diff --git a/thesis_XAML/LIMEtest.ipynb b/thesis_XAML/LIMEtest.ipynb index 35e1d67b..3002a720 100644 --- a/thesis_XAML/LIMEtest.ipynb +++ b/thesis_XAML/LIMEtest.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ @@ -22,12 +22,12 @@ "from matplotlib import pyplot as plt\n", "import matplotlib\n", "\n", - "%matplotlib inline" + "%matplotlib inline\n" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 74, "metadata": {}, "outputs": [ { @@ -58,16 +58,6 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -80,27 +70,27 @@ "plt.scatter(features[labels == 2,2], features[labels == 2, 3], c = 'b', label = 'Virginica')\n", "plt.show()\n", "\n", - "# Visualizing only classes green and blue in 3d (in the first three features)\n", - "custom_colors = ['g', 'b']\n", - "cmap_custom = matplotlib.colors.ListedColormap(custom_colors)\n", - "fig = plt.figure(figsize=(10, 8))\n", - "ax = fig.add_subplot(111, projection='3d')\n", - "ax.scatter(features[labels != 0,1], features[labels != 0,2], features[labels != 0,3], c = labels[labels != 0], cmap = cmap_custom)\n", - "plt.show()" + "# # Visualizing only classes green and blue in 3d (in the first three features)\n", + "# custom_colors = ['g', 'b']\n", + "# cmap_custom = matplotlib.colors.ListedColormap(custom_colors)\n", + "# fig = plt.figure(figsize=(10, 8))\n", + "# ax = fig.add_subplot(111, projection='3d')\n", + "# ax.scatter(features[labels != 0,1], features[labels != 0,2], features[labels != 0,3], c = labels[labels != 0], cmap = cmap_custom)\n", + "# plt.show()" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ - "# Saving only the green and blue classes with features 1,2, and 3\n", + "# Saving only the green and blue classes with features 2, and 3\n", "features = iris.data\n", "labels = iris.target\n", "\n", "features = features[labels != 0] # Drop class 0\n", - "features = features[:,1:] # Drop feature 0\n", + "features = features[:,2:] # Drop features 0 and 1\n", "\n", "labels = labels[labels != 0] # Drop class 0\n", "\n", @@ -117,19 +107,19 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 76, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(80, 3)\n", - "(20, 3)\n", + "(80, 2)\n", + "(20, 2)\n", "(80, 1)\n", "(20, 1)\n", - "torch.Size([80, 3])\n", - "torch.Size([20, 3])\n", + "torch.Size([80, 2])\n", + "torch.Size([20, 2])\n", "torch.Size([80, 1])\n", "torch.Size([20, 1])\n", "tensor([[1],\n", @@ -172,11 +162,11 @@ }, { "cell_type": "code", - "execution_count": 183, + "execution_count": 77, "metadata": {}, "outputs": [], "source": [ - "rf = sklearn.ensemble.RandomForestClassifier(n_estimators=500)\n", + "# rf = sklearn.ensemble.RandomForestClassifier(n_estimators=500)\n", "\n", "class LogisticRegressor(torch.nn.Module):\n", " def __init__(self, input_dim, output_dim):\n", @@ -204,256 +194,528 @@ " predicted_class = x.detach().round()\n", " return predicted_class.reshape(-1,1)\n", "\n", - "model = LogisticRegressor(3,1)" - ] - }, - { - "cell_type": "code", - "execution_count": 165, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor([1.2310, 1.4350])\n" - ] - } - ], - "source": [ - "a = np.array([1.2314, 1.43453])\n", - "a = np.round(a,3)\n", - "a = torch.from_numpy(a).float()\n", - "print(a)\n" + "model = LogisticRegressor(2,1)" ] }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 78, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/tomas/desktop/AMLvenv/EXPLORERvenv/lib/python3.10/site-packages/sklearn/base.py:1351: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n", - " return fit_method(estimator, *args, **kwargs)\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "Epoch [1/200], Loss: 0.9812079668045044 Training accuracy = 0.4749999940395355 | Test accuracy = 0.6000000238418579\n", - "Epoch [2/200], Loss: 0.7612131834030151 Training accuracy = 0.4749999940395355 | Test accuracy = 0.6000000238418579\n", - "Epoch [3/200], Loss: 0.8870990872383118 Training accuracy = 0.4749999940395355 | Test accuracy = 0.6000000238418579\n", - "Epoch [4/200], Loss: 0.7878677248954773 Training accuracy = 0.4625000059604645 | Test accuracy = 0.5\n", - "Epoch [5/200], Loss: 0.6921416521072388 Training accuracy = 0.5249999761581421 | Test accuracy = 0.4000000059604645\n", - "Epoch [6/200], Loss: 0.7279365062713623 Training accuracy = 0.5249999761581421 | Test accuracy = 0.4000000059604645\n", - "Epoch [7/200], Loss: 0.7635400295257568 Training accuracy = 0.5249999761581421 | Test accuracy = 0.4000000059604645\n", - "Epoch [8/200], Loss: 0.7182316184043884 Training accuracy = 0.6875 | Test accuracy = 0.5\n", - "Epoch [9/200], Loss: 0.6567119359970093 Training accuracy = 0.48750001192092896 | Test accuracy = 0.699999988079071\n", - "Epoch [10/200], Loss: 0.6476988792419434 Training accuracy = 0.4749999940395355 | Test accuracy = 0.6000000238418579\n", - "Epoch [11/200], Loss: 0.6704385876655579 Training accuracy = 0.4749999940395355 | Test accuracy = 0.6000000238418579\n", - "Epoch [12/200], Loss: 0.6675988435745239 Training accuracy = 0.48750001192092896 | Test accuracy = 0.6499999761581421\n", - "Epoch [13/200], Loss: 0.6316312551498413 Training accuracy = 0.7749999761581421 | Test accuracy = 0.800000011920929\n", - "Epoch [14/200], Loss: 0.5971710085868835 Training accuracy = 0.800000011920929 | Test accuracy = 0.75\n", - "Epoch [15/200], Loss: 0.5914218425750732 Training accuracy = 0.6625000238418579 | Test accuracy = 0.550000011920929\n", - "Epoch [16/200], Loss: 0.5997393727302551 Training accuracy = 0.6625000238418579 | Test accuracy = 0.550000011920929\n", - "Epoch [17/200], Loss: 0.5918465256690979 Training accuracy = 0.7875000238418579 | Test accuracy = 0.6499999761581421\n", - "Epoch [18/200], Loss: 0.5654489398002625 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", - "Epoch [19/200], Loss: 0.5426129698753357 Training accuracy = 0.824999988079071 | Test accuracy = 0.8500000238418579\n", - "Epoch [20/200], Loss: 0.5368642210960388 Training accuracy = 0.7250000238418579 | Test accuracy = 0.8500000238418579\n", - "Epoch [21/200], Loss: 0.5374875068664551 Training accuracy = 0.737500011920929 | Test accuracy = 0.8999999761581421\n", - "Epoch [22/200], Loss: 0.5284812450408936 Training accuracy = 0.862500011920929 | Test accuracy = 0.8999999761581421\n", - "Epoch [23/200], Loss: 0.5094448328018188 Training accuracy = 0.9375 | Test accuracy = 0.8999999761581421\n", - "Epoch [24/200], Loss: 0.49271684885025024 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", - "Epoch [25/200], Loss: 0.4861028790473938 Training accuracy = 0.875 | Test accuracy = 0.800000011920929\n", - "Epoch [26/200], Loss: 0.4834209978580475 Training accuracy = 0.875 | Test accuracy = 0.800000011920929\n", - "Epoch [27/200], Loss: 0.4747545123100281 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", - "Epoch [28/200], Loss: 0.4601968824863434 Training accuracy = 0.9375 | Test accuracy = 0.8999999761581421\n", - "Epoch [29/200], Loss: 0.4477963447570801 Training accuracy = 0.9375 | Test accuracy = 0.8999999761581421\n", - "Epoch [30/200], Loss: 0.4415801465511322 Training accuracy = 0.949999988079071 | Test accuracy = 0.949999988079071\n", - "Epoch [31/200], Loss: 0.4371728003025055 Training accuracy = 0.949999988079071 | Test accuracy = 0.949999988079071\n", - "Epoch [32/200], Loss: 0.42913103103637695 Training accuracy = 0.9375 | Test accuracy = 0.8999999761581421\n", - "Epoch [33/200], Loss: 0.41799744963645935 Training accuracy = 0.9375 | Test accuracy = 0.8999999761581421\n", - "Epoch [34/200], Loss: 0.4084540903568268 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [35/200], Loss: 0.4026489853858948 Training accuracy = 0.9750000238418579 | Test accuracy = 0.949999988079071\n", - "Epoch [36/200], Loss: 0.39780279994010925 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [37/200], Loss: 0.3906826972961426 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [38/200], Loss: 0.3819388747215271 Training accuracy = 0.925000011920929 | Test accuracy = 0.8999999761581421\n", - "Epoch [39/200], Loss: 0.3744996190071106 Training accuracy = 0.9375 | Test accuracy = 0.8999999761581421\n", - "Epoch [40/200], Loss: 0.36930108070373535 Training accuracy = 0.9375 | Test accuracy = 0.949999988079071\n", - "Epoch [41/200], Loss: 0.3644799590110779 Training accuracy = 0.9375 | Test accuracy = 0.949999988079071\n", - "Epoch [42/200], Loss: 0.35832762718200684 Training accuracy = 0.9375 | Test accuracy = 0.8999999761581421\n", - "Epoch [43/200], Loss: 0.3514231741428375 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", - "Epoch [44/200], Loss: 0.34544849395751953 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", - "Epoch [45/200], Loss: 0.34082087874412537 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", - "Epoch [46/200], Loss: 0.336367666721344 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", - "Epoch [47/200], Loss: 0.3311343193054199 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", - "Epoch [48/200], Loss: 0.32557979226112366 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", - "Epoch [49/200], Loss: 0.32069727778434753 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", - "Epoch [50/200], Loss: 0.3166188895702362 Training accuracy = 0.9375 | Test accuracy = 0.949999988079071\n", - "Epoch [51/200], Loss: 0.31261521577835083 Training accuracy = 0.949999988079071 | Test accuracy = 0.949999988079071\n", - "Epoch [52/200], Loss: 0.3081921935081482 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", - "Epoch [53/200], Loss: 0.3036646842956543 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", - "Epoch [54/200], Loss: 0.2995930314064026 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", - "Epoch [55/200], Loss: 0.29601550102233887 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", - "Epoch [56/200], Loss: 0.2924902141094208 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", - "Epoch [57/200], Loss: 0.28874629735946655 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", - "Epoch [58/200], Loss: 0.28499242663383484 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", - "Epoch [59/200], Loss: 0.28155025839805603 Training accuracy = 0.949999988079071 | Test accuracy = 0.949999988079071\n", - "Epoch [60/200], Loss: 0.27841752767562866 Training accuracy = 0.949999988079071 | Test accuracy = 0.949999988079071\n", - "Epoch [61/200], Loss: 0.27533119916915894 Training accuracy = 0.949999988079071 | Test accuracy = 0.949999988079071\n", - "Epoch [62/200], Loss: 0.27214616537094116 Training accuracy = 0.949999988079071 | Test accuracy = 0.949999988079071\n", - "Epoch [63/200], Loss: 0.2689896821975708 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [64/200], Loss: 0.2660435736179352 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", - "Epoch [65/200], Loss: 0.263300359249115 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", - "Epoch [66/200], Loss: 0.2606045603752136 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", - "Epoch [67/200], Loss: 0.25787287950515747 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [68/200], Loss: 0.2551814317703247 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [69/200], Loss: 0.25263482332229614 Training accuracy = 0.949999988079071 | Test accuracy = 0.949999988079071\n", - "Epoch [70/200], Loss: 0.2502261996269226 Training accuracy = 0.949999988079071 | Test accuracy = 0.949999988079071\n", - "Epoch [71/200], Loss: 0.2478644847869873 Training accuracy = 0.949999988079071 | Test accuracy = 0.949999988079071\n", - "Epoch [72/200], Loss: 0.24550127983093262 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [73/200], Loss: 0.24317936599254608 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [74/200], Loss: 0.24095885455608368 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [75/200], Loss: 0.2388363778591156 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [76/200], Loss: 0.23675866425037384 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [77/200], Loss: 0.23469576239585876 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [78/200], Loss: 0.23267097771167755 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [79/200], Loss: 0.2307194024324417 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [80/200], Loss: 0.22883984446525574 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [81/200], Loss: 0.22700151801109314 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [82/200], Loss: 0.22518546879291534 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [83/200], Loss: 0.22340349853038788 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [84/200], Loss: 0.22167587280273438 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [85/200], Loss: 0.22000300884246826 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [86/200], Loss: 0.2183670699596405 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [87/200], Loss: 0.21675562858581543 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [88/200], Loss: 0.2151741236448288 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [89/200], Loss: 0.2136344164609909 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [90/200], Loss: 0.21213741600513458 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [91/200], Loss: 0.21067290008068085 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [92/200], Loss: 0.2092326581478119 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [93/200], Loss: 0.2078188955783844 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [94/200], Loss: 0.2064381092786789 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [95/200], Loss: 0.2050914466381073 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [96/200], Loss: 0.20377306640148163 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [97/200], Loss: 0.20247752964496613 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [98/200], Loss: 0.20120520889759064 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [99/200], Loss: 0.19995978474617004 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [100/200], Loss: 0.1987421214580536 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [101/200], Loss: 0.1975489854812622 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [102/200], Loss: 0.1963767260313034 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [103/200], Loss: 0.19522501528263092 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [104/200], Loss: 0.1940956562757492 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [105/200], Loss: 0.19298934936523438 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [106/200], Loss: 0.19190426170825958 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [107/200], Loss: 0.19083797931671143 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [108/200], Loss: 0.18978984653949738 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [109/200], Loss: 0.1887606829404831 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [110/200], Loss: 0.18775096535682678 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [111/200], Loss: 0.1867595762014389 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [112/200], Loss: 0.18578505516052246 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [113/200], Loss: 0.18482664227485657 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [114/200], Loss: 0.18388453125953674 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [115/200], Loss: 0.18295904994010925 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [116/200], Loss: 0.18204958736896515 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [117/200], Loss: 0.1811549961566925 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [118/200], Loss: 0.18027479946613312 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [119/200], Loss: 0.17940883338451385 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [120/200], Loss: 0.1785571575164795 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [121/200], Loss: 0.17771951854228973 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [122/200], Loss: 0.1768951714038849 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [123/200], Loss: 0.1760835349559784 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [124/200], Loss: 0.1752844750881195 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [125/200], Loss: 0.17449793219566345 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [126/200], Loss: 0.17372369766235352 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [127/200], Loss: 0.1729612648487091 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [128/200], Loss: 0.1722101867198944 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [129/200], Loss: 0.1714702546596527 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [130/200], Loss: 0.17074139416217804 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [131/200], Loss: 0.1700233519077301 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [132/200], Loss: 0.16931584477424622 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [133/200], Loss: 0.16861850023269653 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [134/200], Loss: 0.16793106496334076 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [135/200], Loss: 0.1672535091638565 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [136/200], Loss: 0.1665855199098587 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [137/200], Loss: 0.1659269481897354 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [138/200], Loss: 0.16527751088142395 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [139/200], Loss: 0.1646369844675064 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [140/200], Loss: 0.16400520503520966 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [141/200], Loss: 0.16338202357292175 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [142/200], Loss: 0.16276727616786957 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [143/200], Loss: 0.162160724401474 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [144/200], Loss: 0.16156217455863953 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [145/200], Loss: 0.1609714776277542 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [146/200], Loss: 0.16038855910301208 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [147/200], Loss: 0.15981321036815643 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [148/200], Loss: 0.15924522280693054 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [149/200], Loss: 0.15868444740772247 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [150/200], Loss: 0.15813077986240387 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [151/200], Loss: 0.15758410096168518 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [152/200], Loss: 0.1570442169904709 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [153/200], Loss: 0.15651102364063263 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [154/200], Loss: 0.15598443150520325 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [155/200], Loss: 0.15546421706676483 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [156/200], Loss: 0.15495029091835022 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [157/200], Loss: 0.15444254875183105 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [158/200], Loss: 0.15394088625907898 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [159/200], Loss: 0.15344519913196564 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [160/200], Loss: 0.1529552936553955 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [161/200], Loss: 0.1524711400270462 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [162/200], Loss: 0.1519925892353058 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [163/200], Loss: 0.15151961147785187 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [164/200], Loss: 0.15105196833610535 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [165/200], Loss: 0.15058963000774384 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [166/200], Loss: 0.15013256669044495 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [167/200], Loss: 0.14968064427375793 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [168/200], Loss: 0.14923366904258728 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [169/200], Loss: 0.14879170060157776 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [170/200], Loss: 0.14835456013679504 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [171/200], Loss: 0.14792220294475555 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [172/200], Loss: 0.14749452471733093 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [173/200], Loss: 0.14707142114639282 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [174/200], Loss: 0.14665289223194122 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [175/200], Loss: 0.14623872935771942 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [176/200], Loss: 0.1458289921283722 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [177/200], Loss: 0.1454235315322876 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [178/200], Loss: 0.14502227306365967 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [179/200], Loss: 0.14462518692016602 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [180/200], Loss: 0.14423221349716187 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [181/200], Loss: 0.14384320378303528 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [182/200], Loss: 0.14345812797546387 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [183/200], Loss: 0.14307697117328644 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [184/200], Loss: 0.14269959926605225 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [185/200], Loss: 0.14232602715492249 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [186/200], Loss: 0.14195607602596283 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [187/200], Loss: 0.14158980548381805 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [188/200], Loss: 0.141227126121521 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [189/200], Loss: 0.1408679336309433 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [190/200], Loss: 0.14051222801208496 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [191/200], Loss: 0.14015991985797882 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [192/200], Loss: 0.1398109495639801 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [193/200], Loss: 0.1394653171300888 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [194/200], Loss: 0.1391228884458542 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [195/200], Loss: 0.13878369331359863 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [196/200], Loss: 0.1384476125240326 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [197/200], Loss: 0.13811467587947845 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [198/200], Loss: 0.13778476417064667 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [199/200], Loss: 0.1374579221010208 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", - "Epoch [200/200], Loss: 0.13713398575782776 Training accuracy = 0.9624999761581421 | Test accuracy = 0.949999988079071\n", + "Epoch [1/500], Loss: 1.2092080116271973 Training accuracy = 0.5249999761581421 | Test accuracy = 0.4000000059604645\n", + "Epoch [2/500], Loss: 1.0567749738693237 Training accuracy = 0.5249999761581421 | Test accuracy = 0.4000000059604645\n", + "Epoch [3/500], Loss: 0.924190878868103 Training accuracy = 0.5249999761581421 | Test accuracy = 0.4000000059604645\n", + "Epoch [4/500], Loss: 0.8170582056045532 Training accuracy = 0.5249999761581421 | Test accuracy = 0.4000000059604645\n", + "Epoch [5/500], Loss: 0.7403010725975037 Training accuracy = 0.5249999761581421 | Test accuracy = 0.4000000059604645\n", + "Epoch [6/500], Loss: 0.6961954236030579 Training accuracy = 0.48750001192092896 | Test accuracy = 0.6000000238418579\n", + "Epoch [7/500], Loss: 0.6823136806488037 Training accuracy = 0.4749999940395355 | Test accuracy = 0.6000000238418579\n", + "Epoch [8/500], Loss: 0.6909869909286499 Training accuracy = 0.4749999940395355 | Test accuracy = 0.6000000238418579\n", + "Epoch [9/500], Loss: 0.7114517092704773 Training accuracy = 0.4749999940395355 | Test accuracy = 0.6000000238418579\n", + "Epoch [10/500], Loss: 0.7335217595100403 Training accuracy = 0.4749999940395355 | Test accuracy = 0.6000000238418579\n", + "Epoch [11/500], Loss: 0.7502025365829468 Training accuracy = 0.4749999940395355 | Test accuracy = 0.6000000238418579\n", + "Epoch [12/500], Loss: 0.7581775188446045 Training accuracy = 0.4749999940395355 | Test accuracy = 0.6000000238418579\n", + "Epoch [13/500], Loss: 0.7569910287857056 Training accuracy = 0.4749999940395355 | Test accuracy = 0.6000000238418579\n", + "Epoch [14/500], Loss: 0.7479905486106873 Training accuracy = 0.4749999940395355 | Test accuracy = 0.6000000238418579\n", + "Epoch [15/500], Loss: 0.7334919571876526 Training accuracy = 0.4749999940395355 | Test accuracy = 0.6000000238418579\n", + "Epoch [16/500], Loss: 0.7162046432495117 Training accuracy = 0.4749999940395355 | Test accuracy = 0.6000000238418579\n", + "Epoch [17/500], Loss: 0.698822557926178 Training accuracy = 0.4749999940395355 | Test accuracy = 0.6000000238418579\n", + "Epoch [18/500], Loss: 0.6836866736412048 Training accuracy = 0.4749999940395355 | Test accuracy = 0.6000000238418579\n", + "Epoch [19/500], Loss: 0.6724870800971985 Training accuracy = 0.48750001192092896 | Test accuracy = 0.6000000238418579\n", + "Epoch [20/500], Loss: 0.6660295724868774 Training accuracy = 0.574999988079071 | Test accuracy = 0.75\n", + "Epoch [21/500], Loss: 0.6641384363174438 Training accuracy = 0.862500011920929 | Test accuracy = 0.75\n", + "Epoch [22/500], Loss: 0.6657622456550598 Training accuracy = 0.574999988079071 | Test accuracy = 0.44999998807907104\n", + "Epoch [23/500], Loss: 0.6692863702774048 Training accuracy = 0.5249999761581421 | Test accuracy = 0.4000000059604645\n", + "Epoch [24/500], Loss: 0.6729667782783508 Training accuracy = 0.5249999761581421 | Test accuracy = 0.4000000059604645\n", + "Epoch [25/500], Loss: 0.6753437519073486 Training accuracy = 0.5249999761581421 | Test accuracy = 0.4000000059604645\n", + "Epoch [26/500], Loss: 0.675518810749054 Training accuracy = 0.5249999761581421 | Test accuracy = 0.4000000059604645\n", + "Epoch [27/500], Loss: 0.6732469797134399 Training accuracy = 0.5249999761581421 | Test accuracy = 0.4000000059604645\n", + "Epoch [28/500], Loss: 0.6688666343688965 Training accuracy = 0.5249999761581421 | Test accuracy = 0.4000000059604645\n", + "Epoch [29/500], Loss: 0.6631220579147339 Training accuracy = 0.625 | Test accuracy = 0.5\n", + "Epoch [30/500], Loss: 0.6569377183914185 Training accuracy = 0.875 | Test accuracy = 0.75\n", + "Epoch [31/500], Loss: 0.6511930227279663 Training accuracy = 0.9375 | Test accuracy = 0.8999999761581421\n", + "Epoch [32/500], Loss: 0.6465399265289307 Training accuracy = 0.7124999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [33/500], Loss: 0.6432918310165405 Training accuracy = 0.574999988079071 | Test accuracy = 0.800000011920929\n", + "Epoch [34/500], Loss: 0.6414035558700562 Training accuracy = 0.5249999761581421 | Test accuracy = 0.699999988079071\n", + "Epoch [35/500], Loss: 0.6405352354049683 Training accuracy = 0.5249999761581421 | Test accuracy = 0.6499999761581421\n", + "Epoch [36/500], Loss: 0.6401785612106323 Training accuracy = 0.5249999761581421 | Test accuracy = 0.6499999761581421\n", + "Epoch [37/500], Loss: 0.6398012638092041 Training accuracy = 0.512499988079071 | Test accuracy = 0.6000000238418579\n", + "Epoch [38/500], Loss: 0.6389738321304321 Training accuracy = 0.512499988079071 | Test accuracy = 0.6000000238418579\n", + "Epoch [39/500], Loss: 0.6374509930610657 Training accuracy = 0.5249999761581421 | Test accuracy = 0.6499999761581421\n", + "Epoch [40/500], Loss: 0.6351948976516724 Training accuracy = 0.5249999761581421 | Test accuracy = 0.699999988079071\n", + "Epoch [41/500], Loss: 0.6323497891426086 Training accuracy = 0.5249999761581421 | Test accuracy = 0.699999988079071\n", + "Epoch [42/500], Loss: 0.6291790008544922 Training accuracy = 0.574999988079071 | Test accuracy = 0.75\n", + "Epoch [43/500], Loss: 0.6259851455688477 Training accuracy = 0.5874999761581421 | Test accuracy = 0.800000011920929\n", + "Epoch [44/500], Loss: 0.6230324506759644 Training accuracy = 0.7124999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [45/500], Loss: 0.6204875707626343 Training accuracy = 0.7875000238418579 | Test accuracy = 0.8999999761581421\n", + "Epoch [46/500], Loss: 0.6183923482894897 Training accuracy = 0.8999999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [47/500], Loss: 0.6166700124740601 Training accuracy = 0.9375 | Test accuracy = 0.949999988079071\n", + "Epoch [48/500], Loss: 0.6151617169380188 Training accuracy = 0.9750000238418579 | Test accuracy = 0.8999999761581421\n", + "Epoch [49/500], Loss: 0.6136801838874817 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [50/500], Loss: 0.6120628118515015 Training accuracy = 0.9624999761581421 | Test accuracy = 0.800000011920929\n", + "Epoch [51/500], Loss: 0.6102117300033569 Training accuracy = 0.9624999761581421 | Test accuracy = 0.800000011920929\n", + "Epoch [52/500], Loss: 0.6081091165542603 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [53/500], Loss: 0.6058095693588257 Training accuracy = 0.9750000238418579 | Test accuracy = 0.8999999761581421\n", + "Epoch [54/500], Loss: 0.6034132838249207 Training accuracy = 0.9375 | Test accuracy = 0.949999988079071\n", + "Epoch [55/500], Loss: 0.6010309457778931 Training accuracy = 0.925000011920929 | Test accuracy = 0.8999999761581421\n", + "Epoch [56/500], Loss: 0.5987515449523926 Training accuracy = 0.9125000238418579 | Test accuracy = 0.8999999761581421\n", + "Epoch [57/500], Loss: 0.5966211557388306 Training accuracy = 0.8374999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [58/500], Loss: 0.5946372151374817 Training accuracy = 0.800000011920929 | Test accuracy = 0.8999999761581421\n", + "Epoch [59/500], Loss: 0.592757523059845 Training accuracy = 0.7875000238418579 | Test accuracy = 0.8999999761581421\n", + "Epoch [60/500], Loss: 0.5909184813499451 Training accuracy = 0.7875000238418579 | Test accuracy = 0.8999999761581421\n", + "Epoch [61/500], Loss: 0.5890573263168335 Training accuracy = 0.7875000238418579 | Test accuracy = 0.8999999761581421\n", + "Epoch [62/500], Loss: 0.5871292948722839 Training accuracy = 0.7875000238418579 | Test accuracy = 0.8999999761581421\n", + "Epoch [63/500], Loss: 0.5851176977157593 Training accuracy = 0.7875000238418579 | Test accuracy = 0.8999999761581421\n", + "Epoch [64/500], Loss: 0.5830339193344116 Training accuracy = 0.8374999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [65/500], Loss: 0.5809097290039062 Training accuracy = 0.8500000238418579 | Test accuracy = 0.8999999761581421\n", + "Epoch [66/500], Loss: 0.5787839889526367 Training accuracy = 0.9125000238418579 | Test accuracy = 0.8999999761581421\n", + "Epoch [67/500], Loss: 0.5766910314559937 Training accuracy = 0.925000011920929 | Test accuracy = 0.8999999761581421\n", + "Epoch [68/500], Loss: 0.5746504068374634 Training accuracy = 0.925000011920929 | Test accuracy = 0.949999988079071\n", + "Epoch [69/500], Loss: 0.5726639628410339 Training accuracy = 0.949999988079071 | Test accuracy = 0.949999988079071\n", + "Epoch [70/500], Loss: 0.570717990398407 Training accuracy = 0.949999988079071 | Test accuracy = 0.8500000238418579\n", + "Epoch [71/500], Loss: 0.5687900185585022 Training accuracy = 0.949999988079071 | Test accuracy = 0.8500000238418579\n", + "Epoch [72/500], Loss: 0.566857099533081 Training accuracy = 0.949999988079071 | Test accuracy = 0.8500000238418579\n", + "Epoch [73/500], Loss: 0.5649030208587646 Training accuracy = 0.949999988079071 | Test accuracy = 0.8500000238418579\n", + "Epoch [74/500], Loss: 0.5629225373268127 Training accuracy = 0.949999988079071 | Test accuracy = 0.8500000238418579\n", + "Epoch [75/500], Loss: 0.5609206557273865 Training accuracy = 0.949999988079071 | Test accuracy = 0.8500000238418579\n", + "Epoch [76/500], Loss: 0.5589100122451782 Training accuracy = 0.949999988079071 | Test accuracy = 0.8500000238418579\n", + "Epoch [77/500], Loss: 0.5569050908088684 Training accuracy = 0.9375 | Test accuracy = 0.8500000238418579\n", + "Epoch [78/500], Loss: 0.5549175143241882 Training accuracy = 0.949999988079071 | Test accuracy = 0.8500000238418579\n", + "Epoch [79/500], Loss: 0.5529531240463257 Training accuracy = 0.949999988079071 | Test accuracy = 0.8500000238418579\n", + "Epoch [80/500], Loss: 0.5510108470916748 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [81/500], Loss: 0.5490843653678894 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [82/500], Loss: 0.5471653938293457 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [83/500], Loss: 0.5452463030815125 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [84/500], Loss: 0.5433231592178345 Training accuracy = 0.949999988079071 | Test accuracy = 0.8500000238418579\n", + "Epoch [85/500], Loss: 0.5413955450057983 Training accuracy = 0.949999988079071 | Test accuracy = 0.8500000238418579\n", + "Epoch [86/500], Loss: 0.5394672155380249 Training accuracy = 0.9375 | Test accuracy = 0.8500000238418579\n", + "Epoch [87/500], Loss: 0.537542998790741 Training accuracy = 0.9375 | Test accuracy = 0.8500000238418579\n", + "Epoch [88/500], Loss: 0.535628080368042 Training accuracy = 0.949999988079071 | Test accuracy = 0.8500000238418579\n", + "Epoch [89/500], Loss: 0.5337256193161011 Training accuracy = 0.949999988079071 | Test accuracy = 0.8500000238418579\n", + "Epoch [90/500], Loss: 0.5318363308906555 Training accuracy = 0.949999988079071 | Test accuracy = 0.8500000238418579\n", + "Epoch [91/500], Loss: 0.5299584865570068 Training accuracy = 0.949999988079071 | Test accuracy = 0.8500000238418579\n", + "Epoch [92/500], Loss: 0.5280892252922058 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [93/500], Loss: 0.5262256264686584 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [94/500], Loss: 0.5243656039237976 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [95/500], Loss: 0.5225087404251099 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [96/500], Loss: 0.5206562280654907 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [97/500], Loss: 0.5188096761703491 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [98/500], Loss: 0.5169712901115417 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [99/500], Loss: 0.5151423215866089 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [100/500], Loss: 0.5133234262466431 Training accuracy = 0.949999988079071 | Test accuracy = 0.8500000238418579\n", + "Epoch [101/500], Loss: 0.5115140080451965 Training accuracy = 0.949999988079071 | Test accuracy = 0.8500000238418579\n", + "Epoch [102/500], Loss: 0.5097131729125977 Training accuracy = 0.949999988079071 | Test accuracy = 0.8500000238418579\n", + "Epoch [103/500], Loss: 0.5079197287559509 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [104/500], Loss: 0.5061330795288086 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [105/500], Loss: 0.5043530464172363 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [106/500], Loss: 0.5025798678398132 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [107/500], Loss: 0.5008144378662109 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [108/500], Loss: 0.4990573525428772 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [109/500], Loss: 0.49730920791625977 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [110/500], Loss: 0.49557018280029297 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [111/500], Loss: 0.49383997917175293 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [112/500], Loss: 0.4921184480190277 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [113/500], Loss: 0.49040499329566956 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [114/500], Loss: 0.48869943618774414 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [115/500], Loss: 0.4870017468929291 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [116/500], Loss: 0.4853120744228363 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [117/500], Loss: 0.4836307466030121 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [118/500], Loss: 0.48195797204971313 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [119/500], Loss: 0.48029404878616333 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [120/500], Loss: 0.4786389470100403 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [121/500], Loss: 0.47699251770973206 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [122/500], Loss: 0.47535473108291626 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [123/500], Loss: 0.47372540831565857 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [124/500], Loss: 0.47210460901260376 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8500000238418579\n", + "Epoch [125/500], Loss: 0.47049206495285034 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [126/500], Loss: 0.4688881039619446 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [127/500], Loss: 0.4672926068305969 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [128/500], Loss: 0.46570587158203125 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [129/500], Loss: 0.464127779006958 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [130/500], Loss: 0.4625583589076996 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [131/500], Loss: 0.46099767088890076 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [132/500], Loss: 0.4594454765319824 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [133/500], Loss: 0.45790180563926697 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [134/500], Loss: 0.4563665986061096 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [135/500], Loss: 0.4548397958278656 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [136/500], Loss: 0.453321635723114 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [137/500], Loss: 0.45181187987327576 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [138/500], Loss: 0.4503106474876404 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [139/500], Loss: 0.44881802797317505 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [140/500], Loss: 0.4473338723182678 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [141/500], Loss: 0.4458581805229187 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [142/500], Loss: 0.4443908631801605 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [143/500], Loss: 0.4429319500923157 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [144/500], Loss: 0.441481351852417 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [145/500], Loss: 0.44003909826278687 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [146/500], Loss: 0.4386052191257477 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [147/500], Loss: 0.4371796250343323 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [148/500], Loss: 0.43576231598854065 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [149/500], Loss: 0.4343532919883728 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [150/500], Loss: 0.4329524636268616 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [151/500], Loss: 0.4315597414970398 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [152/500], Loss: 0.4301753044128418 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [153/500], Loss: 0.4287989139556885 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [154/500], Loss: 0.42743057012557983 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [155/500], Loss: 0.42607030272483826 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [156/500], Loss: 0.42471808195114136 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [157/500], Loss: 0.42337384819984436 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [158/500], Loss: 0.4220375418663025 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [159/500], Loss: 0.42070919275283813 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [160/500], Loss: 0.41938871145248413 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [161/500], Loss: 0.4180760383605957 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [162/500], Loss: 0.4167712330818176 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [163/500], Loss: 0.4154741168022156 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [164/500], Loss: 0.4141848087310791 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [165/500], Loss: 0.41290315985679626 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [166/500], Loss: 0.4116291403770447 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [167/500], Loss: 0.41036278009414673 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [168/500], Loss: 0.40910401940345764 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [169/500], Loss: 0.40785273909568787 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [170/500], Loss: 0.4066089689731598 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [171/500], Loss: 0.405372679233551 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [172/500], Loss: 0.40414372086524963 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [173/500], Loss: 0.4029221534729004 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [174/500], Loss: 0.4017079472541809 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [175/500], Loss: 0.4005010724067688 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [176/500], Loss: 0.39930135011672974 Training accuracy = 0.9624999761581421 | Test accuracy = 0.8999999761581421\n", + "Epoch [177/500], Loss: 0.3981088101863861 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [178/500], Loss: 0.3969234824180603 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [179/500], Loss: 0.39574533700942993 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [180/500], Loss: 0.39457422494888306 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [181/500], Loss: 0.3934100866317749 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [182/500], Loss: 0.39225298166275024 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [183/500], Loss: 0.3911028206348419 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [184/500], Loss: 0.38995957374572754 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [185/500], Loss: 0.38882318139076233 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [186/500], Loss: 0.3876935839653015 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [187/500], Loss: 0.3865707218647003 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [188/500], Loss: 0.3854547142982483 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [189/500], Loss: 0.38434529304504395 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [190/500], Loss: 0.38324254751205444 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [191/500], Loss: 0.382146418094635 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [192/500], Loss: 0.38105684518814087 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [193/500], Loss: 0.37997379899024963 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [194/500], Loss: 0.37889721989631653 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [195/500], Loss: 0.37782707810401917 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [196/500], Loss: 0.3767632842063904 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [197/500], Loss: 0.37570586800575256 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [198/500], Loss: 0.3746547996997833 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [199/500], Loss: 0.3736099600791931 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [200/500], Loss: 0.37257128953933716 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [201/500], Loss: 0.3715388774871826 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [202/500], Loss: 0.37051257491111755 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [203/500], Loss: 0.3694923520088196 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [204/500], Loss: 0.3684782087802887 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [205/500], Loss: 0.36747005581855774 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [206/500], Loss: 0.36646780371665955 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [207/500], Loss: 0.3654715418815613 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [208/500], Loss: 0.36448121070861816 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [209/500], Loss: 0.36349666118621826 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [210/500], Loss: 0.3625178933143616 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [211/500], Loss: 0.3615449070930481 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [212/500], Loss: 0.36057770252227783 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [213/500], Loss: 0.35961613059043884 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [214/500], Loss: 0.35866016149520874 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [215/500], Loss: 0.3577098250389099 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [216/500], Loss: 0.35676509141921997 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [217/500], Loss: 0.35582584142684937 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [218/500], Loss: 0.3548920750617981 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [219/500], Loss: 0.3539637625217438 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [220/500], Loss: 0.3530408442020416 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [221/500], Loss: 0.35212329030036926 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [222/500], Loss: 0.3512110710144043 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [223/500], Loss: 0.35030415654182434 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [224/500], Loss: 0.34940242767333984 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [225/500], Loss: 0.3485059440135956 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [226/500], Loss: 0.34761467576026917 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [227/500], Loss: 0.34672847390174866 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [228/500], Loss: 0.3458474278450012 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [229/500], Loss: 0.3449714183807373 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [230/500], Loss: 0.3441004157066345 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [231/500], Loss: 0.34323441982269287 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [232/500], Loss: 0.3423733711242676 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [233/500], Loss: 0.34151726961135864 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [234/500], Loss: 0.3406660258769989 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [235/500], Loss: 0.3398195803165436 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [236/500], Loss: 0.33897799253463745 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [237/500], Loss: 0.33814117312431335 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [238/500], Loss: 0.3373090624809265 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [239/500], Loss: 0.33648166060447693 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [240/500], Loss: 0.3356589674949646 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [241/500], Loss: 0.3348408639431 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [242/500], Loss: 0.33402734994888306 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [243/500], Loss: 0.3332184851169586 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [244/500], Loss: 0.33241409063339233 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [245/500], Loss: 0.3316141664981842 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [246/500], Loss: 0.3308188021183014 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [247/500], Loss: 0.3300277590751648 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [248/500], Loss: 0.32924115657806396 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [249/500], Loss: 0.32845887541770935 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [250/500], Loss: 0.3276810050010681 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [251/500], Loss: 0.3269074261188507 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [252/500], Loss: 0.32613810896873474 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [253/500], Loss: 0.3253730237483978 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [254/500], Loss: 0.3246121406555176 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [255/500], Loss: 0.323855459690094 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [256/500], Loss: 0.3231029212474823 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [257/500], Loss: 0.3223544657230377 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [258/500], Loss: 0.32161015272140503 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [259/500], Loss: 0.3208698630332947 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [260/500], Loss: 0.32013359665870667 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [261/500], Loss: 0.3194013237953186 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [262/500], Loss: 0.3186730146408081 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [263/500], Loss: 0.3179486095905304 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [264/500], Loss: 0.31722813844680786 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [265/500], Loss: 0.3165115714073181 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [266/500], Loss: 0.3157988488674164 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [267/500], Loss: 0.3150899410247803 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [268/500], Loss: 0.3143848776817322 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [269/500], Loss: 0.31368350982666016 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [270/500], Loss: 0.31298592686653137 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [271/500], Loss: 0.3122919499874115 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [272/500], Loss: 0.31160181760787964 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [273/500], Loss: 0.3109152019023895 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [274/500], Loss: 0.3102322220802307 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [275/500], Loss: 0.30955296754837036 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [276/500], Loss: 0.308877170085907 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [277/500], Loss: 0.3082050085067749 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [278/500], Loss: 0.3075363337993622 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [279/500], Loss: 0.3068711459636688 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [280/500], Loss: 0.3062094449996948 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [281/500], Loss: 0.3055511713027954 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [282/500], Loss: 0.30489638447761536 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [283/500], Loss: 0.3042449355125427 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [284/500], Loss: 0.3035969138145447 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [285/500], Loss: 0.30295220017433167 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [286/500], Loss: 0.3023107945919037 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [287/500], Loss: 0.30167272686958313 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [288/500], Loss: 0.30103790760040283 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [289/500], Loss: 0.3004063069820404 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [290/500], Loss: 0.299778014421463 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [291/500], Loss: 0.2991528809070587 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [292/500], Loss: 0.2985309660434723 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [293/500], Loss: 0.2979121804237366 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [294/500], Loss: 0.29729655385017395 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [295/500], Loss: 0.29668399691581726 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [296/500], Loss: 0.2960745692253113 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [297/500], Loss: 0.29546821117401123 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [298/500], Loss: 0.2948648929595947 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [299/500], Loss: 0.29426461458206177 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [300/500], Loss: 0.2936672866344452 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [301/500], Loss: 0.29307299852371216 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [302/500], Loss: 0.29248160123825073 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [303/500], Loss: 0.29189321398735046 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [304/500], Loss: 0.2913077473640442 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [305/500], Loss: 0.2907251715660095 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [306/500], Loss: 0.2901454269886017 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [307/500], Loss: 0.28956860303878784 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [308/500], Loss: 0.28899458050727844 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [309/500], Loss: 0.2884233593940735 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [310/500], Loss: 0.28785496950149536 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [311/500], Loss: 0.2872893512248993 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [312/500], Loss: 0.2867264747619629 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [313/500], Loss: 0.28616639971733093 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [314/500], Loss: 0.2856089472770691 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [315/500], Loss: 0.2850542664527893 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [316/500], Loss: 0.28450220823287964 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [317/500], Loss: 0.28395286202430725 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [318/500], Loss: 0.283406138420105 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [319/500], Loss: 0.2828620672225952 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [320/500], Loss: 0.2823205590248108 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [321/500], Loss: 0.2817816734313965 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [322/500], Loss: 0.28124532103538513 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [323/500], Loss: 0.2807115614414215 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [324/500], Loss: 0.28018030524253845 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [325/500], Loss: 0.2796515226364136 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [326/500], Loss: 0.2791253328323364 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [327/500], Loss: 0.27860158681869507 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [328/500], Loss: 0.2780803143978119 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [329/500], Loss: 0.2775614857673645 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [330/500], Loss: 0.2770450711250305 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [331/500], Loss: 0.27653107047080994 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [332/500], Loss: 0.27601951360702515 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [333/500], Loss: 0.275510311126709 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [334/500], Loss: 0.27500349283218384 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [335/500], Loss: 0.27449899911880493 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [336/500], Loss: 0.27399688959121704 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [337/500], Loss: 0.2734970152378082 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [338/500], Loss: 0.27299949526786804 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [339/500], Loss: 0.2725042402744293 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [340/500], Loss: 0.27201130986213684 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [341/500], Loss: 0.27152055501937866 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [342/500], Loss: 0.27103209495544434 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [343/500], Loss: 0.2705458104610443 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [344/500], Loss: 0.27006176114082336 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [345/500], Loss: 0.2695799469947815 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [346/500], Loss: 0.2691003084182739 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [347/500], Loss: 0.26862281560897827 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [348/500], Loss: 0.26814740896224976 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [349/500], Loss: 0.26767420768737793 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [350/500], Loss: 0.26720309257507324 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [351/500], Loss: 0.26673412322998047 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [352/500], Loss: 0.26626724004745483 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [353/500], Loss: 0.2658024728298187 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [354/500], Loss: 0.265339732170105 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [355/500], Loss: 0.2648790180683136 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [356/500], Loss: 0.26442036032676697 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [357/500], Loss: 0.2639637589454651 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [358/500], Loss: 0.2635091543197632 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [359/500], Loss: 0.26305651664733887 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [360/500], Loss: 0.2626059353351593 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [361/500], Loss: 0.26215726137161255 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [362/500], Loss: 0.26171058416366577 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [363/500], Loss: 0.2612658441066742 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [364/500], Loss: 0.2608230412006378 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [365/500], Loss: 0.26038217544555664 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [366/500], Loss: 0.2599432170391083 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [367/500], Loss: 0.2595061659812927 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [368/500], Loss: 0.2590709626674652 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [369/500], Loss: 0.2586376667022705 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [370/500], Loss: 0.25820618867874146 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [371/500], Loss: 0.2577765882015228 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [372/500], Loss: 0.25734877586364746 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [373/500], Loss: 0.2569228708744049 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [374/500], Loss: 0.2564987540245056 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [375/500], Loss: 0.2560764253139496 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [376/500], Loss: 0.2556558847427368 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [377/500], Loss: 0.2552371621131897 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [378/500], Loss: 0.2548201382160187 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [379/500], Loss: 0.2544049322605133 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [380/500], Loss: 0.25399142503738403 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [381/500], Loss: 0.25357967615127563 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [382/500], Loss: 0.2531696856021881 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [383/500], Loss: 0.2527613341808319 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [384/500], Loss: 0.2523546814918518 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [385/500], Loss: 0.25194981694221497 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [386/500], Loss: 0.25154656171798706 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [387/500], Loss: 0.25114503502845764 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [388/500], Loss: 0.25074508786201477 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [389/500], Loss: 0.2503468096256256 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [390/500], Loss: 0.24995020031929016 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [391/500], Loss: 0.24955520033836365 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [392/500], Loss: 0.24916183948516846 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [393/500], Loss: 0.248770073056221 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [394/500], Loss: 0.2483798712491989 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [395/500], Loss: 0.24799127876758575 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [396/500], Loss: 0.24760432541370392 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [397/500], Loss: 0.24721884727478027 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [398/500], Loss: 0.24683499336242676 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [399/500], Loss: 0.2464527189731598 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [400/500], Loss: 0.2460719347000122 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [401/500], Loss: 0.2456926852464676 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [402/500], Loss: 0.24531495571136475 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [403/500], Loss: 0.24493873119354248 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [404/500], Loss: 0.244564026594162 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [405/500], Loss: 0.2441907823085785 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [406/500], Loss: 0.24381904304027557 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [407/500], Loss: 0.24344882369041443 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [408/500], Loss: 0.2430799901485443 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [409/500], Loss: 0.24271269142627716 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [410/500], Loss: 0.24234679341316223 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [411/500], Loss: 0.2419823706150055 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [412/500], Loss: 0.24161934852600098 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [413/500], Loss: 0.24125775694847107 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [414/500], Loss: 0.24089758098125458 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [415/500], Loss: 0.2405388057231903 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [416/500], Loss: 0.24018147587776184 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [417/500], Loss: 0.23982545733451843 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [418/500], Loss: 0.23947091400623322 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [419/500], Loss: 0.23911766707897186 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [420/500], Loss: 0.23876579105854034 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [421/500], Loss: 0.23841531574726105 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [422/500], Loss: 0.2380661517381668 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [423/500], Loss: 0.2377183437347412 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [424/500], Loss: 0.23737187683582306 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [425/500], Loss: 0.23702673614025116 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [426/500], Loss: 0.2366829365491867 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [427/500], Loss: 0.23634037375450134 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [428/500], Loss: 0.23599913716316223 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [429/500], Loss: 0.23565921187400818 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [430/500], Loss: 0.2353205680847168 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [431/500], Loss: 0.23498323559761047 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [432/500], Loss: 0.23464715480804443 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [433/500], Loss: 0.23431234061717987 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [434/500], Loss: 0.23397879302501678 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [435/500], Loss: 0.2336464375257492 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [436/500], Loss: 0.2333153933286667 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [437/500], Loss: 0.23298554122447968 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [438/500], Loss: 0.23265695571899414 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [439/500], Loss: 0.2323295623064041 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [440/500], Loss: 0.23200342059135437 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [441/500], Loss: 0.23167844116687775 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [442/500], Loss: 0.23135466873645782 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [443/500], Loss: 0.231032133102417 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [444/500], Loss: 0.2307107150554657 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [445/500], Loss: 0.23039059340953827 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [446/500], Loss: 0.23007158935070038 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [447/500], Loss: 0.22975365817546844 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [448/500], Loss: 0.229436993598938 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [449/500], Loss: 0.22912144660949707 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [450/500], Loss: 0.22880709171295166 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [451/500], Loss: 0.2284938544034958 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [452/500], Loss: 0.22818176448345184 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [453/500], Loss: 0.22787074744701385 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [454/500], Loss: 0.22756090760231018 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [455/500], Loss: 0.22725220024585724 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [456/500], Loss: 0.22694453597068787 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [457/500], Loss: 0.22663798928260803 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [458/500], Loss: 0.22633254528045654 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [459/500], Loss: 0.22602824866771698 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [460/500], Loss: 0.22572501003742218 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [461/500], Loss: 0.22542281448841095 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [462/500], Loss: 0.22512173652648926 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [463/500], Loss: 0.22482165694236755 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [464/500], Loss: 0.22452270984649658 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [465/500], Loss: 0.22422483563423157 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [466/500], Loss: 0.22392794489860535 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [467/500], Loss: 0.22363214194774628 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [468/500], Loss: 0.22333736717700958 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [469/500], Loss: 0.22304363548755646 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [470/500], Loss: 0.2227509468793869 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [471/500], Loss: 0.22245924174785614 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [472/500], Loss: 0.22216860949993134 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [473/500], Loss: 0.22187888622283936 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [474/500], Loss: 0.22159028053283691 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [475/500], Loss: 0.2213025987148285 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [476/500], Loss: 0.22101593017578125 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [477/500], Loss: 0.2207302749156952 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [478/500], Loss: 0.2204456329345703 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [479/500], Loss: 0.22016188502311707 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [480/500], Loss: 0.219879150390625 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [481/500], Loss: 0.21959736943244934 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [482/500], Loss: 0.21931663155555725 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [483/500], Loss: 0.2190367877483368 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [484/500], Loss: 0.21875786781311035 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [485/500], Loss: 0.2184799611568451 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [486/500], Loss: 0.21820297837257385 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [487/500], Loss: 0.21792693436145782 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [488/500], Loss: 0.21765179932117462 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [489/500], Loss: 0.21737763285636902 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [490/500], Loss: 0.21710434556007385 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [491/500], Loss: 0.2168320119380951 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [492/500], Loss: 0.21656055748462677 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [493/500], Loss: 0.21629007160663605 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [494/500], Loss: 0.21602043509483337 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [495/500], Loss: 0.21575172245502472 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [496/500], Loss: 0.21548393368721008 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [497/500], Loss: 0.2152169644832611 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [498/500], Loss: 0.21495088934898376 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [499/500], Loss: 0.21468575298786163 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", + "Epoch [500/500], Loss: 0.21442148089408875 Training accuracy = 0.949999988079071 | Test accuracy = 0.8999999761581421\n", "Done!\n" ] } ], "source": [ - "rf.fit(train, labels_train)\n", + "# rf.fit(train, labels_train)\n", "\n", "# Hyperparameters\n", - "lr = 0.2\n", - "num_epochs = 200\n", + "lr = 0.05\n", + "num_epochs = 500\n", "\n", "optimizer = optim.Adam(model.parameters(), lr=lr)\n", "loss_fn = nn.BCELoss()\n", @@ -512,35 +774,35 @@ }, { "cell_type": "code", - "execution_count": 180, + "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.95" + "0.9" ] }, - "execution_count": 180, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "sklearn.metrics.accuracy_score(labels_test, rf.predict(test))\n", + "# sklearn.metrics.accuracy_score(labels_test, rf.predict(test))\n", "\n", "sklearn.metrics.accuracy_score(labels_test, model.predict(test))" ] }, { "cell_type": "code", - "execution_count": 231, + "execution_count": 80, "metadata": {}, "outputs": [], "source": [ "# Since we removed one class and on feature we need to\n", "# make some manual adjustments to the feature and target names \n", - "feature_names = iris.feature_names[1:]\n", + "feature_names = iris.feature_names[2:]\n", "target_names = iris.target_names[1:][::-1]\n", "\n", "explainer = lime.lime_tabular.LimeTabularExplainer(train.numpy(),\n", @@ -550,7 +812,7 @@ }, { "cell_type": "code", - "execution_count": 240, + "execution_count": 109, "metadata": {}, "outputs": [], "source": [ @@ -563,7 +825,7 @@ "# print(pred.shape)\n", "# print(pred)\n", "\n", - "i = 10\n", + "i_explained = np.random.randint(0, test.shape[0])\n", "apply_model_predict = lambda x: model.prediction_probabilities(torch.from_numpy(x).float()).numpy()\n", "\n", "# sample = test[:].numpy().reshape(-1,3)\n", @@ -571,13 +833,13 @@ "# print(pred.shape)\n", "# print(pred)\n", "\n", - "exp = explainer.explain_instance(test[i].numpy(), apply_model_predict, num_features=3)\n", + "exp = explainer.explain_instance(test[i_explained].numpy(), apply_model_predict, num_features=2)\n", "\n" ] }, { "cell_type": "code", - "execution_count": 241, + "execution_count": 110, "metadata": {}, "outputs": [ { @@ -37674,25 +37936,25 @@ "/***/ })\n", "/******/ ]);\n", "//# sourceMappingURL=bundle.js.map \n", - "
\n", + "
\n", " \n", " \n", " " @@ -37709,6 +37971,37253 @@ "exp.show_in_notebook(show_table=True, show_all=False)\n", "\n" ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(1, -0.2774650084768441), (0, 0.01695588039659934)]\n", + "[0.01695588039659934, -0.2774650084768441]\n" + ] + } + ], + "source": [ + "# Extract coefficients of LIME explanation\n", + "\n", + "coefficients_map = exp.local_exp[1]\n", + "print(coefficients_map)\n", + "\n", + "feature_index = [elem[0] for elem in coefficients_map]\n", + "LIME_coefficients = [coefficients_map[i][1] for i in feature_index]\n", + "print(LIME_coefficients)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Coefficients:\n", + "[[-0.36228552 -4.853373 ]]\n", + "Biases:\n", + "[9.872637]\n" + ] + } + ], + "source": [ + "# Extract coefficients of original model and create decision boundary\n", + "\n", + "coefficients = model.linear.weight.detach().numpy()\n", + "biases = model.linear.bias.detach().numpy()\n", + "\n", + "print(\"Coefficients:\")\n", + "print(coefficients)\n", + "print(\"Biases:\")\n", + "print(biases)\n", + "\n", + "decision_boundary = lambda x : (-biases)/coefficients[0][1] - x*coefficients[0][0]/coefficients[0][1]" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LIME_coefficients:\n", + "petal length (cm): 0.01695588039659934\n", + "petal width (cm): -0.2774650084768441\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + "
\n", + " \n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "x = np.linspace(0, 10, 100)\n", + "y = decision_boundary(x)\n", + "\n", + "features_test = test.numpy() if not isinstance(test, np.ndarray) else test\n", + "labels_test = labels_test.numpy() if not isinstance(labels_test, np.ndarray) else labels_test\n", + "\n", + "\n", + "\n", + "# Print LIME coefficients\n", + "print(\"LIME_coefficients:\")\n", + "for feature, coefficient in zip(feature_names, LIME_coefficients):\n", + " print(f\"{feature}: {coefficient}\")\n", + "\n", + "# Plotting the decision boundary and the test set\n", + "plt.plot(x, y)\n", + "ix0 = (labels_test == 0).ravel()\n", + "ix1 = (labels_test == 1).ravel()\n", + "plt.scatter(features_test[ix0,0], features_test[ix0, 1], c = 'b', label = target_names[0])\n", + "plt.scatter(features_test[ix1,0], features_test[ix1, 1], c = 'g', label = target_names[1])\n", + "point_explained = features_test[i_explained,:]\n", + "circle = plt.Circle(point_explained, 0.1, color='r', fill=False)\n", + "plt.gca().add_patch(circle)\n", + "plt.xlim(0, 10)\n", + "plt.ylim(0, 10)\n", + "plt.xlabel(feature_names[0])\n", + "plt.ylabel(feature_names[1])\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "# Display LIME explanation\n", + "exp.show_in_notebook(show_table=True, show_all=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": {