diff --git a/TD2_Deep_Learning.ipynb b/TD2_Deep_Learning.ipynb
index fd619c2f81b6afe4def754c6cee62d7ea98643f4..f1b0bc68bcd37be4dcc836f8c3b49c5831c04547 100644
--- a/TD2_Deep_Learning.ipynb
+++ b/TD2_Deep_Learning.ipynb
@@ -33,7 +33,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 2,
"id": "330a42f5",
"metadata": {},
"outputs": [
@@ -77,7 +77,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 3,
"id": "b1950f0a",
"metadata": {},
"outputs": [
@@ -85,34 +85,34 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "tensor([[ 1.0605, 0.4877, 1.3848, -0.5006, 0.0414, 0.2985, 0.1982, 0.5928,\n",
- " 0.4444, 0.3458],\n",
- " [-0.1176, -1.5951, -1.1404, 0.6262, -1.0974, -0.7637, 0.4559, 0.3599,\n",
- " 0.7854, 0.1764],\n",
- " [ 0.2529, 0.4114, 1.5469, 0.2975, -0.9362, -1.0877, 0.3287, 1.8137,\n",
- " 0.9959, -0.9416],\n",
- " [ 0.8527, -1.1763, 1.0453, 0.2173, 1.2337, -0.4122, -0.4660, 0.5123,\n",
- " -0.5657, 0.2373],\n",
- " [-0.5439, 0.9611, 0.8822, -1.0495, -0.8781, -0.9642, -0.9343, 0.6947,\n",
- " -0.1654, -0.6049],\n",
- " [-1.2154, 1.4457, 0.5532, 0.5507, 0.9646, 1.6836, -0.7217, -1.5023,\n",
- " -0.1566, -0.0864],\n",
- " [ 2.1407, 0.1364, 0.5218, 0.5398, 0.8079, 1.3318, 0.1576, 0.7597,\n",
- " 1.8427, -0.1918],\n",
- " [-0.4011, -2.5381, 0.6992, -0.8056, 0.1494, -0.8711, -1.0129, 0.4020,\n",
- " 0.1074, 1.4686],\n",
- " [-0.1769, 1.0718, 0.6540, -1.6458, 2.1758, 0.0703, 0.7089, 0.9214,\n",
- " 1.4192, -0.5498],\n",
- " [-1.4553, -0.0320, 0.4980, -0.9733, 0.4106, -0.4363, 1.5308, -0.8421,\n",
- " 0.0412, -2.0390],\n",
- " [ 0.1056, 0.6472, -0.1967, 0.9547, 0.8193, -1.4786, -0.5792, -0.1598,\n",
- " -0.1151, 0.0905],\n",
- " [ 0.6584, -2.2935, 0.0100, -1.8383, 2.2067, -0.1495, 0.0839, 2.0443,\n",
- " -1.7859, -0.5420],\n",
- " [ 0.4898, -0.1235, 0.4655, -0.9289, -0.8926, -2.5259, 0.4773, -0.2087,\n",
- " 2.2034, 0.1863],\n",
- " [-1.3225, 0.4705, -0.3551, -0.0710, -0.4115, -0.2432, -1.2216, 1.2093,\n",
- " -0.1206, 1.0373]])\n",
+ "tensor([[ 1.1553, -0.0581, 1.1725, -1.3031, -0.9055, -0.1700, 0.0107, 0.8367,\n",
+ " -0.2644, -0.6796],\n",
+ " [-2.6043, 0.7077, 0.0745, -1.3235, -0.4293, 0.8440, -0.5132, -1.5214,\n",
+ " -0.1487, -0.0486],\n",
+ " [-0.1820, -1.6224, -0.0613, 2.1617, -0.7577, -2.1852, -0.9381, -1.0193,\n",
+ " 0.9782, -0.2095],\n",
+ " [ 0.8775, -1.1787, 1.9619, -0.3757, -0.1272, -0.5044, 0.2797, -0.4006,\n",
+ " -0.0049, 1.3381],\n",
+ " [ 0.4456, 0.9702, -1.3660, 0.3815, 0.7726, 1.0481, -0.2187, 0.7539,\n",
+ " 0.8051, -0.3459],\n",
+ " [-0.0264, 0.4671, 1.0657, -1.4862, 0.1840, 0.5508, 2.1805, -2.2760,\n",
+ " 0.4900, 0.6758],\n",
+ " [ 0.5677, -0.6335, 0.4963, -0.6345, -1.3937, 0.0058, -1.0223, 0.1338,\n",
+ " 1.0379, -1.0666],\n",
+ " [-1.4058, 0.1023, 0.1497, -0.1281, 1.9152, 0.4198, 1.1238, 0.1918,\n",
+ " -1.6397, 0.3763],\n",
+ " [ 1.9420, -1.5595, -0.5459, -0.3907, -1.0549, 0.1562, 0.3648, 0.3389,\n",
+ " -0.3080, 1.5482],\n",
+ " [-0.3655, 0.7948, -0.2838, 1.1157, 0.8409, -0.4002, 0.4719, 0.4816,\n",
+ " 1.3133, 0.3462],\n",
+ " [-0.5456, 0.0077, -0.0711, -0.6751, -0.8400, -1.3242, 0.1949, 0.9132,\n",
+ " 0.2796, -0.8041],\n",
+ " [ 0.9780, -0.0140, 1.7110, 0.0161, -0.8130, 1.1785, 0.5801, -0.3049,\n",
+ " 1.1432, 1.3139],\n",
+ " [-0.6483, 0.3786, -0.2267, -0.3947, 0.2875, -1.3313, -1.6920, -0.7404,\n",
+ " 1.9661, 0.6756],\n",
+ " [-0.5101, 0.2471, -1.1457, -0.7987, -1.8677, -0.9626, 0.4831, -0.9440,\n",
+ " -0.0813, -1.0011]])\n",
"AlexNet(\n",
" (features): Sequential(\n",
" (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
@@ -297,7 +297,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 8,
"id": "317bf070",
"metadata": {},
"outputs": [
@@ -361,7 +361,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 9,
"id": "4b53f229",
"metadata": {},
"outputs": [
@@ -369,49 +369,50 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Epoch: 0 \tTraining Loss: 42.825791 \tValidation Loss: 38.047178\n",
- "Validation loss decreased (inf --> 38.047178). Saving model ...\n",
- "Epoch: 1 \tTraining Loss: 34.432099 \tValidation Loss: 32.259117\n",
- "Validation loss decreased (38.047178 --> 32.259117). Saving model ...\n",
- "Epoch: 2 \tTraining Loss: 30.593193 \tValidation Loss: 29.343884\n",
- "Validation loss decreased (32.259117 --> 29.343884). Saving model ...\n",
- "Epoch: 3 \tTraining Loss: 28.517643 \tValidation Loss: 28.747610\n",
- "Validation loss decreased (29.343884 --> 28.747610). Saving model ...\n",
- "Epoch: 4 \tTraining Loss: 27.061891 \tValidation Loss: 26.789136\n",
- "Validation loss decreased (28.747610 --> 26.789136). Saving model ...\n",
- "Epoch: 5 \tTraining Loss: 25.740426 \tValidation Loss: 25.652807\n",
- "Validation loss decreased (26.789136 --> 25.652807). Saving model ...\n",
- "Epoch: 6 \tTraining Loss: 24.557613 \tValidation Loss: 24.589273\n",
- "Validation loss decreased (25.652807 --> 24.589273). Saving model ...\n",
- "Epoch: 7 \tTraining Loss: 23.515031 \tValidation Loss: 25.103045\n",
- "Epoch: 8 \tTraining Loss: 22.581234 \tValidation Loss: 24.104360\n",
- "Validation loss decreased (24.589273 --> 24.104360). Saving model ...\n",
- "Epoch: 9 \tTraining Loss: 21.738190 \tValidation Loss: 23.372788\n",
- "Validation loss decreased (24.104360 --> 23.372788). Saving model ...\n",
- "Epoch: 10 \tTraining Loss: 20.909772 \tValidation Loss: 23.101869\n",
- "Validation loss decreased (23.372788 --> 23.101869). Saving model ...\n",
- "Epoch: 11 \tTraining Loss: 20.140882 \tValidation Loss: 22.718813\n",
- "Validation loss decreased (23.101869 --> 22.718813). Saving model ...\n",
- "Epoch: 12 \tTraining Loss: 19.447754 \tValidation Loss: 22.743659\n",
- "Epoch: 13 \tTraining Loss: 18.741209 \tValidation Loss: 21.960046\n",
- "Validation loss decreased (22.718813 --> 21.960046). Saving model ...\n",
- "Epoch: 14 \tTraining Loss: 18.086207 \tValidation Loss: 23.175453\n",
- "Epoch: 15 \tTraining Loss: 17.524023 \tValidation Loss: 22.036695\n",
- "Epoch: 16 \tTraining Loss: 16.883423 \tValidation Loss: 21.853538\n",
- "Validation loss decreased (21.960046 --> 21.853538). Saving model ...\n",
- "Epoch: 17 \tTraining Loss: 16.315123 \tValidation Loss: 22.812885\n",
- "Epoch: 18 \tTraining Loss: 15.772240 \tValidation Loss: 22.325363\n",
- "Epoch: 19 \tTraining Loss: 15.269639 \tValidation Loss: 22.520727\n",
- "Epoch: 20 \tTraining Loss: 14.745204 \tValidation Loss: 22.754284\n",
- "Epoch: 21 \tTraining Loss: 14.261328 \tValidation Loss: 22.611392\n",
- "Epoch: 22 \tTraining Loss: 13.771852 \tValidation Loss: 24.175723\n",
- "Epoch: 23 \tTraining Loss: 13.320821 \tValidation Loss: 23.895067\n",
- "Epoch: 24 \tTraining Loss: 12.871154 \tValidation Loss: 24.741432\n",
- "Epoch: 25 \tTraining Loss: 12.380433 \tValidation Loss: 24.762289\n",
- "Epoch: 26 \tTraining Loss: 11.955164 \tValidation Loss: 24.945322\n",
- "Epoch: 27 \tTraining Loss: 11.516984 \tValidation Loss: 26.506488\n",
- "Epoch: 28 \tTraining Loss: 11.121875 \tValidation Loss: 26.109028\n",
- "Epoch: 29 \tTraining Loss: 10.736281 \tValidation Loss: 26.803389\n"
+ "Epoch: 0 \tTraining Loss: 43.868549 \tValidation Loss: 39.570542\n",
+ "Validation loss decreased (inf --> 39.570542). Saving model ...\n",
+ "Epoch: 1 \tTraining Loss: 36.195879 \tValidation Loss: 32.029579\n",
+ "Validation loss decreased (39.570542 --> 32.029579). Saving model ...\n",
+ "Epoch: 2 \tTraining Loss: 30.961746 \tValidation Loss: 29.930227\n",
+ "Validation loss decreased (32.029579 --> 29.930227). Saving model ...\n",
+ "Epoch: 3 \tTraining Loss: 28.380143 \tValidation Loss: 27.383593\n",
+ "Validation loss decreased (29.930227 --> 27.383593). Saving model ...\n",
+ "Epoch: 4 \tTraining Loss: 26.609379 \tValidation Loss: 25.630681\n",
+ "Validation loss decreased (27.383593 --> 25.630681). Saving model ...\n",
+ "Epoch: 5 \tTraining Loss: 25.228773 \tValidation Loss: 24.972364\n",
+ "Validation loss decreased (25.630681 --> 24.972364). Saving model ...\n",
+ "Epoch: 6 \tTraining Loss: 24.106138 \tValidation Loss: 24.264588\n",
+ "Validation loss decreased (24.972364 --> 24.264588). Saving model ...\n",
+ "Epoch: 7 \tTraining Loss: 23.138273 \tValidation Loss: 23.708663\n",
+ "Validation loss decreased (24.264588 --> 23.708663). Saving model ...\n",
+ "Epoch: 8 \tTraining Loss: 22.270021 \tValidation Loss: 23.530803\n",
+ "Validation loss decreased (23.708663 --> 23.530803). Saving model ...\n",
+ "Epoch: 9 \tTraining Loss: 21.354291 \tValidation Loss: 22.471311\n",
+ "Validation loss decreased (23.530803 --> 22.471311). Saving model ...\n",
+ "Epoch: 10 \tTraining Loss: 20.613555 \tValidation Loss: 22.069605\n",
+ "Validation loss decreased (22.471311 --> 22.069605). Saving model ...\n",
+ "Epoch: 11 \tTraining Loss: 19.884603 \tValidation Loss: 22.567473\n",
+ "Epoch: 12 \tTraining Loss: 19.194082 \tValidation Loss: 22.128961\n",
+ "Epoch: 13 \tTraining Loss: 18.520741 \tValidation Loss: 21.539472\n",
+ "Validation loss decreased (22.069605 --> 21.539472). Saving model ...\n",
+ "Epoch: 14 \tTraining Loss: 17.920634 \tValidation Loss: 21.376756\n",
+ "Validation loss decreased (21.539472 --> 21.376756). Saving model ...\n",
+ "Epoch: 15 \tTraining Loss: 17.302807 \tValidation Loss: 21.674213\n",
+ "Epoch: 16 \tTraining Loss: 16.675798 \tValidation Loss: 21.587345\n",
+ "Epoch: 17 \tTraining Loss: 16.145350 \tValidation Loss: 21.348169\n",
+ "Validation loss decreased (21.376756 --> 21.348169). Saving model ...\n",
+ "Epoch: 18 \tTraining Loss: 15.571500 \tValidation Loss: 21.862647\n",
+ "Epoch: 19 \tTraining Loss: 15.024691 \tValidation Loss: 21.804351\n",
+ "Epoch: 20 \tTraining Loss: 14.487721 \tValidation Loss: 21.531065\n",
+ "Epoch: 21 \tTraining Loss: 14.034568 \tValidation Loss: 21.796746\n",
+ "Epoch: 22 \tTraining Loss: 13.519650 \tValidation Loss: 22.408303\n",
+ "Epoch: 23 \tTraining Loss: 13.066309 \tValidation Loss: 23.018654\n",
+ "Epoch: 24 \tTraining Loss: 12.629155 \tValidation Loss: 22.899327\n",
+ "Epoch: 25 \tTraining Loss: 12.105263 \tValidation Loss: 24.197905\n",
+ "Epoch: 26 \tTraining Loss: 11.618184 \tValidation Loss: 24.667808\n",
+ "Epoch: 27 \tTraining Loss: 11.245077 \tValidation Loss: 24.160339\n",
+ "Epoch: 28 \tTraining Loss: 10.786976 \tValidation Loss: 24.304690\n",
+ "Epoch: 29 \tTraining Loss: 10.413645 \tValidation Loss: 25.813572\n"
]
}
],
@@ -495,13 +496,13 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 10,
"id": "d39df818",
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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iIiIDwzE0REREZPQYaIiIiMjoMdAQERGR0WOgISIiIqPHQENERERGj4GGiIiIjF6bn4dGrVYjOzsbtra29zxFOhEREYlDEASUlpbCw8NDs+Dv7bT5QJOdnW0QC9URERHRvcvIyNBMqnk7BhNolixZgnnz5uH555/XrE3Sr18/rZkpAeDZZ5/F8uXL7/q4DYu9ZWRkwM7OTm/1EhERUcspKSmBl5eX1qKtt2MQgebo0aP48ssvG13S/plnnsFbb72leW5lZXVPx264zGRnZ8dAQ0REZGTudriI6IOCy8rKMGHCBHz11VdwcHC4abuVlRUUCoXmwVBCREREukQPNLNmzcKwYcMwcODARrevXr0azs7OCA0Nxbx58zQrvRIRERE1EPWS07p163D8+HEcPXq00e3jx4+Hj48PPDw8cOrUKbzyyitISUnBxo0bb3lMlUoFlUqleV5SUqL3uomIiMiwiBZoMjIy8Pzzz+OPP/6AhYVFo/tMmzZN8+euXbvC3d0d0dHRSEtLQ0BAQKOvWbx4MRYtWtQiNRMREZFhkgiCIIjxxps3b8bo0aNhYmKiaaurq4NEIoFUKoVKpdLaBgDl5eWwsbHBb7/9hsGDBzd63MZ6aLy8vKBUKjn+hoiIyEiUlJRALpff9fe3aD000dHRSE5O1mqbPHkygoOD8corr9wUZgAgKSkJAODu7n7L48pkMshkMr3WSkRERIZNtEBja2uL0NBQrTZra2s4OTkhNDQUaWlpWLNmDYYOHQonJyecOnUKc+fORZ8+fRq9vZuIiIjaL4OYh6Yx5ubm2L17Nz766COUl5fDy8sLsbGxeOONN8QujYiIiAyMaGNoWsu9XoMjIiIi8d3r97fo89AQERERNRcDDRERERk9BhoiIiIyegw0TaRWC0jNL0NRmerOOxMREVGLYqBpollrjmPgh/HYfipH7FKIiIjaPQaaJurkZgsAOJWpFLkSIiIiYqBpojBPOQAgOatY3EKIiIiIgaapunaoDzSp+WWoqK4VuRoiIqL2jYGmiVztLOBmJ4NaAM5ml4hdDhERUbvGQNMMXTvYA+A4GiIiIrEx0DRDwzia01kMNERERGJioGmGhnE0pxhoiIiIRMVA0wyh1wNNWkEZylUcGExERCQWBppmcLGVwUNuAUEAznBgMBERkWgYaJqpoZfmVGaxuIUQERG1Yww0zcSBwUREROJjoGmmrp72ADgwmIiISEwMNM3UcKfTxYJylFbViFwNERFR+8RA00yO1uboYG8JgAODiYiIxMJAowcNvTTJnDGYiIhIFAw0etDVkxPsERERiYmBRg94pxMREZG4GGj0INSjPtCkF5ZDWcmBwURERK2NgUYPHKzN4eV4fWAwe2mIiIhaHQONnoR1sAcAJDPQEBERtToGGj0J5crbREREomGg0ZOGgcG8dZuIiKj1MdDoScPA4CtXK6Cs4MBgIiKi1sRAoydyKzP4OFkB4DgaIiKi1sZAo0ddNeNoisUthIiIqJ1hoNEjTrBHREQkDgYaPdLc6cSBwURERK2KgUaPGgJN5rVKXCuvFrkaIiKi9oOBRo/sLMzg72wNgAODiYiIWhMDjZ419NIw0BAREbUeBho9axgYfCqzWNxCiIiI2hEGGj1ruHX7dFaJyJUQERG1Hww0etalgxwSCZBVXInCMpXY5RAREbULDDR6ZiMz5cBgIiKiVmYwgWbJkiWQSCSYM2eOpq2qqgqzZs2Ck5MTbGxsEBsbi7y8PPGKvEuay06cj4aIiKhVGESgOXr0KL788kuEhYVptc+dOxfbtm3D+vXrER8fj+zsbIwZM0akKu9eV097AMAp9tAQERG1CtEDTVlZGSZMmICvvvoKDg4OmnalUolvvvkGH374IQYMGICIiAisXLkSf/31Fw4dOiRixXfWcKdTMntoiIiIWoXogWbWrFkYNmwYBg4cqNWemJiImpoarfbg4GB4e3sjISGhtcu8JyHudpBIgNySKuSXVoldDhERUZtnKuabr1u3DsePH8fRo0dv2pabmwtzc3PY29trtbu5uSE3N/eWx1SpVFCp/rm7qKSk9W+ftpaZItDFBhfyy3A6S4kBwRatXgMREVF7IloPTUZGBp5//nmsXr0aFhb6+8JfvHgx5HK55uHl5aW3Y9+LrprLTpyPhoiIqKWJFmgSExORn5+P+++/H6ampjA1NUV8fDw++eQTmJqaws3NDdXV1SguLtZ6XV5eHhQKxS2PO2/ePCiVSs0jIyOjhX+SxnXVLIFQLMr7ExERtSeiXXKKjo5GcnKyVtvkyZMRHByMV155BV5eXjAzM8OePXsQGxsLAEhJScGVK1cQFRV1y+PKZDLIZLIWrf1u/LMEAgcGExERtTTRAo2trS1CQ0O12qytreHk5KRpnzp1Kl544QU4OjrCzs4Ozz33HKKiotCrVy8xSr4nIe5ySCVAfqkKeSVVcLPjOBoiIqKWIuqg4DtZunQppFIpYmNjoVKpMHjwYHzxxRdil3VXLM1N0NHVFil5pUjOVMIthIGGiIiopUgEQRDELqIllZSUQC6XQ6lUws7OrlXf+6X1J7EhMRP/ju6IFx7u1KrvTUREZMzu9ftb9Hlo2rKGcTSnOWMwERFRi2KgaUGhHf4ZGNzGO8KIiIhExUDTgkLc7WAilaCwTIXcEs4YTERE1FIYaFqQhZkJOrnZAuC6TkRERC2JgaaFde1QP5ApmeNoiIiIWgwDTQvr6mkPgBPsERERtSQGmhYW1uGfO504MJiIiKhlMNC0sCCFLUylEhSVVyNbyYHBRERELYGBpoVZmJkgSNEwMLhY3GKIiIjaKAaaVvDPytscR0NERNQSGGhaQVeuvE1ERNSiGGhaQVgHewD1PTQcGExERKR/DDStoJPCBmYmEhRX1CDzWqXY5RAREbU5DDStQGZqgmAFJ9gjIiJqKQw0rYTjaIiIiFoOA00r6XrDBHtERESkXww0raQh0JzKLObAYCIiIj1joGklndxsYW4qRUlVLa5crRC7HCIiojaFgaaVmJtK0blhxmBediIiItIrBppW1DAwOJkDg4mIiPSKgaYVNUywxzudiIiI9IuBphWFNtzplK2EWs2BwURERPrCQNOKOrrZQGYqRWlVLS5zYDAREZHeMNC0IjMTKUI86mcMPpVZLG4xREREbQgDTSvjBHtERET6x0DTyv6ZYI+BhoiISF8YaFpZmKc9AOBMdgkHBhMREekJA00rC3CxhoWZFGWqWqQXlYtdDhERUZvAQNPKTE2k6OLBCfaIiIj0iYFGBA3jaLgEAhERkX4w0IhAE2jYQ0NERKQXDDQiCPP8Z8bg2jq1yNUQEREZPwYaEfi72EBuaYaK6jqcyCgWuxwiIiKjx0AjAhOpBP2CXAAAe87li1wNERGR8WOgEcmAYFcAQNx5BhoiIqLmYqARSd9OLpBKgJS8UmRwoUoiIqJmYaARib2VObr7OAIA4lLYS0NERNQcDDQiGtC5/rITx9EQERE1DwONiKKvj6NJuFiEiupakashIiIyXgw0Igp0tYGngyWqa9U4mFokdjlERERGS9RAs2zZMoSFhcHOzg52dnaIiorCzp07Ndv79esHiUSi9Zg+fbqIFeuXRCLR9NLsPZ8ncjVERETGS9RA4+npiSVLliAxMRHHjh3DgAEDMHLkSJw5c0azzzPPPIOcnBzN4/333xexYv0b0NkNALD3fD4EQRC5GiIiIuNkKuabDx8+XOv5u+++i2XLluHQoUPo0qULAMDKygoKhUKM8lpFpJ8jrMxNkFeiwpnsEoReX+eJiIiI7p7BjKGpq6vDunXrUF5ejqioKE376tWr4ezsjNDQUMybNw8VFbefs0WlUqGkpETrYcgszEzQO9AZQH0vDREREd070QNNcnIybGxsIJPJMH36dGzatAkhISEAgPHjx+PHH39EXFwc5s2bhx9++AFPPvnkbY+3ePFiyOVyzcPLy6s1foxmaRhHs4eBhoiIqEkkgsgDN6qrq3HlyhUolUps2LABX3/9NeLj4zWh5kZ79+5FdHQ0UlNTERAQ0OjxVCoVVCqV5nlJSQm8vLygVCphZ2fXYj9Hc+SVVCHyv3sgkQBHXhsIF1uZ2CURERGJqqSkBHK5/K6/v0XvoTE3N0dgYCAiIiKwePFihIeH4+OPP25038jISABAamrqLY8nk8k0d001PAydm50FQjvYQRCAfZw1mIiI6J6JHmh0qdVqrR6WGyUlJQEA3N3dW7Gi1jEg+J+7nYiIiOjeiHqX07x58xATEwNvb2+UlpZizZo12LdvH3bt2oW0tDSsWbMGQ4cOhZOTE06dOoW5c+eiT58+CAsLE7PsFhEd7IpP9lzA/guFqK5Vw9zU4LImERGRwRI10OTn5+Opp55CTk4O5HI5wsLCsGvXLjz88MPIyMjA7t278dFHH6G8vBxeXl6IjY3FG2+8IWbJLaZrBzmcbWQoLFPh6KWrmjufiIiI6M5EDTTffPPNLbd5eXkhPj6+FasRl1QqQf8gF6xPzMSec/kMNERERPeA1zUMSHRnLoNARETUFAw0BuTBji4wM5HgUlEFLhaUiV0OERGR0WCgMSA2MlP08ncCwLudiIiI7gUDjYHpH3R91uBzDDRERER3i4HGwDSMozl66SpKqmpEroaIiMg4MNAYGB8nawS4WKNWLWD/34Vil0NERGQUGGgMUHTn+lmD9/BuJyIiorvCQGOAGsbR7EspQJ1a1LVDiYiIjAIDjQHq7usAWwtTXC2vRlJGsdjlEBERGTwGGgNkZiJF304uAIA43r5NRER0Rww0BmpA8PXbtxloiIiI7oiBxkD1C3KFRAKcyylBdnGl2OUQEREZNAYaA+VobY77vR0AAHEp7KUhIiK6HQYaA9Zw2WkvZw0mIiK6LQYaA9YQaA6kFqKyuk7kaoiIiAwXA40BC1bYwkNuAVWtGgkXOWswERHRrTDQGDCJRIIB19d24urbREREt8ZAY+Cig+uXQdh7Lh+CwFmDiYiIGsNAY+CiApxgYSZFtrIK53NLxS6HiIjIIDHQGDgLMxP0DnAGwMtOREREt8JAYwQ4joaIiOj2GGiMQMPt28evXMPV8mqRqyEiIjI8DDRGwF1uic7udhAEYB9nDSYiIroJA42RiA7mZSciIqJbYaAxEg3jaOL/LkBNnVrkaoiIiAwLA42RCPe0h6O1OUqranHs0jWxyyEiIjIoDDRGwkQqQb8gFwDA3vN5IldDRERkWBhojIhm1mCOoyEiItLCQGNEHurkDFOpBGkF5bhUWC52OURERAaDgcaI2FmYoYevIwD20hAREd2IgcbIRHPWYCIiopsw0BiZhlmDD6cXoUxVK3I1REREhoGBxsj4u9jAz9kaNXUCDlwoELscIiIig8BAY4Qaeml+OHQZgiCIXA0REZH4GGiM0MQoX5ibSnEwtQg7T+eKXQ4REZHoGGiMkLeTFab3DQAAvL39LMo5loaIiNo5BhojNbNfADwdLJGjrMJncalil0NERCQqBhojZWFmggXDuwAAvt5/EWkFZSJXREREJB4GGiM2sLMrBgS7oqZOwMKtZzhAmIiI2i1RA82yZcsQFhYGOzs72NnZISoqCjt37tRsr6qqwqxZs+Dk5AQbGxvExsYiL48LMzaQSCRYMDwE5qZS7L9QyAHCRETUbokaaDw9PbFkyRIkJibi2LFjGDBgAEaOHIkzZ84AAObOnYtt27Zh/fr1iI+PR3Z2NsaMGSNmyQbHx8laa4BwRTUHCBMRUfsjEQzsOoWjoyM++OADjB07Fi4uLlizZg3Gjh0LADh//jw6d+6MhIQE9OrV666OV1JSArlcDqVSCTs7u5YsXTRVNXUY+GE8Mq9VYka/ALwyJFjskoiIiJrlXr+/DWYMTV1dHdatW4fy8nJERUUhMTERNTU1GDhwoGaf4OBgeHt7IyEh4ZbHUalUKCkp0Xq0dRwgTERE7Z3ogSY5ORk2NjaQyWSYPn06Nm3ahJCQEOTm5sLc3Bz29vZa+7u5uSE399ZjRRYvXgy5XK55eHl5tfBPYBgGdnZF/yAXDhAmIqJ2SfRAExQUhKSkJBw+fBgzZszAxIkTcfbs2SYfb968eVAqlZpHRkaGHqs1XBKJBAtHdOEAYSIiapdEDzTm5uYIDAxEREQEFi9ejPDwcHz88cdQKBSorq5GcXGx1v55eXlQKBS3PJ5MJtPcNdXwaC98nKwxvY8/AA4QJiKi9kX0QKNLrVZDpVIhIiICZmZm2LNnj2ZbSkoKrly5gqioKBErNGwz+gVqZhD+dC9nECYiovbBVMw3nzdvHmJiYuDt7Y3S0lKsWbMG+/btw65duyCXyzF16lS88MILcHR0hJ2dHZ577jlERUXd9R1O7ZGluQnmPxKCaT8k4uv9FzE2whMBLjZil0VERNSiRA00+fn5eOqpp5CTkwO5XI6wsDDs2rULDz/8MABg6dKlkEqliI2NhUqlwuDBg/HFF1+IWbJReDjEDf2DXBCXUoCFW8/g+yk9IZFIxC6LiIioxRjcPDT61h7moWnMpcJyDFr6J6rr1Fg24X7EdHUXuyQiIqK7ZrTz0JB++TpbY3pfDhAmIqL2gYGmDZvRLxAd7C2RrazCZxwgTEREbRgDTRtmaW6CBcNDAABfcQZhIiJqwxho2riHQ9zQjzMIExFRG8dA08ZJJBIsHN4F5ib1Mwj/xhmEiYioDWKgaQd8na3xLAcIExFRG8ZA007M5ABhIiJqwxho2glLcxPMv2GA8EUOECYiojaEgaYdGXTDAOEFHCBMRERtCANNO6I7QPjjPRfELomIiEgvmhRoMjIykJmZqXl+5MgRzJkzBytWrNBbYdQyfJ2tsXBEFwDAR7svYP2xDJErIiIiar4mBZrx48cjLi4OAJCbm4uHH34YR44cweuvv4633npLrwWS/o2P9MaMfgEAgHkbk3HgQqHIFRERETVPkwLN6dOn0bNnTwDAzz//jNDQUPz1119YvXo1Vq1apc/6qIX8Z1AQRoR7oFYtYPqPiTiXUyJ2SURERE3WpEBTU1MDmUwGANi9ezdGjBgBAAgODkZOTo7+qqMWI5VK8MGjYYj0c0SZqhaTVx5FjrJS7LKIiIiapEmBpkuXLli+fDn279+PP/74A0OGDAEAZGdnw8nJSa8FUsuRmZpgxb+6I9DVBrklVZi88ihKq2rELouIiOieNSnQvPfee/jyyy/Rr18/PPHEEwgPDwcAbN26VXMpioyD3MoMKyf1gLONDOdzSzFz9XHU1KnFLouIiOieSIQmTkZSV1eHkpISODg4aNouXboEKysruLq66q3A5iopKYFcLodSqYSdnZ3Y5Ris5EwlHvsyAZU1dXg0whPvjw2DRCIRuywiImqn7vX7u0k9NJWVlVCpVJowc/nyZXz00UdISUkxqDBDd6+rpxyfjb8PUgmwPjETn3J5BCIiMiJNCjQjR47E999/DwAoLi5GZGQk/ve//2HUqFFYtmyZXguk1hPd2Q1vjQwFAHz4x9/YkJh5h1cQEREZhiYFmuPHj+Ohhx4CAGzYsAFubm64fPkyvv/+e3zyySd6LZBa15O9fDC9b/0cNa/+cgoHUzlHDRERGb4mBZqKigrY2toCAH7//XeMGTMGUqkUvXr1wuXLl/VaILW+lwcHYXjDHDU/JOJ8LueoISIiw9akQBMYGIjNmzcjIyMDu3btwqBBgwAA+fn5HHjbBkilEvzfo2Ho6eeI0utz1OQqq8Qui4iI6JaaFGjmz5+Pl156Cb6+vujZsyeioqIA1PfW3HfffXotkMRRP0dNBAJcrJGjrMLkVUdRpqoVuywiIqJGNfm27dzcXOTk5CA8PBxSaX0uOnLkCOzs7BAcHKzXIpuDt203T8bVCoz+4i8UlqnQp5MLvpnYHWYmXKSdiIha1r1+fzc50DRoWHXb09OzOYdpMQw0zXcqsxiPf3kIlTV1eLy7F5bEduUcNURE1KJaZR4atVqNt956C3K5HD4+PvDx8YG9vT3efvttqNWcZbatCfO0x6dP1M9R89OxDHzGOWqIiMjANCnQvP766/jss8+wZMkSnDhxAidOnMB///tffPrpp3jzzTf1XSMZgIEhblg0ogsA4H9//I11R66IXBEREdE/mnTJycPDA8uXL9esst1gy5YtmDlzJrKysvRWYHPxkpN+Ld55Dl/GXwQA/GdwEGb2C+DlJyIi0rtWueR09erVRgf+BgcH4+rVq005JBmJV4cEaybe+2BXCuZvOYM6dbOGYRERETVbkwJNeHg4Pvvss5vaP/vsM4SFhTW7KDJcEokEr8YEY+HwEEgkwA+HLmPGj4moqqkTuzQiImrHmnTJKT4+HsOGDYO3t7dmDpqEhARkZGRgx44dmmURDAEvObWcHck5mPNTEqpr1YjwccA3E7vD3spc7LKIiKgNaJVLTn379sXff/+N0aNHo7i4GMXFxRgzZgzOnDmDH374oSmHJCM0tKs7fpjSE3YWpki8fA2xy/5C5rUKscsiIqJ2qNnz0Nzo5MmTuP/++1FXZziXH9hD0/L+zivFxG+PIEdZBVdbGVZN7okQD55rIiJqulbpoSG6USc3W2yc+QCC3GyRX6rCY18m4C+u0k1ERK2IgYb0wl1uiZ+nRyHSzxFlqlpMXHkEW5IM5/Z9IiJq2xhoSG/klmb4bkpPDOvqjpo6Ac+vS8JXf16EHq9qEhERNcr0XnYeM2bMbbcXFxc3pxZqAyzMTPDpE/fB1U6GlQcv4d0d55CjrMIbwzpDKuUEfERE1DLuKdDI5fI7bn/qqaeaVRAZP6lUgvmPhMBdboH/7jiPbw+mI6+0Cv97NBwWZiZil0dERG2QXu9yMkS8y0lcW5Ky8NL6k6ipExDp54gVT3WH3NJM7LKIiMjAGdVdTosXL0aPHj1ga2sLV1dXjBo1CikpKVr79OvXDxKJROsxffp0kSqmezWyWwesmtwTNjJTHE6/iseWJyBHWSl2WURE1MaIGmji4+Mxa9YsHDp0CH/88QdqamowaNAglJeXa+33zDPPICcnR/N4//33RaqYmqJ3oDN+erYXXGxlSMkrxZgv/kLiZa75RURE+nNPY2j07bffftN6vmrVKri6uiIxMRF9+vTRtFtZWUGhULR2eaRHXTzk2DjjAUxaeQRpBeV47MtDmBPdETP7B8KEg4WJiKiZDOq2baVSCQBwdHTUal+9ejWcnZ0RGhqKefPmoaLi1tPrq1QqlJSUaD3IMHg5WmHzrN4Y2c0DdWoB//vjb4z/6hAvQRERUbMZzKBgtVqNESNGoLi4GAcOHNC0r1ixAj4+PvDw8MCpU6fwyiuvoGfPnti4cWOjx1m4cCEWLVp0UzsHBRsOQRCw8XgW3txyGhXVdbC3MsN7sWEY3IW9cEREVO9eBwUbTKCZMWMGdu7ciQMHDsDT0/OW++3duxfR0dFITU1FQEDATdtVKhVUKpXmeUlJCby8vBhoDFB6YTn+vfYEkrPqe+b+1csHrw/rzFu7iYjIuO5yajB79mxs374dcXFxtw0zABAZGQkASE1NbXS7TCaDnZ2d1oMMk5+zNX6Z8QCm9fEHAPxw6DJGfnYQKbmlIldGRETGRtRAIwgCZs+ejU2bNmHv3r3w8/O742uSkpIAAO7u7i1cHbUGc1MpXhvaGd9N6Qlnm/q7oEZ8dgA/HLrMJROIiOiuiXrJaebMmVizZg22bNmCoKAgTbtcLoelpSXS0tKwZs0aDB06FE5OTjh16hTmzp0LT09PxMfH39V7cGI941FQqsJL608i/u8CAMCgEDe8FxsGB2tzkSsjIqLWZlRjaCSSxm/XXblyJSZNmoSMjAw8+eSTOH36NMrLy+Hl5YXRo0fjjTfeuOtwwkBjXNRqAd8eTMd7v51HTZ0AhZ0FPhrXDb38ncQujYiIWpFRBZrWwEBjnE5nKfHc2hNILyyHRAI81z8Q/47uCFMTgxj2RURELcwoBwUT6QrtIMf25x7EoxGeEATgk72peHzFIWRcvfUcRERE1H4x0JDBspaZ4oNHw/HxuG6wlZki8fI1DP1kPzadyOSAYSIi0sJAQwZvZLcO2PH8Q7jP2x6lVbWY+9NJPPXtEVwuKr/zi4mIqF1goCGj4OVohZ+fjcKLD3eCuakU+y8UYtDSP/F5XCqqa9Vil0dERCJjoCGjYWYixXPRHbFrTh/0DnSCqlaND3alYNgn+3H0ElfvJiJqzxhoyOj4OVvjx6mRWPp4OBytzXEhvwyPLk/AvI2noKyoEbs8IiISAQMNGSWJRILR93lizwt98Xh3LwDA2iMZiP5wH7YkZXHQMBFRO8NAQ0bNwdoc740Nw0/TeiHAxRqFZdV4fl0SBw0TEbUzDDTUJkT6O2HH8w9x0DARUTvFQENthszUhIOGiYjaKQYaanM4aJiIqP1hoKE26XaDhtceuYI6NQcNExG1JVycktqFwxeL8NqmZKQV1A8UDlbY4rWhndGnk4vIlRERUWO42rYOBhpqUF2rxo+HLuPjPRegrKy/9NQvyAWvD+2Mjm62IldHREQ3YqDRwUBDuoorqvHJnlR8n3AJtWoBJlIJxvXwwtyHO8HZRiZ2eUREBAaamzDQ0K2kF5Zjyc5z2HUmDwBgIzPFzP4BmNLbDxZmJiJXR0TUvjHQ6GCgoTs5fLEI7/x6DslZSgBAB3tLvBITjOFh7pBIJCJXR0TUPjHQ6GCgobuhVgvYnJSFD3alIEdZBQDo5mWPNx/pjAgfR5GrIyJqfxhodDDQ0L2orK7D1/svYll8Giqq6wAAw7q645UhwfB2shK5OiKi9oOBRgcDDTVFfkkVPvzjb/x8LANqATA3kWJSb1/M6h8IuaWZ2OUREbV5DDQ6GGioOc7llODdX8/hQGohAMDJ2hyvDAnG2AhPSKUcX0NE1FIYaHQw0FBzCYKAfSkFeOfXs5qJ+cK97PHWiC4I97IXtzgiojaKgUYHAw3pS3WtGt/9dQkf77mAMlUtJBLgsQgvvDwkCE6cv4aISK8YaHQw0JC+5ZdUYclv57HxeBYAwM7CFC883AlP9vKBqQmXRyMi0gcGGh0MNNRSEi9fxfwtZ3AmuwRA/fpQC0d0QS9/J5ErIyIyfgw0OhhoqCXVqQWsO3oFH+xKQXFF/fpQw8M98NrQYLjLLUWujojIeN3r9zf7x4mawUQqwYRIH8S92A9P9vKGRAJsO5mN6P/F44t9qVDV1oldIhFRu8AeGiI9Op2lxIKtZ5B4+RoAwM/ZGvOHh6B/kKvIlRERGRdectLBQEOtTRAEbDqRhcU7z6OgVAUAGNjZFW8+EgIfJ2uRqyMiMg4MNDoYaEgspVU1+HRvKr49kI5atQBzUyke7+6FaX384eXIZRSIiG6HgUYHAw2JLTW/FIu2ncX+C/WzDZtKJRjZrQNm9AtAoKuNyNURERkmBhodDDRkCARBwKGLV/F5XKpmGQWJBIgJVWBmv0CEdpCLXCERkWFhoNHBQEOGJimjGF/EpeL3s3matn5BLpjVPxA9fB1FrIyIyHAw0OhgoCFDlZJbimX7UrH1ZDbU1/8v7OnniFn9A9GnozMkEi5+SUTtFwONDgYaMnSXi8qxPP4ifknMRHWdGgDQtYMcs/oHYFCIgqt6E1G7xECjg4GGjEWusgpf7b+INYevoLKmfkK+QFcbzOwXgBHhHlwniojaFQYaHQw0ZGyKylRY9dclrPrrEkqragEAXo6WeLZPAMZGeMLCzETkComIWh4DjQ4GGjJWJVU1+PHQZXyzPx1F5dUAAGcbGaY+6Icne3nD1sJM5AqJiFoOA40OBhoydpXVdfjp6BV8tT8dWcWVAABbC1P8q5cPJvf2g4utTOQKiYj0z6gWp1y8eDF69OgBW1tbuLq6YtSoUUhJSdHap6qqCrNmzYKTkxNsbGwQGxuLvLy8WxyRqO2xNDfBpN5+2Peffvi/R8MR6GqD0qpafLEvDQ++txdvbj6NjKsVYpdJRCQqUXtohgwZgnHjxqFHjx6ora3Fa6+9htOnT+Ps2bOwtq5f82bGjBn49ddfsWrVKsjlcsyePRtSqRQHDx68q/dgDw21NWq1gD/O5eGLfWk4mVEMoH7V7+Fh7pjRLxBBCltxCyQi0gOjvuRUUFAAV1dXxMfHo0+fPlAqlXBxccGaNWswduxYAMD58+fRuXNnJCQkoFevXnc8JgMNtVWCICDhYhGW7UvTLKsAANHBrpjZPwARPpykj4iMl1FdctKlVCoBAI6O9X8RJyYmoqamBgMHDtTsExwcDG9vbyQkJIhSI5GhkEgkeCDAGT9MjcS22Q9iaFcFJBJgz/l8xC5LwGPLExCXkg8D+jcLEVGLMRW7gAZqtRpz5sxB7969ERoaCgDIzc2Fubk57O3ttfZ1c3NDbm5uo8dRqVRQqVSa5yUlJS1WM5Gh6OopxxcTInCxoAxfxl/ExhOZOHLpKo6svIrO7naY0S8AQ0MVnMuGiNosg/nbbdasWTh9+jTWrVvXrOMsXrwYcrlc8/Dy8tJThUSGz9/FBu+NDcP+lwfg6Qf9YGVugnM5Jfj32hPo+8E+fL3/IkqrasQuk4hI7wwi0MyePRvbt29HXFwcPD09Ne0KhQLV1dUoLi7W2j8vLw8KhaLRY82bNw9KpVLzyMjIaMnSiQySQm6BNx4JwV+vDsDcgZ3gaG2OrOJKvPPrOTyweC/e/fWs5hZwIqK2QNRBwYIg4LnnnsOmTZuwb98+dOzYUWt7w6DgtWvXIjY2FgCQkpKC4OBgDgomugdVNXXYeDwL3xy4iLSCcgD1d0bFhCrw9EP+6OZlL26BREQ6jOoup5kzZ2LNmjXYsmULgoKCNO1yuRyWlpYA6m/b3rFjB1atWgU7Ozs899xzAIC//vrrrt6DgYboH2q1gPi/C/D1gYs4mFqkae/h64CpD/rj4RA3mHAxTCIyAEYVaCSSxv/iXLlyJSZNmgSgfmK9F198EWvXroVKpcLgwYPxxRdf3PKSky4GGqLGnc0uwdcHLmLbyWzU1NX/NeDtaIUpvX3xaHcvWMsM5p4BImqHjCrQtAYGGqLbyyupwvcJl/DjoStQVtYPGLazMMUTkd6Y9IAv3OWWIldIRO0RA40OBhqiu1NRXYtfjmfh2wPpSC+sH2djKpXgkTB3TH3QH1095SJXSETtCQONDgYaonujVgvYez4fXx+4iEMXr2rae/g6YHJvPwwKceN8NkTU4hhodDDQEDVdcqYS3xy4iO2nclCrrv+rooO9JZ6K8sG4Ht6QW5mJXCERtVUMNDoYaIiaL6+kCj8euozVh6/gank1AMDSzASxER0w6QE/BLraiFwhEbU1DDQ6GGiI9Keqpg5bk7Lx7cF0nM8t1bT36eSCyb190bejC6S87ZuI9ICBRgcDDZH+Naz0vfLgJew+l4eGv0X8Xawx+QFfjLnfk7d9E1GzMNDoYKAhallXiiqw6q9L+PlYBspUtQAAWwtTPNHTG09F+cDTwUrkConIGDHQ6GCgIWodZapabDiWgZV/XcLlogoAgFQCDApR4MlePnggwImXo4jorjHQ6GCgIWpdarWAuJR8fHswXWt5BU8HSzzW3QtjIzzhYc/J+ojo9hhodDDQEIknJbcU3ydcwtakbJRevxwlkQB9OrpgXA8vRHd2g7kp57Qhopsx0OhgoCESX2V1HXaezsFPRzNwOP2fyfqcrM0x+r4OeLyHFzq62YpYIREZGgYaHQw0RIblUmE5fj6WgQ2JmcgvVWna7/e2x+M9vPBImAfvkCIiBhpdDDREhqm2To19KQX46VgG9p7PR931mYitzE3wSJg7Hu/hjfu97SGRcCAxUXvEQKODgYbI8OWXVuGXxCz8fCxDszAmAAS62uDx7l6IjfCEo7W5iBUSUWtjoNHBQENkPARBwNFL1/DT0Qz8mpyNqho1AMDcRIohoQo80dMbvfwd2WtD1A4w0OhgoCEyTiVVNdh2MhvrjmQgOUupafd3tsa4nl6Ivd8TTjYyESskopbEQKODgYbI+CVnKrH26BVsOZGF8uo6AICZiQSDuygwPtIbUf5O7LUhamMYaHQw0BC1HeWqWmw7mY01R67gVOY/vTZ+ztYY16N+rI0ze22I2gQGGh0MNERt0+ksJdYeuYItSdmaNaTMTCQY1EWB8T3re2241AKR8WKg0cFAQ9S2latqsf1UNtYcycDJjGJNu6+TFcb19MZY9toQGSUGGh0MNETtx5ns+l6bzSe0e20Gd1FgQqQP75AiMiIMNDoYaIjan4rqWmw/mYPVR65o9dr4u1hjQqQPYu/vAHsrzmtDZMgYaHQw0BC1b6ezlFhzRPsOKZmpFMPC3DEh0oezERMZKAYaHQw0RAQAZapabEnKwo+HruBcTommPVhhiwmR3hh1XwfYWpiJWCER3YiBRgcDDRHdSBAEJGUUY83hK9h26p/ZiK3MTTAi3AMTIn3Q1VMucpVExECjg4GGiG5FWVGDjScysebwFVzIL9O0h3nKMSHSG8PDPWBlzpW/icTAQKODgYaI7qRhDanVhy9jZ3Iuquvqe22szU0wqIsCw8Pd8WCgC8xNpSJXStR+MNDoYKAhontxtbwaGxIzsObwFVwqqtC0yy3NMKSLAsPDPdDL3xGmJgw3RC2JgUYHAw0RNYUgCDh+pRjbTmZjR3IO8ktVmm3ONuaICXXH8HAPdPdx4IzERC2AgUYHAw0RNVedWsCR9KvYdiobO5NzcK2iRrNNYWeBYWH14SbcU85bwIn0hIFGBwMNEelTTZ0af6UVYdvJbOw6k4vSqlrNNi9HSzwS5oHhYR7o7G7LcEPUDAw0OhhoiKilqGrr8Offhdh2Mhu7z+Wh4vrEfQAQ4GKNkd06YPR9HeDlaCVilUTGiYFGBwMNEbWGyuo67D2fj20ns7E3JR/VtWrNtl7+jhhzvyeGdnWHjYy3gRPdDQYaHQw0RNTaSqtq8PuZPGw8kYm/0orQ8LespZkJhoQqEHu/J6ICnGDCwcREt8RAo4OBhojElFVcic0nsvBLYiYuFpZr2t3lFhh9XwfERngiwMVGxAqJDBMDjQ4GGiIyBIIg4ERGMX5JzMS2k9kouWEwcTcve8RGeGJ4mDtXASe6joFGBwMNERmaqpr68Ta/JGZi398FqFPX/zVsbiLFwBBXxN7viT6dXGDGyfuoHWOg0cFAQ0SGrKBUhS1JWfjleJbWKuDONuZ4JMwDI7p54D4ve94CTu0OA40OBhoiMhZns0vwy/FMbEnKQmFZtabd29EKI7t5YGS3Dgh05Xgbah8YaHQw0BCRsampU+NAaiG2JtVP3nfj/DZdPOwwqlsHDA/3gEJuIWKVRC3rXr+/Rb1A++eff2L48OHw8PCARCLB5s2btbZPmjQJEolE6zFkyBBxiiUiaiVmJlL0D3LF0se74dgbA/HJE/chOtgVplIJzmSX4N0d5xC1ZA/GrUjAuiNXoLxhKQai9krUGZ7Ky8sRHh6OKVOmYMyYMY3uM2TIEKxcuVLzXCaTtVZ5RESiszI3xYhwD4wI98C18mr8mpyDrUnZOHLpKg5drH/M33IG/YJcMLJbB0R3doWFmYnYZRO1OlEDTUxMDGJiYm67j0wmg0KhaKWKiIgMl4O1OZ7s5YMne/kgq7gSW5OysSUpC+dzS/H72Tz8fjYPNjJTDO6iwIhuHugd4ART3ilF7YTBz8G9b98+uLq6wsHBAQMGDMA777wDJyenW+6vUqmgUqk0z0tKSm65LxGRsepgb4kZ/QIwo18AUnJLsSUpC1uSspFVXIlfjmfil+OZcLI2x9Cu7hjRzQMR3g6QcmZiasMMZlCwRCLBpk2bMGrUKE3bunXrYGVlBT8/P6SlpeG1116DjY0NEhISYGLSeJfqwoULsWjRopvaOSiYiNo6tVrA8SvXsDkpCzuSc3G1/J87pTzkFnjk+qWrLh52vA2cDJ7R3uXUWKDRdfHiRQQEBGD37t2Ijo5udJ/Gemi8vLwYaIioXampU+OvtCJsTcrG72dyUar6Z2Zif2drDA+vn+OGyy6QobrXQGPwl5xu5O/vD2dnZ6Smpt4y0MhkMg4cJqJ2z8xEir6dXNC3kwuqakKxLyUfW09mY8+5fFwsLMfHey7g4z0XEOJuhxHdPDA83AMd7C3FLpuoyYwq0GRmZqKoqAju7u5il0JEZDQszEwwJNQdQ0LdUaaqxR9nc7E1KRv7LxTibE4JzuaUYMnO84jwccCIcA8M7eoOF1v+w5CMi6iXnMrKypCamgoAuO+++/Dhhx+if//+cHR0hKOjIxYtWoTY2FgoFAqkpaXh5ZdfRmlpKZKTk++6F4YT6xERNe5qeTV2ns7BtpPZOJx+FQ3fBlIJcJ+3AwYEu6J/kCs6u9tyzA21OqMaQ7Nv3z7079//pvaJEydi2bJlGDVqFE6cOIHi4mJ4eHhg0KBBePvtt+Hm5nbX78FAQ0R0Z7nKKmw/lY1tp3JwMqNYa5u73AL9glwxINgVvQOdYGVuVJ37ZKSMKtC0BgYaIqJ7k1Vcibjz+Yg7n4+DaYWoqlFrtpmbStHL3wkDglwwINgN3k5WIlZKbRkDjQ4GGiKipquqqUPCxSLEnc/H3vP5yLxWqbU9wMW6/tJUsCt6+DrCjBP5kZ4w0OhgoCEi0g9BEJCaX4a918PNscvXUKf+5yvEVmaKhzo5IzrYDQND3CC3NBOxWjJ2DDQ6GGiIiFqGsrIG+y8UYO/5fMSnFKDohon8zEwkeKijC4Z2dcfDDDfUBAw0OhhoiIhanlot4FSWEnvP5WHn6VxcyC/TbGO4oaZgoNHBQENE1Pou5JXi1+Qc7EjOwd952uHmwUBnDO3qjkEhCsitGG6ocQw0OhhoiIjExXBDTcFAo4OBhojIcKTml+LXU7nYkZyDlLxSTbuZiQS9r4eb6GBXONlwpuL2joFGBwMNEZFhSs0vw47kHPx6SjvcSCRAuKc9BgTXT+bH1cHbJwYaHQw0RESGryHc/HY6F2dzSrS2udrK0D+ofq6bBzs6w0bGmYrbAwYaHQw0RETGJVdZhbiU+rluDqYWoqK6TrPN3ESKnn6O6H+998bP2VrESqklMdDoYKAhIjJeqto6HL54FXvP5yMuJR+Xiyq0tvs5W6P/9XWmevo5wtyUMxW3FQw0OhhoiIjaBkEQcLGwXLMMw5H0q6i9YaZia3MTPNjRGX07uaJvkAs62FuKWC01FwONDgYaIqK2qbSqBgdTC6/33hSgoFSltb2jqw36dnJB3yAX9PB1hIWZiUiVUlMw0OhgoCEiavvUagFnskuwLyUf8X8X4PiVa7ih8waWZiaICnCqDzidXODLsTcGj4FGBwMNEVH7o6yowYHUQsT/XR9w8kq0e298naw0vTe9/J1gZc47pwwNA40OBhoiovZNEASczy1F/N8F2JeSj2OXrmmNvTE3lSLSzxF9O7lgUIgC3k5WIlZLDRhodDDQEBHRjcpUtfgrtRD7/i5AfEoBsoortbaHecrxSJg7hnZ1h6cDw41YGGh0MNAQEdGtCIKAtIJyxP9dgD3n8nDoYpHW2Jv7vO3xSJgHhnZVwF3Ou6ZaEwONDgYaIiK6W4VlKuw8nYvtJ7Nx5NJV3PgN2cPXAcO61vfcuNpZiFdkO8FAo4OBhoiImiK/pKp+rankHBy9dE3TLpEAkX6OGBbmgZhQBZy5kGaLYKDRwUBDRETNlaOsxI7kXGw/lY0TV4o17VIJ8ECAM4aFuWNIFwUcrM3FK7KNYaDRwUBDRET6lHmtAjuSc7D9VA5OZSo17SZSCXr6OmJwFzc83EXBmYqbiYFGBwMNERG1lCtFFdienI3tJ3NuWiW8awc5BoW4YXCoAh1dbSCRSESq0jgx0OhgoCEiotZwpagCv5/Nxe9n8nD0svaAYj9nawwKccOgLgrc52UPqZTh5k4YaHQw0BARUWsrLFNhz7k87DqThwMXClFdp9Zsc7GV4eEQNwwKccMDAc5cIfwWGGh0MNAQEZGYylS1iE8pwO9nc7H3XD5KVbWabbYyU/QLdsXgLm54KNAFciszESs1LAw0OhhoiIjIUFTXqpFwsQi/n8nFH2fzkH/DCuESCRDkZouefo71D1/Hdj3fDQONDgYaIiIyRGq1gKTMYuw6k4vdZ/OQVlB+0z6+TlbXA44TIv0c4elg2W4GFzPQ6GCgISIiY1BQqsKxS1dxOP0qjqRfxbncEuh+QyvsLP7pwfFzRKCLTZsdYMxAo4OBhoiIjJGysgbHL1+7HnCKkJylRE2d9le2g5UZevg6ItLfCf2DXODvYiNStfrHQKODgYaIiNqCyuo6nMi4hiPpV3H00lUkXr6Gqhq11j5+ztYYEOyKAcGu6OHraNR3UDHQ6GCgISKitqi6Vo3T2UocSb+Kg6mFOHSxSKsHx0Zmij6dnDEg2A39glyMbs0pBhodDDRERNQelKlqceBCAfacy0dcSgEKy7TvoAr3tEd0sCsGdHZFiLudwQ8uZqDRwUBDRETtjVotIDlLiT3n87H3fB5OZ2kvy6Cws0D/YFdEB7uid6AzLM1NRKr01hhodDDQEBFRe5dXUoW48/nYcz4fBy4UorKmTrNNZipFd18H9PR1Qk8/R9znbQ8LM/EDDgONDgYaIiKif1TV1OHQxSJNwMm8Vqm13cxEgjBPe82t4RE+DrCzaP0ZjBlodDDQEBERNU4QBKTml+HQ9blvjqQXIa9EpbWPVAJ0drfTzF7cw8+xVQYYM9DoYKAhIiK6O4IgIONqJQ6nF2luD79UVHHTfgEu1jdM8OeEDvaWeq+FgUYHAw0REVHT5ZVUXe+9qQ8453NLb9rnpUGdMHtAR72+771+f4s6486ff/6J4cOHw8PDAxKJBJs3b9baLggC5s+fD3d3d1haWmLgwIG4cOGCOMUSERG1Q252Fhge7oG3R4Xitzl9cOLNh/HVU93xzEN+CPeyh4lUgi4ecrHLhKmYb15eXo7w8HBMmTIFY8aMuWn7+++/j08++QTfffcd/Pz88Oabb2Lw4ME4e/YsLCza7wqkREREYnGwNsfDIW54OMQNAFCuqoWpifhz2ogaaGJiYhATE9PoNkEQ8NFHH+GNN97AyJEjAQDff/893NzcsHnzZowbN641SyUiIqJGWMtEjRIaBrvIQ3p6OnJzczFw4EBNm1wuR2RkJBISEm75OpVKhZKSEq0HERERtW0GG2hyc3MBAG5ublrtbm5umm2NWbx4MeRyuebh5eXVonUSERGR+Aw20DTVvHnzoFQqNY+MjAyxSyIiIqIWZrCBRqFQAADy8vK02vPy8jTbGiOTyWBnZ6f1ICIiorbNYAONn58fFAoF9uzZo2krKSnB4cOHERUVJWJlREREZGhEHZpcVlaG1NRUzfP09HQkJSXB0dER3t7emDNnDt555x107NhRc9u2h4cHRo0aJV7RREREZHBEDTTHjh1D//79Nc9feOEFAMDEiROxatUqvPzyyygvL8e0adNQXFyMBx98EL/99hvnoCEiIiItXPqAiIiIDI5RLX1AREREpA8MNERERGT0GGiIiIjI6DHQEBERkdFjoCEiIiKjZxhLZLaghpu4uEglERGR8Wj43r7bm7HbfKApLS0FAC5SSUREZIRKS0shl8vvuF+bn4dGrVYjOzsbtra2kEgkejtuSUkJvLy8kJGRwflt7gHPW9PwvDUNz9u94zlrGp63prndeRMEAaWlpfDw8IBUeucRMm2+h0YqlcLT07PFjs8FMJuG561peN6ahuft3vGcNQ3PW9Pc6rzdTc9MAw4KJiIiIqPHQENERERGj4GmiWQyGRYsWACZTCZ2KUaF561peN6ahuft3vGcNQ3PW9Po87y1+UHBRERE1Paxh4aIiIiMHgMNERERGT0GGiIiIjJ6DDRERERk9Bhomujzzz+Hr68vLCwsEBkZiSNHjohdkkFbuHAhJBKJ1iM4OFjssgzOn3/+ieHDh8PDwwMSiQSbN2/W2i4IAubPnw93d3dYWlpi4MCBuHDhgjjFGog7nbNJkybd9NkbMmSIOMUakMWLF6NHjx6wtbWFq6srRo0ahZSUFK19qqqqMGvWLDg5OcHGxgaxsbHIy8sTqWLx3c0569ev302ft+nTp4tUsWFYtmwZwsLCNJPnRUVFYefOnZrt+vqcMdA0wU8//YQXXngBCxYswPHjxxEeHo7BgwcjPz9f7NIMWpcuXZCTk6N5HDhwQOySDE55eTnCw8Px+eefN7r9/fffxyeffILly5fj8OHDsLa2xuDBg1FVVdXKlRqOO50zABgyZIjWZ2/t2rWtWKFhio+Px6xZs3Do0CH88ccfqKmpwaBBg1BeXq7ZZ+7cudi2bRvWr1+P+Ph4ZGdnY8yYMSJWLa67OWcA8Mwzz2h93t5//32RKjYMnp6eWLJkCRITE3Hs2DEMGDAAI0eOxJkzZwDo8XMm0D3r2bOnMGvWLM3zuro6wcPDQ1i8eLGIVRm2BQsWCOHh4WKXYVQACJs2bdI8V6vVgkKhED744ANNW3FxsSCTyYS1a9eKUKHh0T1ngiAIEydOFEaOHClKPcYkPz9fACDEx8cLglD/2TIzMxPWr1+v2efcuXMCACEhIUGsMg2K7jkTBEHo27ev8Pzzz4tXlJFwcHAQvv76a71+zthDc4+qq6uRmJiIgQMHatqkUikGDhyIhIQEESszfBcuXICHhwf8/f0xYcIEXLlyReySjEp6ejpyc3O1PntyuRyRkZH87N3Bvn374OrqiqCgIMyYMQNFRUVil2RwlEolAMDR0REAkJiYiJqaGq3PW3BwMLy9vfl5u073nDVYvXo1nJ2dERoainnz5qGiokKM8gxSXV0d1q1bh/LyckRFRen1c9bmF6fUt8LCQtTV1cHNzU2r3c3NDefPnxepKsMXGRmJVatWISgoCDk5OVi0aBEeeughnD59Gra2tmKXZxRyc3MBoNHPXsM2utmQIUMwZswY+Pn5IS0tDa+99hpiYmKQkJAAExMTscszCGq1GnPmzEHv3r0RGhoKoP7zZm5uDnt7e619+Xmr19g5A4Dx48fDx8cHHh4eOHXqFF555RWkpKRg48aNIlYrvuTkZERFRaGqqgo2NjbYtGkTQkJCkJSUpLfPGQMNtYqYmBjNn8PCwhAZGQkfHx/8/PPPmDp1qoiVUVs3btw4zZ+7du2KsLAwBAQEYN++fYiOjhaxMsMxa9YsnD59muPa7sGtztm0adM0f+7atSvc3d0RHR2NtLQ0BAQEtHaZBiMoKAhJSUlQKpXYsGEDJk6ciPj4eL2+By853SNnZ2eYmJjcNAI7Ly8PCoVCpKqMj729PTp16oTU1FSxSzEaDZ8vfvaax9/fH87OzvzsXTd79mxs374dcXFx8PT01LQrFApUV1ejuLhYa39+3m59zhoTGRkJAO3+82Zubo7AwEBERERg8eLFCA8Px8cff6zXzxkDzT0yNzdHREQE9uzZo2lTq9XYs2cPoqKiRKzMuJSVlSEtLQ3u7u5il2I0/Pz8oFAotD57JSUlOHz4MD979yAzMxNFRUXt/rMnCAJmz56NTZs2Ye/evfDz89PaHhERATMzM63PW0pKCq5cudJuP293OmeNSUpKAoB2/3nTpVaroVKp9Ps50++45fZh3bp1gkwmE1atWiWcPXtWmDZtmmBvby/k5uaKXZrBevHFF4V9+/YJ6enpwsGDB4WBAwcKzs7OQn5+vtilGZTS0lLhxIkTwokTJwQAwocffiicOHFCuHz5siAIgrBkyRLB3t5e2LJli3Dq1Clh5MiRgp+fn1BZWSly5eK53TkrLS0VXnrpJSEhIUFIT08Xdu/eLdx///1Cx44dhaqqKrFLF9WMGTMEuVwu7Nu3T8jJydE8KioqNPtMnz5d8Pb2Fvbu3SscO3ZMiIqKEqKiokSsWlx3OmepqanCW2+9JRw7dkxIT08XtmzZIvj7+wt9+vQRuXJxvfrqq0J8fLyQnp4unDp1Snj11VcFiUQi/P7774Ig6O9zxkDTRJ9++qng7e0tmJubCz179hQOHTokdkkG7fHHHxfc3d0Fc3NzoUOHDsLjjz8upKamil2WwYmLixMA3PSYOHGiIAj1t26/+eabgpubmyCTyYTo6GghJSVF3KJFdrtzVlFRIQwaNEhwcXERzMzMBB8fH+GZZ57hPz4EodFzBkBYuXKlZp/Kykph5syZgoODg2BlZSWMHj1ayMnJEa9okd3pnF25ckXo06eP4OjoKMhkMiEwMFD4z3/+IyiVSnELF9mUKVMEHx8fwdzcXHBxcRGio6M1YUYQ9Pc5kwiCIDSxx4iIiIjIIHAMDRERERk9BhoiIiIyegw0REREZPQYaIiIiMjoMdAQERGR0WOgISIiIqPHQENERERGj4GGiNodiUSCzZs3i10GEekRAw0RtapJkyZBIpHc9BgyZIjYpRGRETMVuwAian+GDBmClStXarXJZDKRqiGitoA9NETU6mQyGRQKhdbDwcEBQP3loGXLliEmJgaWlpbw9/fHhg0btF6fnJyMAQMGwNLSEk5OTpg2bRrKysq09vn222/RpUsXyGQyuLu7Y/bs2VrbCwsLMXr0aFhZWaFjx47YunVry/7QRNSiGGiIyOC8+eabiI2NxcmTJzFhwgSMGzcO586dAwCUl5dj8ODBcHBwwNGjR7F+/Xrs3r1bK7AsW7YMs2bNwrRp05CcnIytW7ciMDBQ6z0WLVqExx57DKdOncLQoUMxYcIEXL16tVV/TiLSI/2tp0lEdGcTJ04UTExMBGtra63Hu+++KwhC/YrG06dP13pNZGSkMGPGDEEQBGHFihWCg4ODUFZWptn+66+/ClKpVLOKtoeHh/D666/fsgYAwhtvvKF5XlZWJgAQdu7cqbefk4haF8fQEFGr69+/P5YtW6bV5ujoqPlzVFSU1raoqCgkJSUBAM6dO4fw8HBYW1trtvfu3RtqtRopKSmQSCTIzs5GdHT0bWsICwvT/Nna2hp2dnbIz89v6o9ERCJjoCGiVmdtbX3TJSB9sbS0vKv9zMzMtJ5LJBKo1eqWKImIWgHH0BCRwTl06NBNzzt37gwA6Ny5M06ePIny8nLN9oMHD0IqlSIoKAi2trbw9fXFnj17WrVmIhIXe2iIqNWpVCrk5uZqtZmamsLZ2RkAsH79enTv3h0PPvggVq9ejSNHjuCbb74BAEyYMAELFizAxIkTsXDhQhQUFOC5557Dv/71L7i5uQEAFi5ciOnTp8PV1RUxMTEoLS3FwYMH8dxzz7XuD0pErYaBhoha3W+//QZ3d3ettqCgIJw/fx5A/R1I69atw8yZM+Hu7o61a9ciJCQEAGBlZYVdu3bh+eefR48ePWBlZYXY2Fh8+OGHmmNNnDgRVVVVWLp0KV566SU4Oztj7NixrfcDElGrkwiCIIhdBBFRA4lEgk2bNmHUqFFil0JERoRjaIiIiMjoMdAQERGR0eMYGiIyKLwKTkRNwR4aIiIiMnoMNERERGT0GGiIiIjI6DHQEBERkdFjoCEiIiKjx0BDRERERo+BhoiIiIweAw0REREZPQYaIiIiMnr/D1suBGmGhDANAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -529,27 +530,27 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Test Loss: 21.960862\n",
+ "Test Loss: 21.238544\n",
"\n",
- "Test Accuracy of airplane: 68% (681/1000)\n",
- "Test Accuracy of automobile: 74% (740/1000)\n",
- "Test Accuracy of bird: 45% (452/1000)\n",
- "Test Accuracy of cat: 51% (518/1000)\n",
- "Test Accuracy of deer: 55% (550/1000)\n",
- "Test Accuracy of dog: 50% (505/1000)\n",
- "Test Accuracy of frog: 73% (730/1000)\n",
- "Test Accuracy of horse: 66% (669/1000)\n",
- "Test Accuracy of ship: 79% (796/1000)\n",
- "Test Accuracy of truck: 62% (621/1000)\n",
+ "Test Accuracy of airplane: 57% (572/1000)\n",
+ "Test Accuracy of automobile: 78% (782/1000)\n",
+ "Test Accuracy of bird: 48% (481/1000)\n",
+ "Test Accuracy of cat: 49% (495/1000)\n",
+ "Test Accuracy of deer: 69% (693/1000)\n",
+ "Test Accuracy of dog: 48% (488/1000)\n",
+ "Test Accuracy of frog: 69% (695/1000)\n",
+ "Test Accuracy of horse: 67% (673/1000)\n",
+ "Test Accuracy of ship: 77% (773/1000)\n",
+ "Test Accuracy of truck: 70% (700/1000)\n",
"\n",
- "Test Accuracy (Overall): 62% (6262/10000)\n"
+ "Test Accuracy (Overall): 63% (6352/10000)\n"
]
}
],
@@ -625,7 +626,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -694,60 +695,60 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Epoch: 0 \tTraining Loss: 45.224616 \tValidation Loss: 41.426994\n",
- "Validation loss decreased (inf --> 41.426994). Saving model ...\n",
- "Epoch: 1 \tTraining Loss: 36.723764 \tValidation Loss: 32.832016\n",
- "Validation loss decreased (41.426994 --> 32.832016). Saving model ...\n",
- "Epoch: 2 \tTraining Loss: 31.097073 \tValidation Loss: 29.986182\n",
- "Validation loss decreased (32.832016 --> 29.986182). Saving model ...\n",
- "Epoch: 3 \tTraining Loss: 27.964499 \tValidation Loss: 26.652988\n",
- "Validation loss decreased (29.986182 --> 26.652988). Saving model ...\n",
- "Epoch: 4 \tTraining Loss: 25.368614 \tValidation Loss: 25.234237\n",
- "Validation loss decreased (26.652988 --> 25.234237). Saving model ...\n",
- "Epoch: 5 \tTraining Loss: 23.028257 \tValidation Loss: 22.257799\n",
- "Validation loss decreased (25.234237 --> 22.257799). Saving model ...\n",
- "Epoch: 6 \tTraining Loss: 21.007300 \tValidation Loss: 21.598088\n",
- "Validation loss decreased (22.257799 --> 21.598088). Saving model ...\n",
- "Epoch: 7 \tTraining Loss: 19.216898 \tValidation Loss: 20.251749\n",
- "Validation loss decreased (21.598088 --> 20.251749). Saving model ...\n",
- "Epoch: 8 \tTraining Loss: 17.697483 \tValidation Loss: 20.098650\n",
- "Validation loss decreased (20.251749 --> 20.098650). Saving model ...\n",
- "Epoch: 9 \tTraining Loss: 16.180104 \tValidation Loss: 18.660767\n",
- "Validation loss decreased (20.098650 --> 18.660767). Saving model ...\n",
- "Epoch: 10 \tTraining Loss: 14.774329 \tValidation Loss: 17.974099\n",
- "Validation loss decreased (18.660767 --> 17.974099). Saving model ...\n",
- "Epoch: 11 \tTraining Loss: 13.394195 \tValidation Loss: 17.683563\n",
- "Validation loss decreased (17.974099 --> 17.683563). Saving model ...\n",
- "Epoch: 12 \tTraining Loss: 12.029678 \tValidation Loss: 18.618383\n",
- "Epoch: 13 \tTraining Loss: 10.594165 \tValidation Loss: 18.077175\n",
- "Epoch: 14 \tTraining Loss: 9.346292 \tValidation Loss: 19.066724\n",
- "Epoch: 15 \tTraining Loss: 7.995027 \tValidation Loss: 20.780326\n",
- "Epoch: 16 \tTraining Loss: 6.687421 \tValidation Loss: 21.976052\n",
- "Epoch: 17 \tTraining Loss: 5.550229 \tValidation Loss: 22.871844\n",
- "Epoch: 18 \tTraining Loss: 4.561936 \tValidation Loss: 24.607759\n",
- "Epoch: 19 \tTraining Loss: 3.798454 \tValidation Loss: 25.928546\n",
- "Epoch: 20 \tTraining Loss: 2.968054 \tValidation Loss: 26.749855\n",
- "Epoch: 21 \tTraining Loss: 2.694036 \tValidation Loss: 30.236186\n",
- "Epoch: 22 \tTraining Loss: 2.217052 \tValidation Loss: 30.743367\n",
- "Epoch: 23 \tTraining Loss: 1.851026 \tValidation Loss: 31.145896\n",
- "Epoch: 24 \tTraining Loss: 1.434988 \tValidation Loss: 33.876719\n",
- "Epoch: 25 \tTraining Loss: 1.226250 \tValidation Loss: 36.642382\n",
- "Epoch: 26 \tTraining Loss: 1.181099 \tValidation Loss: 35.523206\n",
- "Epoch: 27 \tTraining Loss: 0.926557 \tValidation Loss: 37.227898\n",
- "Epoch: 28 \tTraining Loss: 1.313841 \tValidation Loss: 38.011964\n",
- "Epoch: 29 \tTraining Loss: 0.968135 \tValidation Loss: 38.862978\n"
+ "Epoch: 0 \tTraining Loss: 44.580487 \tValidation Loss: 40.655719\n",
+ "Validation loss decreased (inf --> 40.655719). Saving model ...\n",
+ "Epoch: 1 \tTraining Loss: 37.395407 \tValidation Loss: 34.004712\n",
+ "Validation loss decreased (40.655719 --> 34.004712). Saving model ...\n",
+ "Epoch: 2 \tTraining Loss: 31.076469 \tValidation Loss: 28.947368\n",
+ "Validation loss decreased (34.004712 --> 28.947368). Saving model ...\n",
+ "Epoch: 3 \tTraining Loss: 27.819081 \tValidation Loss: 26.298716\n",
+ "Validation loss decreased (28.947368 --> 26.298716). Saving model ...\n",
+ "Epoch: 4 \tTraining Loss: 25.659337 \tValidation Loss: 25.099186\n",
+ "Validation loss decreased (26.298716 --> 25.099186). Saving model ...\n",
+ "Epoch: 5 \tTraining Loss: 23.534661 \tValidation Loss: 23.580557\n",
+ "Validation loss decreased (25.099186 --> 23.580557). Saving model ...\n",
+ "Epoch: 6 \tTraining Loss: 21.658956 \tValidation Loss: 22.173245\n",
+ "Validation loss decreased (23.580557 --> 22.173245). Saving model ...\n",
+ "Epoch: 7 \tTraining Loss: 19.803460 \tValidation Loss: 21.094384\n",
+ "Validation loss decreased (22.173245 --> 21.094384). Saving model ...\n",
+ "Epoch: 8 \tTraining Loss: 18.147863 \tValidation Loss: 19.315424\n",
+ "Validation loss decreased (21.094384 --> 19.315424). Saving model ...\n",
+ "Epoch: 9 \tTraining Loss: 16.645522 \tValidation Loss: 19.678809\n",
+ "Epoch: 10 \tTraining Loss: 15.231918 \tValidation Loss: 17.332762\n",
+ "Validation loss decreased (19.315424 --> 17.332762). Saving model ...\n",
+ "Epoch: 11 \tTraining Loss: 13.963853 \tValidation Loss: 17.392996\n",
+ "Epoch: 12 \tTraining Loss: 12.640484 \tValidation Loss: 17.975388\n",
+ "Epoch: 13 \tTraining Loss: 11.419415 \tValidation Loss: 17.060967\n",
+ "Validation loss decreased (17.332762 --> 17.060967). Saving model ...\n",
+ "Epoch: 14 \tTraining Loss: 10.121284 \tValidation Loss: 17.496235\n",
+ "Epoch: 15 \tTraining Loss: 8.883715 \tValidation Loss: 17.553297\n",
+ "Epoch: 16 \tTraining Loss: 7.629162 \tValidation Loss: 18.591316\n",
+ "Epoch: 17 \tTraining Loss: 6.462874 \tValidation Loss: 20.458817\n",
+ "Epoch: 18 \tTraining Loss: 5.431612 \tValidation Loss: 19.972335\n",
+ "Epoch: 19 \tTraining Loss: 4.461186 \tValidation Loss: 22.878541\n",
+ "Epoch: 20 \tTraining Loss: 3.639514 \tValidation Loss: 22.700774\n",
+ "Epoch: 21 \tTraining Loss: 3.053722 \tValidation Loss: 24.948127\n",
+ "Epoch: 22 \tTraining Loss: 2.251474 \tValidation Loss: 26.497270\n",
+ "Epoch: 23 \tTraining Loss: 1.806678 \tValidation Loss: 29.242567\n",
+ "Epoch: 24 \tTraining Loss: 1.558967 \tValidation Loss: 30.254290\n",
+ "Epoch: 25 \tTraining Loss: 1.469055 \tValidation Loss: 30.540426\n",
+ "Epoch: 26 \tTraining Loss: 1.545504 \tValidation Loss: 32.255079\n",
+ "Epoch: 27 \tTraining Loss: 1.368537 \tValidation Loss: 32.683681\n",
+ "Epoch: 28 \tTraining Loss: 0.980868 \tValidation Loss: 35.020791\n",
+ "Epoch: 29 \tTraining Loss: 0.884202 \tValidation Loss: 34.967844\n"
]
}
],
"source": [
"import torch.optim as optim \n",
+ "import numpy as np\n",
"model = Second_Net()\n",
"\n",
"criterion = nn.CrossEntropyLoss() # specify loss function\n",
@@ -826,27 +827,27 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Test Loss: 17.850414\n",
+ "Test Loss: 17.203986\n",
"\n",
- "Test Accuracy of airplane: 79% (794/1000)\n",
- "Test Accuracy of automobile: 87% (873/1000)\n",
- "Test Accuracy of bird: 58% (585/1000)\n",
- "Test Accuracy of cat: 40% (402/1000)\n",
- "Test Accuracy of deer: 61% (616/1000)\n",
- "Test Accuracy of dog: 67% (670/1000)\n",
- "Test Accuracy of frog: 73% (732/1000)\n",
- "Test Accuracy of horse: 76% (769/1000)\n",
- "Test Accuracy of ship: 77% (779/1000)\n",
- "Test Accuracy of truck: 73% (731/1000)\n",
+ "Test Accuracy of airplane: 74% (742/1000)\n",
+ "Test Accuracy of automobile: 80% (804/1000)\n",
+ "Test Accuracy of bird: 56% (567/1000)\n",
+ "Test Accuracy of cat: 63% (631/1000)\n",
+ "Test Accuracy of deer: 70% (706/1000)\n",
+ "Test Accuracy of dog: 47% (472/1000)\n",
+ "Test Accuracy of frog: 85% (850/1000)\n",
+ "Test Accuracy of horse: 72% (726/1000)\n",
+ "Test Accuracy of ship: 81% (813/1000)\n",
+ "Test Accuracy of truck: 83% (830/1000)\n",
"\n",
- "Test Accuracy (Overall): 69% (6951/10000)\n"
+ "Test Accuracy (Overall): 71% (7141/10000)\n"
]
}
],
@@ -855,8 +856,8 @@
"\n",
"# track test loss\n",
"test_loss = 0.0\n",
- "class_correct = list(0.0 for i in range(10))\n",
- "class_total = list(0.0 for i in range(10))\n",
+ "class_correct2 = list(0.0 for i in range(10))\n",
+ "class_total2 = list(0.0 for i in range(10))\n",
"\n",
"model.eval()\n",
"# iterate over test data\n",
@@ -874,7 +875,7 @@
" _, pred = torch.max(output, 1)\n",
" # compare predictions to true label\n",
" correct_tensor = pred.eq(target.data.view_as(pred))\n",
- " correct = (\n",
+ " correct2 = (\n",
" np.squeeze(correct_tensor.numpy())\n",
" if not train_on_gpu\n",
" else np.squeeze(correct_tensor.cpu().numpy())\n",
@@ -882,22 +883,22 @@
" # calculate test accuracy for each object class\n",
" for i in range(batch_size):\n",
" label = target.data[i]\n",
- " class_correct[label] += correct[i].item()\n",
- " class_total[label] += 1\n",
+ " class_correct2[label] += correct2[i].item()\n",
+ " class_total2[label] += 1\n",
"\n",
"# average test loss\n",
"test_loss = test_loss / len(test_loader)\n",
"print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n",
"\n",
"for i in range(10):\n",
- " if class_total[i] > 0:\n",
+ " if class_total2[i] > 0:\n",
" print(\n",
" \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n",
" % (\n",
" classes[i],\n",
- " 100 * class_correct[i] / class_total[i],\n",
- " np.sum(class_correct[i]),\n",
- " np.sum(class_total[i]),\n",
+ " 100 * class_correct2[i] / class_total2[i],\n",
+ " np.sum(class_correct2[i]),\n",
+ " np.sum(class_total2[i]),\n",
" )\n",
" )\n",
" else:\n",
@@ -906,13 +907,20 @@
"print(\n",
" \"\\nTest Accuracy (Overall): %2d%% (%2d/%2d)\"\n",
" % (\n",
- " 100.0 * np.sum(class_correct) / np.sum(class_total),\n",
- " np.sum(class_correct),\n",
- " np.sum(class_total),\n",
+ " 100.0 * np.sum(class_correct2) / np.sum(class_total2),\n",
+ " np.sum(class_correct2),\n",
+ " np.sum(class_total2),\n",
" )\n",
")"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "With this new network our accuracy increased up to 71%"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -922,12 +930,12 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -946,6 +954,13 @@
"plt.show()"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Comparison of the results obtained between new model and original model"
+ ]
+ },
{
"cell_type": "markdown",
"id": "944991a2",