365,0.04460614188660565,0.04528368343457531,0.04315999560623736,0.045449965668429,0.04930824305741191,0.04283826560632438,0.04108247859154013,0.04423610125145301,0.04032945076981576,
\ No newline at end of file
--- a/loss_valid.txt
+++ b/loss_valid.txt
+0.7362933201909512,0.6163856863236329,1.3273141826035384,1.5671682171401458,3.5975048998270522,0.3208140740501673,1.60716543938882,3.7727238974062614,0.3727143925575459,0.39486539538513027,0.30012787602620505,0.2645583272986157,0.28670598589140556,0.877864682356586,0.31970177022714447,0.3537218891526128,0.23517900836004693,0.23142625330760383,0.29114914753153515,0.19204040009411208,1.383013668015098,0.29187787832020173,0.30029773062818627,0.20152319045968844,0.5768455127529731,0.23056407164745374,0.5357079009287545,0.2149459716328706,0.1735658104628493,0.22748694864047736,0.15573396021900965,0.19751328618058758,0.25568632156248966,0.19130310058533873,0.21698788530739277,0.17937653658909178,0.1841336501966603,0.17111435995329394,0.17860585031031706,0.16341808670263447,0.1674421660797369,0.20584173513718435,0.19133936254487285,0.17769178415356504,0.15445901458264635,0.17249739720020704,0.16489796243608087,0.18674146796817262,0.18428602356302096,0.15860075746543972,0.17152209940613636,0.16037222628516629,0.2036763697891736,0.1687593689412151,0.14177400080895142,0.18278716804204703,0.14799000272867793,0.1600002346522127,0.14605365284565963,0.18423723773755435,0.17847097962048566,0.16315428260541065,0.22504110104722083,0.1685476378482461,0.1624539961414715,0.16267618834170236,0.21929416910379954,0.20767059386729905,0.20314688874566306,0.21894106984455217,0.1854989590078617,0.16438417145409634,0.20492887748025262,0.15864856163937738,0.17424752525398982,0.1800764260328554,0.17779819888212697,0.22541044369261604,0.16276436379049866,0.185863304678096,0.19503489832381585,0.1772332505598556,0.17212355645936103,0.18769453135639058,0.2188312843085706,0.16567276273672485,0.17063623352618648,0.20191060677620382,0.174123426197158,0.1821983714126591,0.21106482365035026,0.18981046517962916,0.19061526675608978,0.18479735974127254,0.21741512184906572,0.22110039089647493,0.20986085644259478,0.2100832273115594,0.20389257205650918,0.19798582192817635,
\ No newline at end of file
--- a/stats.txt
+++ b/stats.txt
+Epoch 1 	Training Loss: 1.1035438804806341 	Validation Loss: 0.7362933201909512 	 time: 00:28:19
+Train Accuracy : 60.59137725830078 	Validation Accuracy : 72.81468963623047
+Validation Loss Decreased( inf ---> 842.3195582984481 ) 	 Saving The Model
+
+100%|██████████| 1796/1796 [23:55<00:00,  1.25it/s]
+100%|██████████| 1144/1144 [01:36<00:00, 11.89it/s]
+
+Epoch 2 	Training Loss: 0.7226075545878446 	Validation Loss: 0.6163856863236329 	 time: 00:25:31
+Train Accuracy : 71.66165161132812 	Validation Accuracy : 74.82518005371094
+Validation Loss Decreased( 842.3195582984481 ---> 705.145225154236 ) 	 Saving The Model
+
+100%|██████████| 1796/1796 [21:41<00:00,  1.38it/s]
+100%|██████████| 1144/1144 [01:40<00:00, 11.41it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
+
+Epoch 3 	Training Loss: 0.5927857020558719 	Validation Loss: 1.3273141826035384 	 time: 00:23:21
+Train Accuracy : 76.8459701538086 	Validation Accuracy : 54.16666793823242
+
+100%|██████████| 1796/1796 [22:25<00:00,  1.33it/s]
+100%|██████████| 1144/1144 [01:44<00:00, 10.94it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
+
+Epoch 4 	Training Loss: 0.4855417087383751 	Validation Loss: 1.5671682171401458 	 time: 00:24:10
+Train Accuracy : 81.20057678222656 	Validation Accuracy : 53.263404846191406
+
+100%|██████████| 1796/1796 [28:16<00:00,  1.06it/s]
+100%|██████████| 1144/1144 [02:02<00:00,  9.31it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 5 	Training Loss: 0.42466082714937775 	Validation Loss: 3.5975048998270522 	 time: 00:30:19
+Train Accuracy : 84.1463394165039 	Validation Accuracy : 42.16200637817383
+
+100%|██████████| 1796/1796 [27:13<00:00,  1.10it/s]
+100%|██████████| 1144/1144 [01:59<00:00,  9.55it/s]
+
+Epoch 6 	Training Loss: 0.3899900257610249 	Validation Loss: 0.3208140740501673 	 time: 00:29:13
+Train Accuracy : 85.36028289794922 	Validation Accuracy : 88.5489501953125
+Validation Loss Decreased( 705.145225154236 ---> 367.0113007133914 ) 	 Saving The Model
+
+100%|██████████| 1796/1796 [28:05<00:00,  1.07it/s]
+100%|██████████| 1144/1144 [02:03<00:00,  9.25it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 7 	Training Loss: 0.33965107956475976 	Validation Loss: 1.60716543938882 	 time: 00:30:09
+Train Accuracy : 87.16448974609375 	Validation Accuracy : 57.7214469909668
+
+100%|██████████| 1796/1796 [28:21<00:00,  1.06it/s]
+100%|██████████| 1144/1144 [02:05<00:00,  9.15it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 8 	Training Loss: 0.3088414678495286 	Validation Loss: 3.7727238974062614 	 time: 00:30:26
+Train Accuracy : 88.48980712890625 	Validation Accuracy : 30.09906768798828
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+100%|██████████| 1796/1796 [29:04<00:00,  1.03it/s]
+100%|██████████| 1144/1144 [02:08<00:00,  8.92it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 9 	Training Loss: 0.2893916949011927 	Validation Loss: 0.3727143925575459 	 time: 00:31:12
+Train Accuracy : 89.44759368896484 	Validation Accuracy : 86.85897827148438
+
+100%|██████████| 1796/1796 [28:35<00:00,  1.05it/s]
+100%|██████████| 1144/1144 [02:06<00:00,  9.05it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 10 	Training Loss: 0.26239258625865075 	Validation Loss: 0.39486539538513027 	 time: 00:30:42
+Train Accuracy : 90.41095733642578 	Validation Accuracy : 84.73193359375
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+100%|██████████| 1796/1796 [27:55<00:00,  1.07it/s]
+100%|██████████| 1144/1144 [01:46<00:00, 10.72it/s]
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+Train Accuracy : 91.26294708251953 	Validation Accuracy : 88.92774200439453
+Validation Loss Decreased( 367.0113007133914 ---> 343.34629017397856 ) 	 Saving The Model
+
+100%|██████████| 1796/1796 [22:31<00:00,  1.33it/s]
+100%|██████████| 1144/1144 [01:41<00:00, 11.28it/s]
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+Train Accuracy : 91.27407836914062 	Validation Accuracy : 89.7727279663086
+Validation Loss Decreased( 343.34629017397856 ---> 302.6547264296164 ) 	 Saving The Model
+
+100%|██████████| 1796/1796 [23:48<00:00,  1.26it/s]
+100%|██████████| 1144/1144 [02:04<00:00,  9.19it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 13 	Training Loss: 0.23417071717115065 	Validation Loss: 0.28670598589140556 	 time: 00:25:53
+Train Accuracy : 91.45783996582031 	Validation Accuracy : 89.51049041748047
+
+100%|██████████| 1796/1796 [31:12<00:00,  1.04s/it]
+100%|██████████| 1144/1144 [02:15<00:00,  8.45it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Train Accuracy : 92.3432388305664 	Validation Accuracy : 69.87179565429688
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+100%|██████████| 1796/1796 [33:27<00:00,  1.12s/it]
+100%|██████████| 1144/1144 [02:01<00:00,  9.45it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Train Accuracy : 92.52700805664062 	Validation Accuracy : 88.78205108642578
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+100%|██████████| 1796/1796 [25:01<00:00,  1.20it/s]
+100%|██████████| 1144/1144 [01:51<00:00, 10.29it/s]
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+Epoch 16 	Training Loss: 0.19501896397021903 	Validation Loss: 0.3537218891526128 	 time: 00:26:52
+Train Accuracy : 93.01703643798828 	Validation Accuracy : 86.21794891357422
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+100%|██████████| 1796/1796 [25:14<00:00,  1.19it/s]
+100%|██████████| 1144/1144 [01:59<00:00,  9.54it/s]
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+Epoch 17 	Training Loss: 0.18287320016237196 	Validation Loss: 0.23517900836004693 	 time: 00:27:14
+Train Accuracy : 93.25091552734375 	Validation Accuracy : 91.20046997070312
+Validation Loss Decreased( 302.6547264296164 ---> 269.0447855638937 ) 	 Saving The Model
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+100%|██████████| 1796/1796 [27:46<00:00,  1.08it/s]
+100%|██████████| 1144/1144 [02:08<00:00,  8.88it/s]
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+Train Accuracy : 93.71310424804688 	Validation Accuracy : 91.52098083496094
+Validation Loss Decreased( 269.0447855638937 ---> 264.7516337838988 ) 	 Saving The Model
+
+100%|██████████| 1796/1796 [28:18<00:00,  1.06it/s]
+100%|██████████| 1144/1144 [02:06<00:00,  9.08it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 19 	Training Loss: 0.171295881071395 	Validation Loss: 0.29114914753153515 	 time: 00:30:24
+Train Accuracy : 93.8578872680664 	Validation Accuracy : 88.92774200439453
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+100%|██████████| 1796/1796 [28:28<00:00,  1.05it/s]
+100%|██████████| 1144/1144 [02:06<00:00,  9.02it/s]
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+Epoch 20 	Training Loss: 0.17367128944810054 	Validation Loss: 0.19204040009411208 	 time: 00:30:35
+Train Accuracy : 93.65184783935547 	Validation Accuracy : 92.97785949707031
+Validation Loss Decreased( 264.7516337838988 ---> 219.69421770766422 ) 	 Saving The Model
+
+100%|██████████| 1796/1796 [25:03<00:00,  1.19it/s]
+100%|██████████| 1144/1144 [01:36<00:00, 11.83it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 21 	Training Loss: 0.16889888298814962 	Validation Loss: 1.383013668015098 	 time: 00:26:40
+Train Accuracy : 94.06949615478516 	Validation Accuracy : 61.188812255859375
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+100%|██████████| 1796/1796 [21:21<00:00,  1.40it/s]
+100%|██████████| 1144/1144 [01:37<00:00, 11.72it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 22 	Training Loss: 0.15593305252522763 	Validation Loss: 0.29187787832020173 	 time: 00:22:59
+Train Accuracy : 94.53168487548828 	Validation Accuracy : 89.07342529296875
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+100%|██████████| 1796/1796 [21:11<00:00,  1.41it/s]
+100%|██████████| 1144/1144 [01:34<00:00, 12.09it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 23 	Training Loss: 0.16150809311312375 	Validation Loss: 0.30029773062818627 	 time: 00:22:46
+Train Accuracy : 94.26995849609375 	Validation Accuracy : 89.13170623779297
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+100%|██████████| 1796/1796 [21:04<00:00,  1.42it/s]
+100%|██████████| 1144/1144 [01:35<00:00, 11.97it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 24 	Training Loss: 0.14889712126819024 	Validation Loss: 0.20152319045968844 	 time: 00:22:40
+Train Accuracy : 94.40917205810547 	Validation Accuracy : 93.0361328125
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+100%|██████████| 1796/1796 [21:27<00:00,  1.40it/s]
+100%|██████████| 1144/1144 [01:38<00:00, 11.60it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 25 	Training Loss: 0.14728439946149172 	Validation Loss: 0.5768455127529731 	 time: 00:23:05
+Train Accuracy : 94.62635040283203 	Validation Accuracy : 82.22611236572266
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+100%|██████████| 1796/1796 [21:50<00:00,  1.37it/s]
+100%|██████████| 1144/1144 [01:39<00:00, 11.49it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 26 	Training Loss: 0.14183516678818942 	Validation Loss: 0.23056407164745374 	 time: 00:23:30
+Train Accuracy : 94.77669525146484 	Validation Accuracy : 91.89977264404297
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+100%|██████████| 1796/1796 [22:00<00:00,  1.36it/s]
+100%|██████████| 1144/1144 [01:39<00:00, 11.53it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 27 	Training Loss: 0.14214308556186903 	Validation Loss: 0.5357079009287545 	 time: 00:23:39
+Train Accuracy : 94.9883041381836 	Validation Accuracy : 82.10955810546875
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+100%|██████████| 1796/1796 [21:53<00:00,  1.37it/s]
+100%|██████████| 1144/1144 [01:38<00:00, 11.67it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 28 	Training Loss: 0.13260898183909905 	Validation Loss: 0.2149459716328706 	 time: 00:23:31
+Train Accuracy : 95.22775268554688 	Validation Accuracy : 92.22028350830078
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+100%|██████████| 1796/1796 [21:31<00:00,  1.39it/s]
+100%|██████████| 1144/1144 [01:35<00:00, 11.93it/s]
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+Epoch 29 	Training Loss: 0.13150178402533158 	Validation Loss: 0.1735658104628493 	 time: 00:23:07
+Train Accuracy : 95.1776351928711 	Validation Accuracy : 93.85198211669922
+Validation Loss Decreased( 219.69421770766422 ---> 198.5592871694996 ) 	 Saving The Model
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+100%|██████████| 1796/1796 [21:33<00:00,  1.39it/s]
+100%|██████████| 1144/1144 [01:38<00:00, 11.62it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 30 	Training Loss: 0.12626396362086523 	Validation Loss: 0.22748694864047736 	 time: 00:23:12
+Train Accuracy : 95.48390197753906 	Validation Accuracy : 92.22028350830078
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+100%|██████████| 1796/1796 [21:39<00:00,  1.38it/s]
+100%|██████████| 1144/1144 [01:36<00:00, 11.89it/s]
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+Epoch 31 	Training Loss: 0.1243195203532862 	Validation Loss: 0.15573396021900965 	 time: 00:23:15
+Train Accuracy : 95.51174926757812 	Validation Accuracy : 94.2599105834961
+Validation Loss Decreased( 198.5592871694996 ---> 178.15965049054705 ) 	 Saving The Model
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+100%|██████████| 1796/1796 [22:13<00:00,  1.35it/s]
+100%|██████████| 1144/1144 [01:38<00:00, 11.56it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 32 	Training Loss: 0.12209497421210111 	Validation Loss: 0.19751328618058758 	 time: 00:23:52
+Train Accuracy : 95.52288055419922 	Validation Accuracy : 93.26923370361328
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+100%|██████████| 1144/1144 [01:36<00:00, 11.83it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 33 	Training Loss: 0.12278669627672056 	Validation Loss: 0.25568632156248966 	 time: 00:23:13
+Train Accuracy : 95.62311553955078 	Validation Accuracy : 91.05477905273438
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+100%|██████████| 1144/1144 [01:38<00:00, 11.62it/s]
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+Train Accuracy : 95.84585571289062 	Validation Accuracy : 93.06526947021484
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+100%|██████████| 1144/1144 [01:38<00:00, 11.57it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 35 	Training Loss: 0.11643929763509502 	Validation Loss: 0.21698788530739277 	 time: 00:23:26
+Train Accuracy : 95.82915496826172 	Validation Accuracy : 92.62820434570312
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+100%|██████████| 1144/1144 [02:00<00:00,  9.53it/s]
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+Epoch 36 	Training Loss: 0.11314545146687183 	Validation Loss: 0.17937653658909178 	 time: 00:24:49
+Train Accuracy : 95.84585571289062 	Validation Accuracy : 94.11421966552734
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+100%|██████████| 1144/1144 [02:09<00:00,  8.87it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 37 	Training Loss: 0.11227715401390395 	Validation Loss: 0.1841336501966603 	 time: 00:30:50
+Train Accuracy : 95.95166015625 	Validation Accuracy : 93.4149169921875
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+100%|██████████| 1796/1796 [30:40<00:00,  1.02s/it]
+100%|██████████| 1144/1144 [02:10<00:00,  8.78it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 38 	Training Loss: 0.1071929339383833 	Validation Loss: 0.17111435995329394 	 time: 00:32:50
+Train Accuracy : 96.10201263427734 	Validation Accuracy : 93.9102554321289
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+100%|██████████| 1144/1144 [02:10<00:00,  8.74it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 39 	Training Loss: 0.10536505841217854 	Validation Loss: 0.17860585031031706 	 time: 00:33:09
+Train Accuracy : 96.24679565429688 	Validation Accuracy : 93.61888122558594
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+100%|██████████| 1144/1144 [02:11<00:00,  8.73it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Train Accuracy : 96.35816955566406 	Validation Accuracy : 94.34732055664062
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+100%|██████████| 1144/1144 [02:12<00:00,  8.66it/s]
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+Epoch 41 	Training Loss: 0.1052557724369013 	Validation Loss: 0.1674421660797369 	 time: 00:33:34
+Train Accuracy : 96.18553924560547 	Validation Accuracy : 94.49301147460938
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+100%|██████████| 1144/1144 [02:00<00:00,  9.52it/s]
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+Epoch 42 	Training Loss: 0.10263119807659637 	Validation Loss: 0.20584173513718435 	 time: 00:28:26
+Train Accuracy : 96.21895599365234 	Validation Accuracy : 92.5407943725586
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+100%|██████████| 1144/1144 [01:59<00:00,  9.55it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 43 	Training Loss: 0.10232017596648244 	Validation Loss: 0.19133936254487285 	 time: 00:27:43
+Train Accuracy : 96.28577423095703 	Validation Accuracy : 93.4149169921875
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+100%|██████████| 1144/1144 [01:33<00:00, 12.29it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 44 	Training Loss: 0.09797717922651777 	Validation Loss: 0.17769178415356504 	 time: 00:23:49
+Train Accuracy : 96.62545776367188 	Validation Accuracy : 94.2599105834961
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+100%|██████████| 1144/1144 [01:32<00:00, 12.39it/s]
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+Epoch 45 	Training Loss: 0.0962465678795243 	Validation Loss: 0.15445901458264635 	 time: 00:22:08
+Train Accuracy : 96.50294494628906 	Validation Accuracy : 94.46387481689453
+Validation Loss Decreased( 178.15965049054705 ---> 176.70111268254743 ) 	 Saving The Model
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+100%|██████████| 1144/1144 [01:33<00:00, 12.22it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Train Accuracy : 96.7646713256836 	Validation Accuracy : 94.28904724121094
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+100%|██████████| 1144/1144 [01:34<00:00, 12.11it/s]
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+Train Accuracy : 96.80364990234375 	Validation Accuracy : 94.93006896972656
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+100%|██████████| 1144/1144 [01:35<00:00, 11.96it/s]
+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 48 	Training Loss: 0.09256357114334521 	Validation Loss: 0.18674146796817262 	 time: 00:22:37
+Train Accuracy : 96.90388488769531 	Validation Accuracy : 94.05594635009766
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+100%|██████████| 1144/1144 [01:34<00:00, 12.05it/s]
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+Train Accuracy : 96.90388488769531 	Validation Accuracy : 94.2016372680664
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+  0%|          | 0/1796 [00:00<?, ?it/s]
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+Epoch 50 	Training Loss: 0.08749571777711337 	Validation Loss: 0.15860075746543972 	 time: 00:22:43
+Train Accuracy : 96.93729400634766 	Validation Accuracy : 94.72611236572266