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@nofreewill42
Created March 13, 2020 10:52
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from fastai.vision import *\n",
"from own.show_filts import show_filts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[PosixPath('/home/nofreewill/.fastai/data/mnist_png/testing'),\n",
" PosixPath('/home/nofreewill/.fastai/data/mnist_png/training'),\n",
" PosixPath('/home/nofreewill/.fastai/data/mnist_png/models')]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path = untar_data(URLs.MNIST)\n",
"path.ls()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 14x14"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"data_small = ImageDataBunch.from_folder(path, train='training', valid='testing', size=14, ds_tfms=get_transforms(do_flip=False))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 144x144 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"data_small.show_batch(rows=2, figsize=(2,2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Change first filters to 5x5"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False),\n",
" Linear(in_features=512, out_features=1000, bias=True))"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = models.resnet18()\n",
"model.conv1, model.fc"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1728x144 with 64 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"show_filts(model)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(Conv2d(3, 64, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), bias=False),\n",
" Linear(in_features=512, out_features=10, bias=True))"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.conv1 = nn.Conv2d(3,64, kernel_size=5,stride=2,padding=2, bias=False)\n",
"model.fc = nn.Linear(512,data_small.c)\n",
"model.conv1, model.fc"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1728x144 with 64 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"show_filts(model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"learn = Learner(data_small, model, metrics=[accuracy])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <div>\n",
" <style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
" </style>\n",
" <progress value='0' class='' max='1', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" 0.00% [0/1 00:00<00:00]\n",
" </div>\n",
" \n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>accuracy</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table><p>\n",
"\n",
" <div>\n",
" <style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
" </style>\n",
" <progress value='88' class='' max='937', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" 9.39% [88/937 00:02<00:20 2.3231]\n",
" </div>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"learn.lr_find()\n",
"learn.recorder.plot()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>accuracy</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.318107</td>\n",
" <td>0.173854</td>\n",
" <td>0.943000</td>\n",
" <td>00:19</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.219014</td>\n",
" <td>0.144017</td>\n",
" <td>0.955400</td>\n",
" <td>00:19</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.178614</td>\n",
" <td>0.118437</td>\n",
" <td>0.961500</td>\n",
" <td>00:19</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.153931</td>\n",
" <td>0.110065</td>\n",
" <td>0.966400</td>\n",
" <td>00:19</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>0.108716</td>\n",
" <td>0.061386</td>\n",
" <td>0.980900</td>\n",
" <td>00:20</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>0.094976</td>\n",
" <td>0.062073</td>\n",
" <td>0.980700</td>\n",
" <td>00:20</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>0.086879</td>\n",
" <td>0.087880</td>\n",
" <td>0.972900</td>\n",
" <td>00:20</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>0.072044</td>\n",
" <td>0.059609</td>\n",
" <td>0.980300</td>\n",
" <td>00:20</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>0.059870</td>\n",
" <td>0.043094</td>\n",
" <td>0.985400</td>\n",
" <td>00:20</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>0.059321</td>\n",
" <td>0.038578</td>\n",
" <td>0.987300</td>\n",
" <td>00:20</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>0.052465</td>\n",
" <td>0.034340</td>\n",
" <td>0.988500</td>\n",
" <td>00:20</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>0.037962</td>\n",
" <td>0.030792</td>\n",
" <td>0.989400</td>\n",
" <td>00:20</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>0.032169</td>\n",
" <td>0.031229</td>\n",
" <td>0.989700</td>\n",
" <td>00:20</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>0.024835</td>\n",
" <td>0.026913</td>\n",
" <td>0.991000</td>\n",
" <td>00:20</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>0.028127</td>\n",
" <td>0.024987</td>\n",
" <td>0.991400</td>\n",
" <td>00:20</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>0.027680</td>\n",
" <td>0.024727</td>\n",
" <td>0.991700</td>\n",
" <td>00:20</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"learn.fit_one_cycle(16, 1e-3)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1728x144 with 64 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"show_filts(model)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"99.17% accurate\n"
]
}
],
"source": [
"loss, acc = learn.validate()\n",
"print(f'{round(acc.item()*100, 2)}% accurate')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Images twice the size\n",
"### 28x28"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"data_big = ImageDataBunch.from_folder(path, train='training', valid='testing', size=28, ds_tfms=get_transforms(do_flip=False))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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WrSqUdzfKy8vZu3cvYGS/MjMzAfj2228tWXSxXOBAIKBCTmFhoVp4mD9/PmFhYWzevBmAjRs3qj3AtbW17Srx7gYaGxvZtWsXAI8++qjaK252eR1Fh2iXY2uJd4/Ho7JVubm5NDU1UVZWBhjLZnV1dUD77wl2yxkd0dHRgDEo/fXXXwFu6Sa0610HWwW2G7cI3FH0ISzdGC2wy9GHkboc7cEuRwvscrTALkcL7HK0wC5HC+xy/g8PfoDo888Y4gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 144x144 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"data_big.show_batch(rows=2, figsize=(2,2))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"learn_big = Learner(data_big, model, metrics=[accuracy])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Obviously, it will do terrible"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"9.71% accurate\n"
]
}
],
"source": [
"loss, acc = learn_big.validate()\n",
"print(f'{round(acc.item()*100, 2)}% accurate')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Because the first conv layer detects what is appropriate to 14x14 images.\n",
"\n",
"So, lets make it compatible to 28x28 images.\n",
"\n",
"For that to happen, we just need it to span twice as much area and also to stride twice as much. The former can be adjusted by dilation and the latter with stride."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Conv2d(3, 64, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), bias=False)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.conv1"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"model.conv1.stride = tuple(x*2 for x in model.conv1.stride)\n",
"model.conv1.padding = tuple(x*2 for x in model.conv1.padding)\n",
"model.conv1.dilation = tuple(x*2 for x in model.conv1.dilation)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Conv2d(3, 64, kernel_size=(5, 5), stride=(4, 4), padding=(4, 4), dilation=(2, 2), bias=False)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.conv1"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"98.89% accurate\n"
]
}
],
"source": [
"loss, acc = learn_big.validate()\n",
"print(f'{round(acc.item()*100, 2)}% accurate')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Looks good, but what if we would have actually bigger filters instead of just bigger dilation?"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"conv1_small = model.conv1"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"model.conv1 = nn.Conv2d(3,64, kernel_size=9, stride=4, padding=4, bias=False).to(data_big.device)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"conv1_big = model.conv1"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1728x144 with 64 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"show_filts(model)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"11.45% accurate\n"
]
}
],
"source": [
"loss, acc = learn_big.validate()\n",
"print(f'{round(acc.item()*100, 2)}% accurate')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Change these filters' weights to the weights of the trained 5x5 filters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### With resizing them like if they were images"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"from torchvision import transforms"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"weight_small = conv1_small.weight.data.cpu()\n",
"weight_small_imgs = []\n",
"# Convert them to PIL Images so we can use resize\n",
"for i in range(len(weight_small)):\n",
" weight_small_imgs.append(transforms.ToPILImage()((weight_small[i]-weight_small.min())/max((weight_small.max()-weight_small.min()), 1e-6)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### There are 5 algorithms to resize an image in PIL Image"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 360x144 with 10 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"img = weight_small_imgs[0]\n",
"fig, axs = plt.subplots(2,5, figsize=(5,2))\n",
"for i in range(5):\n",
" Image((weight_small[0]-weight_small.min())/max((weight_small.max()-weight_small.min()), 1e-6)).show(ax=axs[0,i], figsize=(1,1))\n",
" Image(transforms.ToTensor()(img.resize((9,9), resample=i))).show(ax=axs[1,i], figsize=(1,1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Which one could be the best to use"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1728x144 with 64 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
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"metadata": {},
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},
{
"name": "stdout",
"output_type": "stream",
"text": [
"73.83% accurate\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1728x144 with 64 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"67.38% accurate\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 1728x144 with 64 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"83.19% accurate\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1728x144 with 64 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"73.45% accurate\n"
]
},
{
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"text/plain": [
"<Figure size 1728x144 with 64 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"73.83% accurate\n"
]
}
],
"source": [
"for j in range(5):\n",
" for i in range(len(weight_small)):\n",
" img = weight_small_imgs[i]\n",
" model.conv1.weight.data[i,:] = (transforms.ToTensor()(img.resize((9,9), resample=j)))*(weight_small.max()-weight_small.min()) + weight_small.min()\n",
" show_filts(model)\n",
" plt.show()\n",
" loss, acc = learn_big.validate()\n",
" print(f'{round(acc.item()*100, 2)}% accurate')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### It may be the 2nd one"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1728x144 with 64 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"83.19% accurate\n"
]
}
],
"source": [
"j = 2\n",
"for i in range(len(weight_small)):\n",
" img = weight_small_imgs[i]\n",
" model.conv1.weight.data[i,:] = (transforms.ToTensor()(img.resize((9,9), resample=j)))*(weight_small.max()-weight_small.min()) + weight_small.min()\n",
"show_filts(model)\n",
"plt.show()\n",
"loss, acc = learn_big.validate()\n",
"print(f'{round(acc.item()*100, 2)}% accurate')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Let's fix the accuracy drop"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <div>\n",
" <style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
" </style>\n",
" <progress value='0' class='' max='1', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" 0.00% [0/1 00:00<00:00]\n",
" </div>\n",
" \n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>accuracy</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table><p>\n",
"\n",
" <div>\n",
" <style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
" </style>\n",
" <progress value='56' class='' max='937', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" 5.98% [56/937 00:01<00:21 0.0304]\n",
" </div>\n",
" "
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
]
},
{
"data": {
"image/png": 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krJx/fJaOKvz7iwwGd4vlx6cnMWV4NxJjIpu6eq3Sqp15DE2KIzLMpl/xlz3+MqaF2JV7BFX4ww8H8ceLBxEaIvzp3W8Y95dP+NlzK0+Z5NA0TElZOev3FAT9So51ZXcqxrQQGc6U66nJHRiW1J7rzuzNtgOFvLFmD2+t2cN1c1fy5s1nMjTJZsoNhE17D1NaVmEj5evI7lSMaSEq1/FI7tj2RFq/zjH87+SBfPCrCXRsF8ntr67l2PHypqpiq7J6Z2Ujvc3pVRcWVIxpITKzi+nYLpJYZ1JDb3HR4fz1smFsO1jEQx9tbYLatT5pO/LoGR9Np5iopq5Ki2JBxZgWIjOnmN4do6vdPqF/IleP68UzX2WyLCO3EWvW+qiqZ9CjtafUmQUVY1qIjJxiens9+vLlrosG0is+mjteW0dRSVm9jvPC0h1Mm72UvOLSeu3fGmTlHSW7sMTGp9SDBRVjWoDCY8fJKSqhd8d2NeaLjgjjoSuGszf/KPe9+02NeX05WHiMB97fwrKMQ8yYu6Legamlq5xEcpQ10teZBRVjWoAdOUcAar1TARjVK54bv9eHV1bu5tMtB+p0nMc/2UZpWQV/+OEgNu49zPXPr2yVDf8b9xTwp3e/4Wip73NbtTOPthGhNsC0HiyoGNMCZOQUAZCSWHtQAfj1+f0Y2CWG3y7YwCE/H2NlZBfx8ordTB/Tk5+d1ZuHrxjO8sxD3PLSao6XV9S77s3R80t28OxXmcyal+YzaK7amcfInh0IDbFJJOvKgooxLUBmTjEi0DO++oZ6b5FhoTxy5QgKjpbyf29twJ+JY//+0VYiw0K49bx+AEwd0Z17pw7hv5sP8tsF66moaD2Tz67LyqdzbCSLt+Vw84urKS37LmgWlZSxZf9ha0+pJwsqxrQAmTnFdG/fhqhw/6cLOa1rLLdN6s+iDft59qvMGvOu2ZXHog37ueHslJOmfLl6XC9+8/0BvLlmD/e8s8mv4NTcFZWUse1gEVeN6cX9lwzh0y0H+eXL392NrdudT4VCqgWVerER9ca0AJl+9Pzy5cYJfdi4p4D73ttMx3aR/Ghk91PyqCp/eX8LHdtF+Jze/eZz+lBw9Dizv8wgNiqc/7mgf4teW2RDVgGqMLxHHOcM6MTxsgrueecbbpu/lkevHMGqnXmIwIieNuixPiyoGNPMqSqZ2cVccvqpAaE2oSHCI1eOIK94JXe8to646HDOHdDppDyfbT3IisxD3Dt1MO0iT/2TICLcdeFADh89zhOfpfPu+r38YFhXfjisGwO7xLS4ALM+Kx+AYUmeoDHjzN6Ullfw50VbiAgNIbuohAGdY3wOMjW1c/Xxl4hMFpGtIpIuInf62B4pIvOd7ctFJNlJnyQiq0Rkg/Nzotc+nztlrnVenWoqy5iWLqeolMKSsnrdqYCnfWX2NaMY0CWGm/+zmjXOGiEA5RXKg+9vJTkhmuljelZbhohw/yVD+etlw+gRH82/vsjgwscWc/7DX/Dwx9/y7YHCetWtKazLyqdnfDTxbSNOpM2a0Ic7LujPG2v2sHhbjrWnNIBrQUVEQoEngQuBQcB0ERlUJdtMIE9V+wKPAA866TnAxao6FM8a9vOq7PcTVR3hvA7WUpYxLdqOXM+cX/UNKgAxUeE8d90YOsVGct1zK0k/6AkCb6zOYuuBQu74/gDCQ2v+cxAaIlyR2oN5M8ey4nfncf8lQ+gUE8UTn27jgke+5Ip/L2Xd7vx617GxrNtdwDAfk27eMrEfv5zYF4BxKQmNXa1Ww807lTFAuqpmqGop8AowtUqeqcDzzvsFwHkiIqq6RlX3OumbgCgRqW3BCJ9lNfgsjGlimdmeoJJSy8DH2iTGRDLvZ2MJDw3h6mdXkJlTzMMff8vwpDh+MLRrncpKaBfJT8b24uVZ41j2u/O4+4eDyMguYuqTX3Pry2vYfehIg+rqluzCEvbkH2VED9/tJbdP6s9Ht03g4mF1ux7mO24Gle7Abq/PWU6azzyqWgYUAFW/IlwKrFHVEq+0uc6jr7u9Aoc/ZSEis0QkTUTSsrNt/QnT/GXkFBMeKnTv0KbBZfVMiOb568ZQdKyMix5bzL6CY9x54WkNahfpFBPFzLN689kd53DLuX35cNN+znvoC/6yaDMFR483uM6BVNmeMryaoCIi9O/c8tqJmhM3g4qvf5Wq/RFrzCMig/E8xrrRa/tPnMdiZzuvq+twPFR1tqqmqmpqYmJiDdU3pnnIzCmiV0LbgA3EG9QtlmeuTaVclXMHJDK+T2Ae9cREhXPH9wfw+W/O4eLh3Zi9OINz/vYZ85buaDZjXNbtzidEYHA3Wx7YLW4GlSygh9fnJGBvdXlEJAyIAw45n5OAN4FrVHV75Q6qusf5WQi8hOcxW41lGdOS1bc7cU3GpiTwye3f48mfnB7QcgG6xrXhoSuG884tZ3Fa11jufnsTN7yQ1iwmqFyXVUD/zjFER1jHV7e4GVRWAv1EpLeIRADTgIVV8izE0xAPcBnwqaqqiLQH3gPuUtWvKzOLSJiIdHTehwM/BDbWVJYL52VMoymvUHbkHgl4UAHoER/t6h/XId3jePH6sfzx4kF8uS2bHzy+mFU7m+57nqqyLiu/2vYUExiuBRWnXeMW4ENgM/Cqqm4SkXtFZIqT7VkgQUTSgduBym7HtwB9gburdB2OBD4UkfXAWmAP8HQtZRnTYu3NP0ppWYUrQaUxiAjXndmb1286g7DQEK749zL+9cX2JnkctuvQEfKPHD8xPsW4w9V7QFVdBCyqkvYHr/fHgMt97HcfcF81xY6q5lg+yzKmJatcQrilBpVKw5La8+6tZ3Hn6+udqfVzefiKESeNFXHbuqwCwDOS3rjH5v4yphmrDCopLTyoAMRGhfPkVadz79TBLEnP5aLHFjfqoMl1u/OJCg+hf2ebzt5NfgUVEelTOU5ERM4RkVuddg9jjIsyc4ppGxF60iSPLZmIcM34ZN64+QzKVbl2zgr2FRxtlGOv253P4G5xtQ7yNA3j79V9HSgXkb542i564+l5ZYxxUWZOMb0T27a6cRNDusfx3HWjOXz0ONfNXcnhY7WPZ0k/WMSiDfvqdbyy8go27i1guLWnuM7foFLhNLxfAjyqqrcBNuTUGJd5uhM3bCR9czW4Wxz/unoU6QeLuPGFVZSUVb/C5Jtrsrj4H19x84urmf3l9mrzVefbA0UcO15h7SmNwN+gclxEpuPpsvuuk2ZTeBrjpeBIYEePl5SVk5V3hN4J/i3M1RKd3S+Rv142jKUZufzmtVMXAispK+f3b27gtvnrGJoUx+TBXfjzoi28lra7mhJ9W1c5kt7uVFznb1C5DhgP3K+qmSLSG/iPe9UypmVZsj2HEX/6qN6PZ3zZfegIFQq9/VxCuKX68elJ/HbyABau28uDH2w5kb770BEu/9dSXly+ixsnpPDS9WN5fPpIzu7XkTvf2MB/vzng9zHW7c4nrk04vVpxgG4u/OpSrKrfALcCiEgHIEZVH3CzYsa0JIu35aAKv3ltHf07x9C3U8MfWWVkV3Ynbp2Pv7zd9L0+7Ms/xr+/zKBLXBTJCW359fy1VKjy76tH8f3BXU7kfeqno/jJ08v4xUurmTdzLGN6x9da/rqsAob3aN/q2qaaI397f30uIrEiEg+swzOh48PuVs2YlmPVjjxSEtsSFR7Kz/+ziuKSsgaXeWKMSkLrvlMBT6+we6YM5oJBnbn33W+47rmVdGvfhnd/edZJAQWgXWQYc68bQ/cObZj5/Eo27ztcY9lHSsv49kAhw31Md28Cz9/HX3Gqehj4MTBXVUcB57tXLWNajtKyCtZl5TNxQCf+MX0kGdlF/Pb19Q1ezz0zp5iEthHERQdH82VoiPD49JGcf1pnfjK2J2/efAa9qgmo8W0jmDdzLG0jwrhmzgp25VY/1f6mvYcpr1BrT2kk/gaVMBHpClzBdw31xhhg494CSsoqSE3uwBl9O/Kb7w/kvfX7mPP1jgaV68ZEks1dVHgoT1+Tyv2XDCUqPLTGvN3bt2HezDGUllVw9ZzlHDx8zGe+yoXDhlnPr0bhb1C5F88cXttVdaWIpADb3KuWMS1H2g7PJImjenme7f/8eylcMKgzf1m0mZU76j+BYjAGlbrq1zmGudeNJruwhCv+vZQ9+acOpFyXVUC3uCg6xUQ1QQ2Dj19BRVVfU9VhqnqT8zlDVS91t2rGtAxpO/LolRB9YtS7iPD3K4bTIz6am19cXe036JoUlZRxsLCEZAsqtTq9ZwfmzRxLbnEpV/xrKTud5ZcrrdudX+2iXCbw/G2oTxKRN0XkoIgcEJHXnfVOjAlqqsqqnXmM6tXhpPTYqHCe+unpFB47zi0vreF4eUWdyt3Riub8agyjenXg5RvGcaS0jMv/tZRtzpxih4pL2XXoiAWVRuTv46+5eNYr6YZn2d53nDRjgtqO3CPkFpeS2uvUbq0Du8TywI+HsWLHIW5+cXWdFqnKqOz51crHqATSkO5xvDJrPBUKV85exqa9BSeWDx5mPb8ajb9BJVFV56pqmfN6DrC1eE3Qq2xPSU3u4HP7j0Z25+4fDuLzrQf5/qNf8uW32X6Vm+mMUUkOgu7EgTSgSwyv3jiOyLAQps9exssrdiECQ7tbUGks/gaVHBH5qYiEOq+fArm17SQik0Vkq4iki8gpi2aJSKSIzHe2LxeRZCd9koisEpENzs+JTnq0iLwnIltEZJOIPOBV1gwRyfZa1Ot6P8/NmHpbtTOP2Kgw+iZWP0Bx5lm9eesXZxLXJpxr5qzgnoWbOHa8+nmuwLMufff2bWrtAWVOlZLYjldvHE/76Ag+3HSAvontiIkKjm7ZzYG/QeVneLoT7wf24Vmu97qadhCRUOBJ4EJgEDBdRAZVyTYTyFPVvsAjwINOeg5wsaoOxTPf2Dyvff6uqgOBkcCZInKh17b5qjrCeT3j57kZU29pTntKSEjNI7UHd4vjnV+exXVnJvPckh1c/I+v2LS3oNr81vOrYXrER/PqjeMZ3qM9Fw7pUvsOJmD87f21S1WnqGqiqnZS1R/hGQhZkzFAutNTrBR4BZhaJc9U4Hnn/QLgPBERVV2jqnud9E1AlIhEquoRVf3MqVMpsBqwDgOmSeQfKSX9YBGpybVPEwKeMRh/vHgwL/xsDAVHj/OjJ7/mwQ+2sGpnHmVeDfmqSoYFlQbrEhfF2784k9svGNDUVQkqDVlO+Hbg0Rq2dwe8pxLNAsZWl0dVy0SkAEjAc6dS6VJgjaqWeO/oLBJ2MfCYd14RmQB8C9ymqqdMZSois4BZAD179qyh+sbUbNXOPIBTen7VZkL/RD789QT+7+2N/OuL7Tz1+XZiIsMYmxLPGX06MrhbLIXHyiyomBapIUGltpnZfG2vOm9FjXlEZDCeR2IXnLSTSBjwMvC4qmY4ye8AL6tqiYj8HM8d0MRTCledDcwGSE1Nbdg8Giaope3MIyxE6jX9R4e2ETx51ekcKi5l6fZcvt6ew9fpOfx388ETeSyomJaoIUGltj/IWUAPr89JwN5q8mQ5gSIOOASesTHAm8A1qlp1VZ7ZwDZVPXGnpKreHQee5rv2GWNcsWpHHoO7x9Emov6N6fFtI/jBsK78YJhnzbusvCMsSc8lI6eY8X0SAlVVYxpNjUFFRArxHTwEaFNL2SuBfs7aK3uAacBVVfIsxNMQvxRP4/+nqqrOo633gLtU9esqdboPT/C5vkp6V1WtXMxiCrC5lvoZU2+Vk0j+dFyvgJab1CGaK0bbmh+m5aoxqKhqTH0LdtpIbsEzZ1goMEdVN4nIvUCaqi7Es979PBFJx3OHMs3Z/RagL3C3iNztpF0ARAC/B7YAq521EZ5wenrdKiJTgDKnrBn1rbsxtTkxiWQd21OMae0a8virVqq6CFhUJe0PXu+PAZf72O8+4L5qivXZlqOqdwF31buyxtTBiUkkqxn0aEyw8necijHGS+UkkjbzrTEns6BiTB1VN4mkMcaCijF1VtMkksYEOwsqxtRRbZNIGhPMLKgYU0f+TCJpTLCyoGJMHfk7iaQxwciCiglaR0rLeGZxBvsL/F/ut66TSBoTbCyomKD13vp93PfeZs75+2c89NFWikrKat2nvoJMHV0AABxwSURBVJNIGhMsLKiYoJWZU0xYiDBpUBf+8Wk65/ztM/6zbOdJ09BXKiop47MtB3luyY56TyJpTDBwdUS9Mc1ZRnYxPROi+cf0kcw8qzd/fm8z//fWRuZ+nclvJw+kXWQYS7bnsGR7LuuzCiivUCJCQ5g2pkeDJpE0pjWzoGKCVmZOMSnO9PIjerRn/o3j+PibAzzwwRZunLcKgNAQYXhSHDd9rw/j+yQwqlcHW+LXmBpYUDFBqaJCycwtZkL/jifSRIQLBnfh3IGd+HDTftpGhDG6dzztIu2/iTH+sv8tJijtLThKaVkFKT7GmoSHhvDDYd2aoFbGtHzWUG+CUmZOMWCrKxoTaBZUTFDKyPYElRQLKsYElAUVE5Qyc4ppGxFKYkxkU1fFmFbF1aAiIpNFZKuIpIvInT62R4rIfGf7chFJdtInicgqEdng/Jzotc8oJz1dRB4XZ/lHEYkXkY9FZJvz00anmWpl5BTTO7Etzq+PMSZAXAsqIhIKPAlcCAwCpovIoCrZZgJ5qtoXeAR40EnPAS5W1aF41rCf57XPU8AsoJ/zmuyk3wl8oqr9gE+cz8b4lJlTREpHmxDSmEBz805lDJCuqhmqWgq8Akytkmcq8LzzfgFwnoiIqq5R1b1O+iYgyrmr6QrEqupSVVXgBeBHPsp63ivdmJOUlJWTlXfUGumNcYGbQaU7sNvrc5aT5jOPqpYBBUBClTyXAmtUtcTJn1VNmZ1VdZ9T1j6gk69KicgsEUkTkbTs7Ow6n5Rp+XbmHkEVUhItqBgTaG4GFV8Pq7UueURkMJ5HYjfWocwaqepsVU1V1dTExMS67GpaicqeX3anYkzguRlUsoAeXp+TgL3V5RGRMCAOOOR8TgLeBK5R1e1e+ZOqKfOA83gM5+fBgJ2JaVVsjIox7nEzqKwE+olIbxGJAKYBC6vkWYinIR7gMuBTVVURaQ+8B9ylql9XZnYeaxWKyDin19c1wNs+yrrWK92Yk2TmFJEYE0lMVHhTV8WYVse1oOK0kdwCfAhsBl5V1U0icq+ITHGyPQskiEg6cDvf9di6BegL3C0ia51XZRvJTcAzQDqwHXjfSX8AmCQi24BJzmdjTpGZU2x3Kca4xNW5v1R1EbCoStofvN4fAy73sd99wH3VlJkGDPGRnguc18AqmyCQkV3MpEGdm7oaxrRKNqLeBJWCI8fJLS61OxVjXGJBxQSVzFxrpDfGTRZUTFDJzCkCbIyKMW6xoGKCSmZ2MSECPeMtqBjjBgsqJqhszymmR3w0EWH2q2+MG+x/lgkqmdnWndgYN1lQMUFDVW2MijEus6BigsaBwyUcPV5uqz0a4yILKiZoZJzo+WXrqBjjFgsqJmjY7MTGuM+CigkamTnFRIWH0CU2qqmrYkyrZUHFBI3MnGKSE9oSEmLr0hvjFgsqjSS7sITcopKmrkZQy8wptpH0xrjMgkojuf6FNK6duwLVOi1UaQLkeHkFuw4dIaWjNdIb4yYLKo0gr7iU9Vn5bNxzmK/Sc5q6OkFp16EjlFeoNdIb4zILKo1geWYuqhARGsLsLzOaujpBKbOy55c9/jLGVa4GFRGZLCJbRSRdRO70sT1SROY725eLSLKTniAin4lIkYg84ZU/xmslyLUikiMijzrbZohItte26908t7pYsj2X6IhQfjmxL4u35bBxT0FTVynoVK5LbwMfjXGXa0FFREKBJ4ELgUHAdBEZVCXbTCBPVfsCjwAPOunHgLuBO7wzq2qhqo6ofAE7gTe8ssz32v5M4M+qfpZsz2VM73iuOSOZthGhdrfSBDJyiukQHU776IimrooxrZqbdypjgHRVzVDVUuAVYGqVPFOB5533C4DzRERUtVhVv8ITXHwSkX5AJ2Bx4KseOAcPHyP9YBFn9Ekgrk04V43tyXsb9rH70JGmrlpQycwpsvYUYxqBm0GlO7Db63OWk+Yzj6qWAQVAgp/lT8dzZ+LdnepSEVkvIgtEpIevnURkloikiUhadna2n4eqv6UZuQCc0acjANed2RsBnv0q0/VjB5sFq7JYtzvf57aM7GKbnsWYRuBmUPE1wqxqf1p/8lRnGvCy1+d3gGRVHQb8l+/ugE4uXHW2qqaqampiYqKfh6q/Jem5xLUJ57SusQB0a9+GKSO6MX/lbvKKS10/frDYV3CUO15bx4+fWsLjn2yjrLzixLaikjIOFpbYnYoxjcDNoJIFeN8tJAF7q8sjImFAHHCotoJFZDgQpqqrKtNUNVdVK0cXPg2Mqn/VA2dJRg7jUuIJ9RrFPWtCCkePl/OfZTubsGaty9LtnjvCsb3jefjjb5k2e9mJR4w7rJHemEbjZlBZCfQTkd4iEoHnzmJhlTwLgWud95cBn6p/owOnc/JdCiLS1evjFGBzvWrtp4qK2qu5+9ARdh86euLRV6WBXWI5Z0Aizy/dwbHj5S7VMLgs2Z5L++hw/jNzLI9eOYIt+wu56LHFvLVmDxk51p3YmMbiWlBx2khuAT7E8wf+VVXdJCL3isgUJ9uzQIKIpAO3Aye6HYvIDuBhYIaIZFXpOXYFVYIKcKuIbBKRdcCtwAwXTguA+St3cf7DX1BSVnNAqPz2fEafU5uJZk1IIaeolNdXZ7lSx2CzdHsu43onEBIi/Ghkd97/1dn07xLDr+ev5f73vkEEkhMsqBjjtjA3C1fVRcCiKml/8Hp/DLi8mn2Tayg3xUfaXcBd9a1rXXRr34aMnGIWrt3L5ak++wMAsGR7Dh3bRdK306kNxONTEhiWFMfTX2YwbXTPkx6PmbrZfegIe/KPMmvCd78WPeKjmT9rHP/8fDuPfbKNpA5tiAoPbcJaGhMcbER9PZzVtyMDu8Tw7FeZ1c7lpaos2Z7LGX0SEDk1YIgIsyaksCP3CB9/s9/tKrdqlXeE46vcEYaFhnDref1455az+OdVzaKJzZhWz4JKPYgIM8/qzZb9hdXO5bU9u5iDhSU+H31Vmjy4Cz3jo/nXFxk20WQDLM3IpWO7CPr5uCMEGNQtlqFJcY1cK2OCkwWVepoyohuJMZE8vdj3eJOl2z3Bpmojvbew0BBmTUhh7e583t9odyv14bkjzGFciu87QmNM47KgUk+RYaFcO74XX36bzdb9hadsX7I9l+7t29Ajvk2N5Uwb3YPTusZy7zvfUFRS5lZ1W63MnGIOHC455dGXMaZpWFBpgJ+M7UVUeAjPfnXyXF4VFcrSjOrbU7yFhYZw/yVDOFB4jEc+/tbN6rZKlTMWjE+xoGJMc2BBpQE6tI3gslFJvLVmL9mF363quHn/YfKPHOeMvv79oTu9Zwemje7J3K8z2bTXZjCui6Xbc+kcG2mj5Y1pJiyoNNDPzuzN8YoK5i3dcSLtRG+klOrbU6r638kD6BAdwf+9tdGvgZXG056yLCOX8daeYkyzYUGlgVIS23HewM7MW7bzxOj4JdtzSUlsS5e4KL/LaR8dwe8uOo01u/J5ZeXuavMdKS3jnoWbeOD9LUEffLYdLCKnqLTGzhDGmMZlQSUAbji7N3lHjvP66iyOl1ew3GlPqasfn96dsb3jefCDLeQUlZyyffO+w1z8j694bskO/vXFdn6zYD3lLTiwHC+v4PrnV/JqWvVBtCbVjU8xxjQdCyoBMKZ3PEO7x/Hs4kzWZ+VTXFper2/PIsL9lwzhSGkZf1703dRlqsq8ZTuZ+uTXHD5WxovXj+W28/vz+uosbpu/9qQZeVuSpdtz+e/mg/zv6+tZtGFfvfb39LCLdqF2xpj6cHWalmAhIlx/dm9+9cpa/rJoCwDj6tkbqW+nGG44O4V/fr6dK1J7cFqXWP739fV8sGk/3+ufyENXDKdju0jO7NuR8DDhrx9s5Xh5BY9NG0lEWMv6jvD+xv20jQhlYNdYfv3KWtpHh/sdjCsqlGWZuZx/WmeXa2mMqYuW9VeoGbtoaFe6xUWRtjOP07rGEt+2/svW/nJiP5I6tOHO19dz0eOL+e/mA/zuooHMnTGaju0iT+S7+Zy+/N8PTuP9jfu5+cVVtU5w2ZyUVygfbdrPuQM78ey1qSR3jGbWC6vYuMe/3m9b9heSf+S4dSU2ppmxoBIg4aEhzDgzGfA9K3FdtIkI5d6pg9mRe4TQEGHBTWcwa0IfQnxMOnn92Sn8aepg/rv5IDe8sKrFTKW/IvMQucWlXDikK+2jI3j+Z2OIjQpjxtyV7MwtrnX/Jc6MBdaeYkzzYkElgKaN6cm5AxK5ZGTVVZPrbuLAziz4+Xjeu/UsRvRoX2Peq8cn8+ClQ1m8LZuZz6+ktKz5t7F8sHEfkWEhnDPAs/pm17g2vDBzLGUVFVwzZ8VJ4358WZaRS3JCNN3a1zxjgTGmcVlQCaDYqHDmXjeGId0DM3lhanI8MVHhfuW9cnRP/nrpML5Oz+WxT5r3yPyKCuWDTfs5Z0AibSO/a9br26kdc2eM5uDhEmbMXUHhseM+9y+vUJZnHrK7FGOaIQsqrcjlqT24fFQST32+nbQdta7KXC/lFcqDH2xhZQPKX7M7nwOHS7hwSNdTto3s2YGnfno6W/cXcvWzK04sBext094CCo+V1bszhDHGPa4GFRGZLCJbRSRdRO70sT1SROY725eLSLKTniAin4lIkYg8UWWfz50y1zqvTjWVFWz+OGUw3Tu04bZX17oyQeXyjFye+nw71zy7guXOvFt19f6GfYSHChNP6+Rz+zkDOvHEVSPZnl3E5Me+ZM5XmScN9PxuxgILKsY0N64FFREJBZ4ELgQGAdOrLAkMMBPIU9W+wCPAg076MeBu4I5qiv+Jqo5wXgdrKSuotIsM45ErRrAn7yj3vrMp4OW/tXYPbSNC6dY+iuueW8mqnXW7Y1FV3t+4n7P6diS2hkd7k4d05ePbvsf4lATuffcbpj297EQD/pLtufTt1I5Osf7PWGCMaRxu3qmMAdJVNUNVS4FXgKlV8kwFnnfeLwDOExFR1WJV/QpPcPGXz7LqX/2WKzU5npvO6cOraVl8uClw67QcO17O+xv38/0hXXj5hnF0jo3i2jkrWbMrz+8yNu45zJ78oz4ffVXVJS6KOTNG87fLhrF532EmP7qYOV9lkrbjkN2lGNNMuRlUugPe829kOWk+86hqGVAA+PPXYq7z6Otur8DhV1kiMktE0kQkLTs7uy7n06L86rz+DOkey11vbOBgYV1ic/U+33qQwmNl/GhEdzrFRvHSDWOJbxvBNXNWsD4r368y3t+4j9AQYdIg/wYtigiXp/bgo9smMDYlnnvf/Ybi0nJrpDemmXIzqPi6S6g6UZU/ear6iaoOBc52XlfXpSxVna2qqaqampiYWMuhWq6IsBAevXIExSVl/HbB+oAsV/zWmr10bBd5YhxO17g2vDxrHHFtwrn62RW1DlysfPQ1PiWBDnUcHNo1rg1zZ4zmr5cOY+LATpzdzyaRNKY5cjOoZAE9vD4nAXuryyMiYUAcUONDelXd4/wsBF7C85itXmW1dn07xfC7i07j863Z/Gf5rgaVVXD0OJ9uOcjFw7sSFvrdr0339m14+YZxtI0I5epnl7N53+Fqy9h6oJDMnGImD+lSrzqICFeM7sGcGaP97mptjGlcbgaVlUA/EektIhHANGBhlTwLgWud95cBn2oNX6lFJExEOjrvw4EfAhvrU1awuGZ8Lyb0T+T+975h24FTlz321wcb91FaXsGPRpw6sLNHfDQvzxpHZFgo059extrdvh+Fvb9hPyJwwWCbr8uY1sq1oOK0a9wCfAhsBl5V1U0icq+ITHGyPQskiEg6cDtwotuxiOwAHgZmiEiW03MsEvhQRNYDa4E9wNO1lRXMRIS/XTaMdpFhTH96eb0Dy1tr9tK7Y1uGJfke2NkroS3zbxxHbFQ4Vz29jK+25ZyS54ON+xndK55OMdZry5jWSoL5y3xqaqqmpaU1dTUaxbYDhVz1zHIqKpT/XD+W07rG+r3v/oJjjH/gE351Xj9+fX7/GvMePHyMa+asICO7mMemjeDCoZ5eXtuzizjvoS/448WDuO7M3g06F2NM0xKRVaqa6mubjagPEv06xzB/1jjCQ0OY/vQyv2cDBnhn3V5UYaqPR19VdYqNYv6s8QxNiuMXL63m5RWetpwPNnq6Nte3PcUY0zJYUAkiKYntePXG8bSNCGP608v8Hl/y1to9DE+Ko3fHtn7lj4sOZ97MMUzon8hdb2zgqc+38/7GfYzo0Z6ucTYBpDGtmQWVINMzIZr5N46jQ3QEVz+7otY5wtIPFrJp72G/7lK8RUeE8fQ1qUwd0Y0HP9jCxj2HudDuUoxp9SyoBKGkDtG8euN4OsVEcs2cFSxJP7VRvdJba/YSIvDD4bWPgK8qPDSER64YwYwzkmkbEcpFQ+tehjGmZbGgEqS6xEXxyqxxdG/fhqvnrODxT7ZRXnFypw1V5e11ezizb8d699gKCRHumTKY1X+YZGvJGxMELKgEsU6xUbx+8xlcPKwrD3/8LdNmLyUr78iJ7at35bH70FGfY1PqKjIstMFlGGOaPwsqQS42KpxHp43kkSuHs3lfIRc+tpiF6zwTH7y9di+RYSF839pCjDF+Cqs9iwkGl4xMYlTPeH41fw23vryGL7Zm89nWg5w/qDPtIu3XxBjjH7tTMSf0TIjmtRvHc+vEvry5JotDxaUBefRljAke9hXUnCQsNITbLxjA2f0T+WzLQc4Z0HpncjbGBJ4FFePT6OR4RifHN3U1jDEtjD3+MsYYEzAWVIwxxgSMBRVjjDEBY0HFGGNMwLgaVERksohsFZF0ETll0SwRiRSR+c725SKS7KQniMhnIlIkIk945Y8WkfdEZIuIbBKRB7y2zRCRbBFZ67yud/PcjDHGnMq1oCIiocCTwIXAIGC6s3qjt5lAnqr2BR4BHnTSjwF3A3f4KPrvqjoQGAmcKSIXem2br6ojnNczATwdY4wxfnDzTmUMkK6qGapaCrwCTK2SZyrwvPN+AXCeiIiqFqvqV3iCywmqekRVP3PelwKrgSQXz8EYY0wduBlUugO7vT5nOWk+8zhr2hcACf4ULiLtgYuBT7ySLxWR9SKyQER61Lfixhhj6sfNwY/iI03rkefUgkXCgJeBx1U1w0l+B3hZVUtE5Od47oAm+th3FjDL+VgkIlurOUwcniDn77aqabXl6QhUv5BJw9VU/0DsV1u+6rb7c+18pdm1C8y1g+Z5/Zri2vlKb4nXri771fXvWnXbelV7BFV15QWMBz70+nwXcFeVPB8C4533YXj+scRr+wzgCR9lz8ETUKo7dihQ0MD6z67LtqppteUB0ty69rXVPxD71Zavuu3+XLvarqddu4b9LjbH69cU187Pa9Xsr12grl99t1V9ufn4ayXQT0R6i0gEMA1YWCXPQuBa5/1lwKfqnEF1ROQ+PFHz11XSvZcVnAJsbkDdwXPnU5dtVdP8yeOm+h7L3/1qy1fddn+viz/X0y127RqmPsdrimvnK70lXru67FfXv2t1Ld9zV+AWEbkIeBTPncMcVb1fRO7FE+0XikgUMA9PT65DwDR1HmeJyA4gFogA8oELgMN42mC2ACXOYZ5Q1WdE5C94gkmZU9ZNqrrFtZNrIBFJU9XUpq5HS2TXrmHs+tWfXbvauRpUTPVEZJaqzm7qerREdu0axq5f/dm1q50FFWOMMQFj07QYY4wJGAsqxhhjAsaCSgCIyBwROSgiG+ux7ygR2eDMf/a4iIjXtl86c6dtEpG/BrbWzYMb105E7hGRPV7zwF0U+Jo3Pbd+75ztd4iIikjHwNW4eXHpd+9PzgDstSLykYh0C3zNmzcLKoHxHDC5nvs+hWcwZj/nNRlARM7FM43NMFUdDPy94dVslp4jwNfO8Yh+Nw/cooZVsdl6DheunTMbxSRgVwPr19w9R+Cv399UdZiqjgDeBf7Q0Eq2NBZUAkBVv8TTjfkEEekjIh+IyCoRWSwiA6vu54ytiVXVpc74nBeAHzmbbwIeUNUS5xgH3T2LpuHStQsKLl67R4Df4sfsFi2ZG9dPVQ97ZW1LK7+GvlhQcc9s4JeqOgrPbMv/9JGnO5450Sp5z4/WHzjbWRLgCxEZ7Wptm5eGXjuAW5zHEHNEpIN7VW12GnTtRGQKsEdV17ld0Waqwb97InK/iOwGfkIQ3qm4OfdX0BKRdsAZwGtej6ojfWX1kVb5zSYM6ACMA0YDr4pISm0zDrR0Abp2TwF/cj7/CXgI+Flga9r8NPTaiUg08Hs8A42DToB+91DV3wO/F5G7gFuAPwa4qs2aBRV3hAD5znPVE8Szxswq5+NCPH/8vKfuTwL2Ou+zgDecILJCRCrwTGaX7WbFm4EGXztVPeC139N4nm0Hg4Zeuz5Ab2Cd80c1CVgtImNUdb/LdW8OAvH/1ttLwHsEWVCxx18ucJ6rZorI5QDiMVxVy70aj/+gqvuAQhEZ5/QeuQZ42ynmLZxZlkWkP57patycHbVZCMS1k5PngbsEqHPvnpaooddOVTeoaidVTVbVZDxfbE4PkoASqN+9fl5FTsEzpVRw8XfmSXvVOPPny8A+4Die/4gz8Xzj+wBYB3wD/KGafVPx/NHbDjzBd7McRAD/cbatBiY29Xm2oGs3D9gArMfzzbJrU59nS7l2VfLsADo29Xm2pOsHvO6kr8czCWP3pj7Pxn7ZNC3GGGMCxh5/GWOMCRgLKsYYYwLGgooxxpiAsaBijDEmYCyoGGOMCRgLKsZ4EZGiRj7eMyIyKEBllTuz424UkXdEpH0t+duLyM2BOLYxlaxLsTFeRKRIVdsFsLwwVS0LVHm1HOtE3UXkeeBbVb2/hvzJwLuqOqQx6meCg92pGFMLEUkUkddFZKXzOtNJHyMiS0RkjfNzgJM+Q0ReE5F3gI9E5BwR+VxEFojIFhF50RmJjZOe6rwvciYjXCciy0Sks5Pex/m8UkTu9fNuainfTRLZTkQ+EZHV4lkDZKqT5wGgj3N38zcn72+c46wXkf8XwMtogoQFFWNq9xie9VlGA5cCzzjpW4AJqjoSz2y0f/baZzxwrapOdD6PBH4NDAJSgDN9HKctsExVhwNfAjd4Hf8x5/i+5pg6iTNX1Xl4ZhMAOAZcoqqnA+cCDzlB7U5gu3qmH/mNiFyAZ22QMcAIYJSITKjteMZ4swkljand+cAgr5lrY0UkBogDnnfme1Ig3Gufj1XVe62OFaqaBSAia4Fk4Ksqxynlu8kvV+FZKAs8AapyvZOXqH7BtjZeZa8CPnbSBfizEyAq8NzBdPax/wXOa43zuR2eIPNlNccz5hQWVIypXQgwXlWPeieKyD+Az1T1Eqd94nOvzcVVyijxel+O7/97x/W7Rs7q8tTkqKqOEJE4PMHpF8DjeNb1SARGqepxEdkBRPnYX4C/qOq/63hcY06wx1/G1O4jPOtiACAilVOjxwF7nPczXDz+MjyP3QCm1ZZZVQuAW4E7RCQcTz0POgHlXKCXk7UQiPHa9UPgZ866IohIdxHpFKBzMEHCgooxJ4sWkSyv1+14/kCnOo3X3wA/d/L+FfiLiHwNhLpYp18Dt4vICqArUFDbDqq6Bs9Mu9OAF/HUPw3PXcsWJ08u8LXTBflvqvoRnsdrS0VkA7CAk4OOMbWyLsXGNHPiWZHxqKqqiEwDpqvq1Nr2M6YpWJuKMc3fKOAJp8dWPkGwNLJpuexOxRhjTMBYm4oxxpiAsaBijDEmYCyoGGOMCRgLKsYYYwLGgooxxpiA+f9uq9UvVMZsvwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"learn_big.lr_find()\n",
"learn_big.recorder.plot()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>accuracy</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.028620</td>\n",
" <td>0.021049</td>\n",
" <td>0.993000</td>\n",
" <td>00:20</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"learn_big.fit_one_cycle(1, 1e-100)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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lFXE/YqY516ZF3g+en3Dff3bGPNm4In47Ix/PQ3zZ3GY7UxwnL8d2LZc5p/PcMhnlBMcxZVDudyvk2nsSxsvPYYHX0xf3V70p95xtg3Erx3lUMZ5CnA13ePwKa8SwEs+L/7YtrrEnU47L6z7njTvmPIgmrKE1g3v6fJLtKmTY31bqkY9D/ozcMtfcvnAbH/msK/Y54+IrzL/SNutFaZk1uWlxT9XMssYu4uiFP0a8n2B/fFa4TpdtPm+5mWCcXxN7jeU3IS5P34U46/N+ehF7f8r+GBaY98ll1tdmyP48n7CutHzOfzdiu28m+bxhNRnPTdm6zzV3KubrRDiczSLzcUU4NQvCvWqL33pJOkI0HjM/e/fEczWD5xkafF7jHPNeN+/eYjueejfi6LK4V2myXdNhvL1IJH6vY3rOcRzdYt2ePM/+c7+I41Fq8LprZ9z7nQkX8KAXz5GtnyRVFEVRFEVRFEVRFEVRFOVCow9JFUVRFEVRFEVRFEVRFEW50OhDUkVRFEVRFEVRFEVRFEVRLjSPlBycUaVjTMd0NywJ35hVolMhXaMzIBSiu2BGh81yi+8vDvj6IsZX+L6sRzfM0oj+splPB0ghousgpOrIEM00BlfoIHFX4jkYntq6hNj3+Yzaddk/+UB4qEI6F7IjOlY2Xbo2ZmJ8ZkY890a6SRdEu09XxGGbDpFWX7iiGuyfYpL9Xd2iS2dZ+PoKRjyHzblPX+PAo8vEHNAZMgh43rsGHT+Gy+sKhsLlMeB1ujk6W6593eK29oU3JZ1jzj39JrrPpqfC8fYGc8PxOFf8FY5tNc04tRqvT70l+ouKZTF2DtsxHnNunZ/y9cE+/Sa9Jn1Hffc64qAYzwHnCo9wlOd5c8I/tGLzOvIOPTX7wk3ZHXPs77cYT8Yc++cXtHOrynFIDujTGpzsIx726VkKMvx7K8GcTQr3otSozSzmySLODZ533ONx2z06NKuXxFxbYz5bLouo49MPUxcOxKxBf88iHpjCyzVhDe0PmReew+P2s4eIK0m2s2KK/JPiMIcOz0W8vfZWxJ2A+XU8Y7uaHcZnp3uIXYP9t5XkvHOXmVd+QXqd3zG3ncUleoEii7U4THLcgwznVb/DNabZ4fz2O/QdZVrMm0I3nnc8UeT8tdNsp5NjfwwbbFfjgI7Mk6bwpvmsB1aOa61V4/kXYWfZX6kM51G2Kty8D+h7e+lz7L/AEY7hdTolDYvHn4o9wcJ2trh2ZoSfKXClm1LU1wH3COd15vcwx3YkC+wXJ4jndD4Urt2U0Ehl79BPtpTnPKg8znzLC4dxMWI9MW3mTTqK5626+pi4npDXf9ymB2x6QG9+MGJ+hjPOd/My12VHOMnzpXifd5i6vP6kz9gR45wQ/jYjy/6J6jxvlOV6k2gIF/Mgnu/RMAxj5zL7aCoc/qM+3WhWlnM4VWQfRsK/mgi55rkmc+UoQYfpwnaOeFxfKEKP2uwzwxQuRJvnsSM5h7l3S6ZYiyybY7iIXK4t/oV7i6jFWjydcU6f57i5KCd53U+uc23cb9PDfz6Ot7fL+jxOIhKeYId5kBNebEfsrTtj5vDDM47HOwO+fikdb00aHnCPLscln2V/Rx329+Cc+XZaEnvrgviNCXG/kknEc5J27jM/BlOe1xW/f5A8YP8tHe8gTuc4h6MCa0BC3GclRvHauWNybUvnmAfeJh2v5jnb6fe4ljaFQ7eb4Z4oa7Je5KJ4zxvSs03EGVHrczbXjPMOveuHr3PNisTvKSRKHK9UmvellbSoJwvwxJpui3uBepLxSkE8jxFrTLXA5xdVh07eZILXYVnxfgfjXv51xLsu++vlW9wThWKt85e59qZT3AvmTebrJM38byYZL2K5xPk78plfCXFPPhPPc4Ie29kRvzFzIn7nwRB1zVyOV+e7u9xLjLO8vm6V68/lDPvnepHtKNVYxzJZ3rNOu7x38Q7jObL9Mdfnvsu97pnHurzykP1f+1N68+tlzsNSTjwjSbJ+GIl4e3r9JKmiKIqiKIqiKIqiKIqiKBcafUiqKIqiKIqiKIqiKIqiKMqFRh+SKoqiKIqiKIqiKIqiKIpyoXmkk/TOS3RKlBy6Da4Kx00mz9ftZToCdmd0BJxHdCysCr9hoRvP9endpPPEntGNsSL8ePaMlz0J6Hop1+nySFbpqLknVJbn8ZQrxnufeBKxGc1EzPNOXToUJpMlxI+5bFcwpY+r7XN8RkE8J2l4+ADxwKVP88GQfq3mgOM0HNC18cQ7Oa71p/j6JYduiKWYjqXbsxcQh13mm3NAB00/xTx4uXQZsT2m0yY4o/Pi/sv8P4Vz4dD40COcpCc+PR4by/SAvOnpLcTJP+LYDX/7LuJcSH9M32Rb6iX6UjaXpKNwATscq5TD81TEeQfUehkPH7AGtM4+x/cPPos4F9JTlZncjNXMVkRPzzhNf4ldYe1aLfH6M6ucW/5rHNvhHvvzlSk9Ky9YvM7vXNDOlVV6vfoe23FwKrxfnsjBZbarYHDuZAxeRz/gHJhE8Vy0Z8J71G8yP0/vsOgt59juWZG1y0vsILYCepxq9ucRr4SsKYt4Mc12jFvsT+8B2zWqizy5wnm4ZrAG1FLXEAcr9OYY1XguxQ9u/KeIb804f3/f/XeIX3uJ/r27n2LsVenfmy7R31VICvenzZptGN84t52rK3Su+lnmvVdhLW41edyzh/QfnTU4ruMm15I3D1mDl6fCE7SAaJ1rhHSnGibHpdFiPr30mZcQHzq8rkmR9beQ4x7CrsX7/2QrK+onQ6Mo3NFnD+kj/P0/fQ3xW77ofYifvvEWxKbJvczoPJ4PKnf6CuJsm3leHtE/5iTpQcuYe4iDZe7tgjLbVRJ7u2IQz+37oMy/yx2zzlRepb9suUI/1tp7uSeKhMN5lmY7UknmTd+iw3gRb3sLj3N/nwvjJz/P+W/eoWetvMvzhu/nOCffzr2fnea6mZW+rQU0J1wX0yHrZ+CL3xUoCF9hbQ9xX+wXJjXhixReMKctNs+P4LFNzo3AZ232Xdbu9MYOzz2k+88+pSPT7nCtPUhzb3KapE99EW/32Pd7M665n/aYg9OAtTo14NjL+4Cx2NstVbhHymfFjygsoJZmLnsux3r4gLX9PMncOHN4XTtltut6nrlgzd6EuBBzX18XTlIj4pzt29ybJB3u/arCm9sZ8br2zri3zgZ8/5V0vBp6eucOz1vn9dUv88awc8rzHt5ijXjlMteg0611xO8SXv2rVrz+bL0of/uC941ej3ukfJP9vX3+NGLrunCBrjJPHI9z3HHjuf82fO5pcsIt61wRjtGI49oXnvFTkRfjEufV40mer2TQw72I9JAuzoJwJ9ZCrkG7bc67T73OemCuca1Mi7zJlfj+Uibenj6a8jhF8TjlCXFLWF3l/F16kv2x1me9rQ6ZnxOfB5z63Bsaxo257by3/SLi1gPWw4dvvIy49jjr3eVn+VzlqQTn77LFfO87zINmMp53fGuTxxkMuLbZDc6b1oT3ah3hLD0rct1wcozzwvWbrsd7Htbdp29+ssR5NCgzrtQ57m/d4l4jWuU6Ok0KB+k569j0NvN1Ef6Ie87ulNd71+ZeOb3LuHyP6/PqF/M6qm/iHr6fpIN4mlQnqaIoiqIoiqIoiqIoiqIoyv8n+pBUURRFURRFURRFURRFUZQLjT4kVRRFURRFURRFURRFURTlQvNIJ+l5my9HDp0LNZff+T8e0k2w/4CuiUz+OuJ0gQ6R7k2er3853jPcd/foFsqZwuWQpBvCEQ7S0pRx06WLo0uVjDFt0U2RGfE6jA/Ob6f9Bj2GozydMudL7E8zRYdF2qYLIh/RzZOe0SUxtOgM8RLxnBafdz6NuOnT8XEasR3lFZ7n8Zv03N3coD/ucorjHJgiD4x4/sxak46JkfA9NjN0jdguz5M6FM6ULF0vZpr9f8Pi+FxPcz48ik97HPsN4e2ZCL9pc4nHtt/FsT532LZ+wGtdE57XciGeC2xFeH6mfXpAPn1G/06rybnQEfHajauIn7lEn0wqQQ+LnaAfaRH7HtsZDNhf3ohzPr8kPD9XmQtPZBhvC83X0QP6TjpN9v8i7lv0qA0T9ME0fcatLHOsVaafazBgbcuMmVeFJPOoaMab8+9xnkK8nxEeqxxr1cMGfXH/2x/TcbjusGaspejky+aZr6k08+b5Be3cG9BP5AtXrJulo7BW5XXcWKFr8ZpDL9VaguNsNnk8+0D4i76Anrv/h45F96lRZP9t1d6NeJrZQ5xM0XeW7/I6PZ+eoK0U5/dSWjg7F/B7u1yjgyHnd3BOX1sk/NSG8Lklt5jPy9e4Nky77N+zSbwaujnl3mE2Yjtvn38C8e4B3bT3Z6yfq2XWh5s1tiNfZH87uXiepSWfa1JwxDXp1i16AJuHzPvqFjcbK08yf9eeZn1Iv848sw/FZsV4z9x2Nkw6Lachj+tOuX5MxN5hHLFeFvrsz7LNulVM00lcXuX5F7G+QW/VKM/1YTfPfOyWWGcevMZ593yWPsdrKeZnsirGuRBvHv2bP3yV7TBYJ46LHOeb72C9fesXfDnicVZcd4ftGLsc993pbcTvfMsXzm3n+T3O18BkPkrfWKrF9SVzxP1A4XXO94yYzud54c1Px3SjG4bx+pRe5pnLNXzSo4d4Wygan6gIV6WowZdmnFvFNl2Dw7HwUS9gP3cFsR9yH/lkyD4binYMS2xnqcEctMUebH/IuTdOsVZ97YJ2nmY4FgmDf2ca7G8zJfzbDmvb6Yh7oGabc9FMCEdnIp5Dc3jGnCuJve5ajnngNTiOd+6z1i+lWGve/TT3AFlzB3HrhO7F8gJN/md9zpUSQ2N/LO7fupwrdp+T5bGAa+NVm/mXnrKmNoJ4e/qoSB/3ULj+dmdsR1jlWrCc5XkGdXqKPbE2FoT7L23XY7XzlSP6zFPi/rh0hfEgx3lwXuBeuyXcv6OI+d4QjkjD4/gsYjQV/m2T/ZWpcJzsMp9P+Dnm9zTg/bA7YP56Fmu0Ecb7bZGOxzW5UuaafmWTr88qXGNaE74/HHFP1RnxHuBsxr1HP2B/P2l88dx2LvlvR5yvc36W3s15UNpi/6ysbPD1CeuWOWZ9TZ2zXkRuvN8ZaFTpPh1mmffNDPcmPfGbMIOQz8daLvcImX3md3hJzMuYTuedDbqgrRL3bskc42s5zteowPkaNLguuE3ee5yK3684EnvgRRREHq9GwnUsbl0zBvtzL+K9Wa/BPWZxl/d2piNcrFvx6qd+klRRFEVRFEVRFEVRFEVRlAuNPiRVFEVRFEVRFEVRFEVRFOVCow9JFUVRFEVRFEVRFEVRFEW50DzSSXrSFe4G4RuqCsfjgfCTvezRxXHtMbo61nboBGiX6PrwHtm6P+fKkI4UP0lHyCSiY9QQ/sfkgHFrxnY9TPDCiw/o9nEa8Zyk7kO6RTo1OkCOU/Qzpop0VhTTdDKkArp5Ui4dIpFwaAZmPFfEHeMW4pFFd0TPoVdqe4MOizffpOvkqsXrrIu02w3YLy0/nnOlOOC4+hbb5RdEf0zZn06Xro3Q4vsTwuu3lOLrhQT791HcNenDGAgfhzVj26wa/SPZt9DL0jpjzjaG7MN0im23c/H8GyWDYqWGmON3ztju2VB4X0O2Y31b+GrfKuZin14Xy6W3ZRHHBj0z5pCeH3NP+GfF5YdV5uB2XjgJl1j7Xh/x/Wf9eH6YvYCelFFIT083Qa9K3xHepSLnkil8SBmXcVmkZNmJ5059LkufU6bMGnewwnYfCc/Pw9sPEN+osv+nVY5rQXjC0g59XIto9ejqTBjMN6vMGlmv0xf31DKvs5rmmpQVjtj0LufDZE+IxxbQsA8RB0X6oje2HkOcsumxK0UPEQ/eEC6/E86bUpL1Yc2O51n6zC5rfXTK4yZu07NUttmOapHt3lynT2pphX5qN8151JjEc+YWevQLNVp0T+7fpxPysM1a3beZb0/nWIeeKnP+B8LzFyXo41rYTpdrYfeM/qb9W+zfocf+W7vM/li/xDV+ZZnx9PPcc4XSs72Aocn5NrbZX11buJNNtlNW6edmrFPXZ9xrVCuMi5l4e5GNZeG7KrE/d6vMx/Mp94J3Dvj6cpay6SsFOllxqqYAACAASURBVLND4UqNKuyXRbzwOfrvonXhF3uCeV/Ypvfw6sZ7Ebd3uZ6dizrUFq7k5pB1cRH9Y9btgc353UyzHtgR8zETcJx3eszHJZ/X2X2S+4XxKvPoUeyPxRoyYW0fjnnNuTxzNshy7AsFWSNZ+yuR8PT6Qky+gEaR76sIX+8Vg2vpKMdacpYV/l6DuZOc8O9bwqXasOLtl8+FCzEbMi5HwrftcA0JCxz7bpe5ETaZW+tFjkcpHc/5GLRZe5Ml/l25yM3cSZ85ePg6+2epzjXo2ibHJ23y9U5L7FEXcGSyv7piGPrCSboqHJEbSY77Vpq1rpRm/+9PWDNbE75/EYHDvdko4L3BWYabxlKK8arBdvoVVv8wy37ICWdsPhXvPunhCdu5vMXau1Rnu60ca80wzVo+OmZ+D88Zd4Tj2JrGe+AwEe7SUMyTbJl7vVSda21QY6IEtljTXb7f9TjPZjavYxFjQ7hl04y3l1gPT5KsO/sD5q875F7DGtFBfGTweU87wfcvYmVGP7dR5HyMhIM0XWJeZDPMz7wn6tiM9dNqi3vjLvt/EYPyFuKhz7XTzXNvN5uIPJmwPvbbvHc57TNOCHd1kIp3z7m6TH9/Lsv8qQrNesVmPfUt1imvyb3N4IHwwPd2ELfDeC7vVMD1uWSwLidT7L9JwL3fWYJe9kaT/ZO2uae/fln8XkIx3jzST5IqiqIoiqIoiqIoiqIoinKh0YekiqIoiqIoiqIoiqIoiqJcaPQhqaIoiqIoiqIoiqIoiqIoF5pHSjjuSFfkhC6C8ewVxKVVugzeeek9iE3hvPCGdPMctXcRt8f0LhnPfGhuO38v4PuCAZ0p0y49SImmcJu06BTpp4STwaYjpO7SWVOYxfO/fSJPh4Ix5HHDz9HJ0FmiM+HhBp0Mq/fprSuf0b3RpmLJGMVT2BiFE7pIkkU6Jqwr9MNtXhPOnxv8+9QeT9w9pnv11Kdr6lR4LRfhVtl/UZqOi1yB+VbMs125FN0lUU64NDK8roHB6+4ZMTvUMIz1Kzx2JSc8KeYbbKvwhuRqdBgmO8zxgU9nWUV4raqW8OYuYGLfQ5wo8xprOfp26g49SZsOPSCOQUee++mXEPs+xySUyrL3GnNxV5lDps9aYhkfRzw+pbdm+Fm2u7/KdiSKYg48Rr9OsRbPA9bcY072hT+5UX0RcTQWefGQvpyCR8dp3eJ1ra7TH7O8zJq9CGeJ+bJUYw188grjrdETiGdDtnsrSW/QmkXvTdZmDU2Z8bxVq216fYpLzMe1a29nO9a2EW8W6Mw83GdNvnVC1+UoZP5P19kPi7ib5nysCR/R9gPW8m3hf5qaTyK+X+Aa1/LoP6oE9A5l+8LLtIDqPeZHssDC47yX47xkct6tR8y/WsR8z4j83yuwDvXz8Tx1L37+NcSzKdeIcMjFbkcc9/ENtmNJ+I68kHXr4Iz+qU6b3sEv/tL57ew2mNejCccpKHH+r+RYh67Uvxrx5S7zu/RHXCvbh8xHV9SHRWxEbFe3xMI7Ey5Je8yt4vKI/f38NcbPXmc/mE3me2IofWVPGvNYTQsP+IzraH1K5/BDm3m/X+B1HE7ovftUh3mQkO5uO56v7J3Zt/EfpHKVzTQqYp7ce/3fIj455+ttMZ/lelezOC8XkVkWG4sp/y50WR+jJMfNzHLedW3WD1csi8GA88qP6f0zDMNIHvP+wU5zjUlX6K6chuyT2+fC8dZhn6YN5qDb5Rz3p6xti1jZZB9lJzyv0xW/JSD2agVR+4062xEscW28HjEnN6N4rrphVXjre8yF/NnjiL0K15RRnf2/nGe7ysK5adrciyRC9tMiHktzzzjLsT/uVZijswH7dynNNT1ToSc6V+OewLBY+ybB66JFT89t55ddYa1NFjiX0stsd85g/+RmYq+c51zybOZJYsTxSI3kfdL8dnYPuYfxk5yklQz3Fk7IOer5rLlbWV5HIc/7sFzE8Uh78dx/m6f8/YKUxbweVtiuhM38XTZYu80W25F7wHHOZjh+VnIjVjv9AeuImWQdKom9R36LdSn5DK8rJVyrqQL708sJX3omnte5eknssW2OW++Ur7cD9m9nxro0CbhnSCXE/XaF96i5bLznIpen/KxeRoxr0WcdHpzwOpr3uNia4nlC6HN8XPGbM14m3hq/lN1DXBK/I1KzeB3TjnBcP+DrB0newz9w/hjx5IR1riRdyV/yjrntHBkiXwxefxhxvkdt1gdryDpz4nOd7OT59ytZ7tHWx/Ge35yPhONW7N2SW1xv8hP2X37E9ac147zrHzAPRqIeuPL3Or74A3PbqZ8kVRRFURRFURRFURRFURTlQqMPSRVFURRFURRFURRFURRFudDoQ1JFURRFURRFURRFURRFUS40jxQEnYV0TkyFy8Cc0SH6VOYm4qs7b0bc7NMJcNY6Qdxr0kXTaO89qnl/xq2QzoVgTLeBf0wXTnBKB0VwJlw6SboOnBRdELbDZ8thMp5n6b5Dx0x2SIdCZZ/nHQjnRSNPt4nRoJN0eszx6aXp6pik4vn00lSPGGZB/F2drozKGtu9RJWnMX5IB8iwLxwjU7onun48T50vXC1GluOSEopRJ0EvYGpK90bC4XWGKbqMOhH9cGMjnrPKMAyjtMRjZ4V/wzQ5F9JJjl2tQF/PyGIuRT49IDmD15q14nmrpuY54oTI9UKZ7VircG7crLHdndfoh+rtHSJ2Tfpn/ES8uTQr0XdjtoSX2OB5p136ZN2HzFk3z/5Jc+gNa5XjlS7F864M2sLBGAoXYI7tTI7ZjkKLOZe2mEfFPL03pTLHr7Qez0WbLAjfi/D0rBc41+oj+rbMNmvTss85XRe+mLTwlyV94QJcQHHMNWlFuEyvrNChuSr8Y5U0++t2jy7gvYM7POEyj2eU4nmIz2yuSbkZ86U8pH+sPGN+Ogk6MWdpHi/KcpyzEY+flHLABeTOWSccUetz1zjfl0x6u9ZGjItDzm+rTQenkaWfbOLEa+fDA9ZHJ8F5kk3Q41UrC0fuuvBPu/QRjT3WnWaf9eUwiFeXJn3231S4YsM09yLFOhfLze2nENd22c7MPscr0Rf+qVm8dpYiHifIsL+cOudzusXFNDNmvL3M/t+5zP4bucz3aZ/nX9hOsbfKmzxu1WehDlLCb54TDtcxPWB7Y45PzeW8y3liE7GAy6kdxGZOOK5rrMNei3vnximduyfCe9eZ0EO3GgovoBmvfqby7M9ozL/zx1zP/QTna5DmOE4sjuN4Rj+Z7XKemsP4TlKrd41/a3Isrdwe2zbiGtMQnuyyqJFp6b6fMJdCP96eqVBhbXEszh17yL1aasZanhKKQXeJbku3xDVreSY97sKttoCp8OBHQ65l6TFrkZ/ndc2SHPuCwz3CWppzcxgx5ycRr3sRy8Jzey7uvx7mmGMp8VsElSTvIzNZrlGZ/FWecMo9gBecxWrnzRprg7nC/rNu0PFq2dyrmD7nwmzC3/hwe2IvbrPW21G8+6Rxh3vssMhxyRY4D8yQeeJH3BuWU2zHlsPj2y5rguXHG/eyuC8Mc6zNXlt8pisnaqDF/p0NOI8SLe69nSrzKpETm/4FhB4nrC36K5Pm605V/M7AFq/DTidFLLzRYp5FqXjzPVdlbbdczs9xV6zRYg80FFszubWQ11mxWaeyWd4jLKLmc5yLYq+1GvI6jnsc586hGGeLdSu0uYb54t5m6rCeLCKfZB0PTY5DID5zGM7Eb860eF2H1duIzzPcmyS77JeZH2+v7InHepEh7wG5B00L13G+zXnYKXA+txzO5+tiXVhJxrvn7LvinlqsT6kKrzeVYTtt4a5tNVgP3Y5wljfFeiaeby5CP0mqKIqiKIqiKIqiKIqiKMqFRh+SKoqiKIqiKIqiKIqiKIpyodGHpIqiKIqiKIqiKIqiKIqiXGgSURTPb6EoiqIoiqIoiqIoiqIoivL/R/STpIqiKIqiKIqiKIqiKIqiXGj0IamiKIqiKIqiKIqiKIqiKBcafUiqKIqiKIqiKIqiKIqiKMqFRh+SKoqiKIqiKIqiKIqiKIpyodGHpIqiKIqiKIqiKIqiKIqiXGj0IamiKIqiKIqiKIqiKIqiKBcafUiqKIqiKIqiKIqiKIqiKMqFRh+SKoqiKIqiKIqiKIqiKIpyodGHpIqiKIqiKIqiKIqiKIqiXGj0IamiKIqiKIqiKIqiKIqiKBcafUiqKIqiKIqiKIqiKIqiKMqFRh+SKoqiKIqiKIqiKIqiKIpyodGHpIqiKIqiKIqiKIqiKIqiXGj0IamiKIqiKIqiKIqiKIqiKBcafUiqKIqiKIqiKIqiKIqiKMqFRh+SKoqiKIqiKIqiKIqiKIpyodGHpIqiKIqiKIqiKIqiKIqiXGj0IamiKIqiKIqiKIqiKIqiKBcafUiqKIqiKIqiKIqiKIqiKMqFRh+SKoqiKIqiKIqiKIqiKIpyodGHpIqiKIqiKIqiKIqiKIqiXGj0IamiKIqiKIqiKIqiKIqiKBcafUiqKIqiKIqiKIqiKIqiKMqFRh+SKoqiKIqiKIqiKIqiKIpyobEf9eJv/IOfj/7D+KEzxuufy3cQz2Z9xI47YPzKMk9+UELs7/QQh/UJ4n/8E/9dYl47/9Xv/gnaWeqc4fW1wzuIT44sxHeP84hzb24hzj7pIbbW34zYLG0h/ovX3jG3nX/nn/4h2rnUG+L1K8c8bxSkEY/NMmL/6hTxdI3tdL0u4tmM/fnt//n3zW3nP/7NH0Y7A3eV5+3fQJye4u1G3ud1BRHbZUQBwkKY4fGiFOIv+4GvmdvOb/2F/xEnrjn8u8v5LGK3yPO0Snx94wW2s3LXRdwpthGPMyPEf+vD3zW3nYZhGD/30b+FtiZK63jdWn0CcT5g28pj/n9GYWMPcabG3Ol4PP5kxtz5S+94/9y2fvlXfg/aeXWd7XjPUzXE0/QK4rNoDbHdv812DhuIN5znEVftbZ7vB5+d285/8a//LtpZnnGubLoFxIMp5/jpjPFwwrEdzM4Z5x8idtN8/49+y6/Nbeev/85raGe2z3GsnTLeG72M+FX3E4gP91mDmz3mYOl51uDMJc75j/3VP5jbzu/83Z9FO5ND5r7T5HG3zn3EO40Q8afW+P5XC2yH+0DU/hbn5h/+6t+f285f/K5/jnbaGzxv6km2a23ZYTuLzOe9CtvRsphHO5/kOFd32f/Xv/9r57bzYz//UdZQg9fnG5yPtQFr4nqb/f/aGmv5QZE1dv0hj19qc2n/pn/0A3Pb+eO//TnWpeiuwfjfIZ7c4Zo/epFrULj+DsTO6k3Eb8qyP7dS7M8v/NbvmNvO7/4l1voKh924FPLPOgPuJR52WR/PercQz2y26/0f+ADim88+i/iLa18wt53f++tsZzbJt9UyjKsR860q6v5ayHwuRTPEh2n2X9fmeHzwS759bjs/Jdo5sJp4vZl8gLhxzLp/ev8y23Wde730FtvdC64gHodLiH/qq94zt52/9yf/Eu2cnfN6J7sct/N91v2TgzriGze4xi9f4d7lVo7XeeTweH//g++a286f+pWP40DdM64Xp7dfQOwYTOByJof4+pdyj7X6PPv7sMc9bGfCdeTD73zr3Hb+2n/J+unUuX4ULnOepJq8/uR9jpt5jeu5t8W6f8cWeWNx3f2xD3zDwj3Tr/zaX0db00W+XuS21Ahuc//nfppzK7HB2K/w/Z0O49GQffzhj/xXc9v62//mY2hnocy3La1xbDIptiOV4JxvR7ywvi/GYLiH2HI5B5577hvntvN/+sjPoZ1hme3yN7hmLE84lpfarBG7JvduRyHneCBqbDg+Qfw3fvKH5rbzZ77+m9HOdpdvOzhku8tPMTG2vqCKeP82X2+dcK6trLMGlEqM//aP/7W57fzV/5V7JvuMf+fcZuyXuOZPL7E2raZZM/MO5+btFPvzzGJ/fvgr/4e57fytH+GcD0v8u2D5NbYzwXYG/g7izIB7fmvMPdRZmnuVgc2939/43m+e385/8tvsz3wSr6drnDdmgXsgI8u9ye3bYm99yLeHGa5ZUYprw9/75u+d285//nv/DO0cGdxrtI0jxIGogQlnE/H1Kte09QzX+AcHIn9b7Ifv+cBXz1/jX/ww1/gHrGetT3K+F2zWmeU6zxt02K6pyz1rc03UzxLP903fNn8v8lM/9itoZz3BurOTYL66q6JeXhL92+H8Nzqc70FfjLvLvfa3/Oj8+f7a8S+wLrV4nIe7fO7Ufcj+He6zHYcW862T456gfoNrZX6V/fuT752/pz/45RfQzv6A13dyyv6dBMxfX/T//uVTxL0tvv+ZOvN5K8/68fybPjS3nX/7H36E95wsK4bzOOtn9pTjnLsj/qDPPdbMvYf4ZI393y2yP//hh/77ue3UT5IqiqIoiqIoiqIoiqIoinKh0YekiqIoiqIoiqIoiqIoiqJcaB75dfuzHj9OPeU3F40ln39uZfgx51yZX6/PXubHt1NFfmx8ssSPUc/y/OrYIs73+NVY1+THi8MKz9sJ+Knasc+P3U5SfD3l8WPmqx4/bpz3eB2LmAz5ceqhK746G/JjzYkpn2FPffF17zE/ju25HCDbZ/9ZIftlEdXkDuJZgv3nmfwaRDbi1ymKBs8zi/j+KOBXQyt95lHW5cf0F7Hc58fbC+Lr9mbEj1ensszHcqrCv0/yOjIiPksz7mXi9adhGMZMfLQ+b/Cj5Esz5n6UZJ+MijxXb8yv5oRCeWA47POEzT5fxPoGr7Gwwr+b1Jlzs4hjFQ35FQTbYTvThphbBr8yY0ecy4uITI6dZ4uvYDh8fepx7llDzr1gwjnuzXhdYYoaASPJj/4vYjh6nee1+FWEaJ1fVSj4rCVbM34FxhwxxzOi9pgW+8E04tVQe8BxMEfiq0xjfg1zYLE2HdRZexJiZVnxmI8Dm+/3MvFqqF/eQzwR+eQesV2Dc8Ytk+8fFITCJMn8Lh7z/AlOj8XtPGAcFJlPU6EBmKWEOoXDbvhCAzD1eV29Ivt3lor3/58Fj3Up6bCd6SJr5iAj6kzEr9YNxVowDZg3vsuvEPqzePnpTHheM2JdmRq8jm6b+Xt2wPluiPyrVPjVx9SUa0c05rwzFkz/wu4u4myJ58lusJ7KeuAbPE9DfFWxYzIeJth/Xsg92yL8idiLpThhq0m226wxv2zxfrPAeRXO2A57wHXB8hZ+yxpMj/m+kdCAtGbcMw3Fmm0VOQ8SZV53VOBEm4xEfeiLvFlARuztZgHjfI75OxP90/OFkkjsDYuiHtdHzM/0WGzOF9Bz+Z3ThFj3mmPOz3LI65C6iJnF65gEbJcfsV2pgHn0KDI2lUFJj21LnLD2mGIMkmm21Q+Ys8FYtEWoLEwn3v6u1eHY9Mbia6hdztGsKHnZIdvZSrJm9pPM2URG7JGSPP9zC9o5Fl+n9YX2yh0xTomxK4v7u2yOa9KamNIH4v6xPWTNXsSBGJYwxwMXljmOuSzPY3pC/WGynQWHcTnggJRd5sEimuJrqWLpNPLyPrIs7o/XuIiYM35NO5wyD9Ki/4p+vLk0TXNNCQORB23ubYOMuBfJ8e8toWlwRG1LDBmHI+HGWUB2mWvj1GL/tQNe/+yM/R+OuVk73Oc4n57yOqplrhW5TLxxd0PWITvgwK943NNPbean53Oc2x1eR89n3TkPeS8zCDk+i/D63NOYIfOlXGf/BGKpO+1y3IKI88rP8A/6FvPZNeK1c81mPqXF/PQKPG4k7mXzDeZzQtybmCVeR1rcu6Vm8eZRq8P86/eYT7MhNSk5cctdvcbYNjiuebHnyoruS4pnBosw83xfMsP8y1W414jEnsrrcV7kQ45H1BN7DYu6DVfsnRdyxudZkc1xCEX/muIznekK29ULed1doXns+uzQwSTePZJ+klRRFEVRFEVRFEVRFEVRlAuNPiRVFEVRFEVRFEVRFEVRFOVCow9JFUVRFEVRFEVRFEVRFEW50DzSSXqnQQdDweZ3/q+lhbPmygri0vo2X1/n30tLYtugO2EcxXOZ3PuM8IDt0Ilw/jZ61TyqYYxBji6Sjk0XSjSio6E+OEFcs+goWcSgxevzpx3EU1/4CyfCU9Wns6FVexLxuLiFeN2kM6SUoEt1EZcyb2O7hHdqYNPhk3foKqmkGbsGXR2BSyfI8gPh2mnGG/frJ7cRRxnmY+TSQeGU6LgoZR5jO9J0ACXT7K9Jge6Y03w8X5lhGIZrs0+WRW4/4dJNd5xhn9yq07+1e59j0GpwjDYvsS9qNSE5XMDTz/A8iRrb1b+8hNhv87zBiJ7YdJ45XMjT35Md0q/leMKFuYDQ3EA8EG6/foq1KN2hByjXuIN4KjxUw1CUxswNhIkUz7eIVvsPEJvLTyBeuUHv2rJD/1hdzJ1V4aVqCO9XSzSbWbcYpyE8ZxPWpoTov7My3Z27m8yLtQ7//+1Gl9X+PMXrHJTjeWy8jU8jbnaY5/uv8DjTJn07CeGqrKS4GNSy9DhZy/RLTYTD9O2L2vmy8OFd5cB4l9nOyRLf7+W5pnh7wp3Y4PHcJeErysSroUsjZkguyxq5tHoFceP/JYxjHZoErJkDfw+x5zEvZmE8N2Wuz+uzExzXUYL5e3LI63rjM4xvPsb+3KwzHzMT+qKCjtitcKn9M5Y/+VnEzhWep1BlXUpkLyH2hEf7yGE9Hjjs34LL/kvP4vWn16WHLlfguK/meb3ujnASr9D/dn7Eut46ZJ1KnXJ8/DbryyImt3jcZoLz945xF3Eyzf4urzBPzPXnEQdL7P/eCR2njcN4e6bSOdcP2+D8C2uMT/qcNyct9lc0YP8stVlvCx3Of38Sz+l8MnwJ8Ui4z1sd5sFli3ueN5XpEWvZzKO+z3XACJnPThTfSVpKcX9r9LkGGQ/YFkt4Xq0Sc2co/N0z2WfZJv8+w2tfxIND5rYnvMv9PscyK3KqvMvztMpcg3p1rjnTZ5grwTpz60ML2tlJc2wmQkLYb7PW+HnmnFPmvv3qGl/fSbFG799jO4/O4zleX0rzOKtpjuPjVeZotiBcsmJNK5isZWkhC1z32f+VQbz7ufsWa4V0kC7lObfW1nZ4nidYe6yj+4iDwweIC+fsF6cXb86Pc5yDwYjtmp2Kvdcq51FC3C/bVf59XjwXsF4S9wTNeJ7s2g2uuScD5svDU45r/x773331FcSvn3MenQ04j55bEb81UoznfByGrDv1CZ8vXO7wXmFkv4H4PPN5xLdeYf14cMw1LnyS8zZa5V5lYTvPLiPOR8yXlW26JPcfsv7dvst6Gy01EAdl7gmmKe7F/Siek/Qp4c/vV7mGN3aYF2nhll16Q/wewnXO39QW69CGz34pheL3Jhbw4JD3skGf4zBrMQ821rnGX7/Bur/hc/6f+fz7scf+9fx4dcmqcz1wymx3eVX44Q9Z//qvsZ21EduVO+W8SojnLL1svDU+usf8CmbCZ17neWzhQi5usv+PEpy/e8Id3J3y/WMv3m/f6CdJFUVRFEVRFEVRFEVRFEW50OhDUkVRFEVRFEVRFEVRFEVRLjT6kFRRFEVRFEVRFEVRFEVRlAvNI52kb1q5ijgn3l0VLolkQryhwde7JToCDjN0jkz6dARMJ/H8WrND+gVdg+6RQZnukWRAR0W5SwdI16BzZJIUfqmAx08MhdNigajuapVOEMvYFPH7EA+bbEdbtKs5Y3+NW3RYVLN0S5RS8ZwW44d0VozFsHaEeyhcorM1m+d5jwd0AHWoPjF6J8LRdMrjVY0vndvOu8IplRI+tpxLF0e1RudSxaMTw6vTpTFIcHwCg8d3onhOC8MwjEqfHpVChTll5+kFSTm8tnRAH1QU0sM7Cg4RN9u8lokrHXBvmtvOoH4NcS5JZ2a5zdw/OqCn5sFtuuqqT9MdWt4Q7tXb9C4NuryuN89tpWFUHI6tN2VuD0ec0/6MOeXarAnFJeaonWb/N0s8Xz8tHJ4L2L3LnOlPOLf8Ep162xX6eLYzHKdxUYz7Bvs7EH5lx4zngxqf0J/j++yPqUuPT1p4rkoGfS/lGvuvUuPxkj5FV24Qz0laGtGjZM44rxKO8G/V6LUxCixmhSX6jIol+nVKQ867rBfPB3Vv4yHiWpXz6FKSa0bFEm5J4TfLCYdxTqxhfpr9G5QeubT/GYMCa2BS+NpSDn1YtugfY5O+oqnF6x736SjuzTgebT9ef1oDtnMWcj6PA873jPATPXmJ/rwbq7zOKwV6r7LUnRm+qCfGM/PbORBr0KxD7591h/1zYrJ/7rl0RhYLrOOVLPM7l9lBXE0KgdwCPut/BnGpz+tfmTBfsx7rXzoUzlGxPpXLzJuMKEM+p/FCRgPueUYG69Q4EnWnxLpTXmM7cynmTWooju/dQtwMOF6G8ZfmttOrcj0IE7xgx+JeIu3Qa5+2GE+K9KW3HLpss2IPmo7rJC2w46dJ7mEmQ+bbQ0/8ToDwBDp9rkeZItcjW1y3ZcZzeRuGYUwi4bEVflQvyzk+GXOsJ1POvdmQe4SEy/uTNZtzoJRm2xeRrXGPE42EI3PGWj+rcc80MFlsrIB7gJIpXG0JHj+Mucav9bgH7CRYq4YGz5OvMjc2rzA33CHP23jIffxpjzndnsm5NJ9tg+O2WuUavX2Ja042z/4yHdb2QYO1aCw85lbImjsxeJ+wiFTIuVAss12rJc7hwirzILBZi/ZOmZ+N2+wH22R+Wla8PVMxz71D3xUO3ME+4lmR54kiCrjzeZ7XyjMv2jbz7NRnvi6iP2E+TUUNTVV5nHydNS9bY42sucwDf8Y9kWVz7zhNxPvM2FlnD7Er5pV/wj1+b8p5cByx/wOTa9j6Jq/DTjOvrCDenqkj7s3MMmvvsnA6Lvkc95lwON8Vv0VyPGP9LU85Xrkons+7keAeaBDyuK2p2GsMOZ+9Dutv43X27/Ah1/iNGeOyz3rwVe//1rnt1QVmeQAAGFFJREFUzIy4x5kEzJdhVtxzllk/E3XuQZdEPhbE+nXQZD1ot+L151GddT3piN8CEvcOyYT8PQPO50wo7o3FrwmNJqxjfTdeOw2fdTvts64UQr5eTTAfq45Yv4VzdNoT7lRTbDpNjsci9JOkiqIoiqIoiqIoiqIoiqJcaPQhqaIoiqIoiqIoiqIoiqIoFxp9SKooiqIoiqIoiqIoiqIoyoXmkeKyL736NOKESVdBwqaboCccNK0H9A3ubtAxdLtCd0HikMczujzfIswzev2MKd0aU+HTytt0ydRCOioawq06StLlEbp0YITlm7Ha+ewGfW7TFL1fXpbuzd2DA8QD/zXEnRk9beMmfVBulW6UKMfrXsT5G3QHDW26IM7TdDl4Ad05KeH+vHNGN8eDPbo7Nu7yOsundLy8dUE7PzOjI6Ti0pmyNWO78is8bm7I/GysPIW4uS5cU9R+GsVOPO+fYRjGapNjXxCeoFmNXhbTpA+rKLy32QSv1UrRDdbpcAw6Dekv+oa57ewV3sLzjjjWV/Y5t07fYKfcepU5+ezTzyKuXn4S8advv4r41TadcN8+t5WGsZLhWPZd5vasQ+/KzONcGOXY/yvCz7O5RB/KOLyPuBnS97OIe7fpCGwMWYtayVcQp9eeR/zE0rsRpzIcV/PS64iLKV5n3ow35wen9AlNEmx3L3EJ8WXhwNvx+P9thR3mW3ZN+M484YeZidq/gFqTbtRaknNwR3iq7CLnTbLI6wifYI1I1DivCn/EvEi/QX/RIl57hmvSM1l6gt5n3eB5Ar4eCLdl0Rd+ZeFtcm3O01kmnm/nvMa6YefpHwssesnDCvt/dpX94Z38JuJx8zbitke/Vkp4iRaRGHL+ecKH1Z0wrme4Zj/zLNeKDeHIrWfZX/0D5oEn3JXGX5nfzkaa+ZId0o8VvsL6+OKIe6bfEXuo9xWuI35PiXuypW3uPdZrzO9F/HH4bxGX+9yLbHRYpy+dsX8uNTnvnJ0dxNV1Hs+psx7bUTyP4mjKOuv6wsXrcY12hIK4usV5VHJZH60W9zoT/9OIWzb3XIsYbtArGYXsLyu8gjibocernOd5Rsv8+4MC/35T+CQrTNeFHC/tILYDzpu0mEdHZ8zfFw+5/jxVuof4sSK9+IkM9xNGajteQw3D6Af8rYGJw4vsL3MNPxeu+/MT7jNTA+6xqgMe7+nSlyC+GXHuLaK6w/elB+I+KU334WyL+/RQ7N1qR3x/vivc90Xx2wJprg2LeKzJ2nMo/LDnJufSUpo5dvM6596//xOe97Mvcy09bHNu9UOO5yKeCzlHqzXuRS49v4o4W2L/2ybnSvcB16j2EdvZTHAP1UrEc5JWhJd5ZYlr29UrX4hYLPFGx+X98KsPmK8vvcDj126wJuTXuKYtol6gy9PrsaZ2Xfqphx7vg4KAXvyi8J+bJV7YqfitkqOI17GIRpN71aDAuLzC45gh89cOn0DsJ/n3mST7IZVjXgydePdzp03O14aoO7t7LyButjl/j3o8z7u/jL8D8ebnuDd0+nRwJifxnKStfe6ZUpZw4pa5B1/Ncs3eztKpe3jCtaHBcmrUB1wrqp7omAXcNVgnPOGSHna497K63PM0hnS4fv4B33/3/A3Eq2Pe9xembOdX/cR8J2nNY91pWawTjSrr66jC/h1m+fcrM1E/Zlw7uy3O096u2IMu4F6N868mbgEvN7n3MgfMzzDFZxUp4QK1kjx+U4xPZyx//2QBFvsjbfG8yxbzfDnBulOJmI9Xh6wH+SafoUQ5cU+Uincvp58kVRRFURRFURRFURRFURTlQqMPSRVFURRFURRFURRFURRFudDoQ1JFURRFURRFURRFURRFUS40j5RwfOLh/4J4kqS7oJehOycxovMj2aEMYVgUHrAMHQS1HJ/Z5lJ0Di3Cq9HNYgq/WMpiO4MRr6PfoVPFsemaqGTpTj2v0Znjm3QELXJotrr0L6aKdNgUK2zHU4/R8XFjhw6GNx7SebF3xv7yPf79bpvXsYjXV+hUGQv3QzfieTrHdGsMT+i82G9wnFs9jketIpxLa/FcEXafHjDTogsqEM6J4TnT/ezTwsO4TDdqokBXidWif9MaxHOEGIZh2Es8t5tjfDBlnMkzx67U6X9yhWfXHNH5OB1yjIKh8FgtwGnTi9I3hJMsS9+I/Th9JV+z+RcQL5nM0c7LHLN14TtJb9FTsogoRzdqJk0fycoy3XyTCa/LGwkH5JQ52R8wpxNTHj/jx8vR54q8PsOhbycK6KUaNli77giXbCvkuI4T9HElHfavlYrnh3Gvc+5Uxbg8a7Fda1uU/60+Rm/YxGStcT06+Lw95pXf5OvG2+e3s2TvIB7W2O7mFteYpM05ng32EAcHzIPwHtegwRnXNNPnPKVB9s/Zuc/aUd0Q+bjJcc2YdHM6PXqoJo0XETcP9hEnc7xu25QO4vnk7tFDZ5V4nOGUNS5v0eP3zHXhJ+tyHHND9v96nvlYL8b7f1pnm25Ut8P6OD1mf/tpjlO0LNpZ5dob5VlPTvJsV2PKPPnqBe18vEafW2TTMWqkWLef8zmPtj06iFcv0yO2vMHxmpnCEW3G879dMXn91oz9FXboTTyJ+HpXOHb9B0LYfcb6WEiwvjpi6/m2tzwzt52NFR63J9o5cXmeRoZ7FVN4vwbCk+aMWIe8NvM3G0+rZjQjjmPKYzuzfbkesz+8EfewTpt/XyyzXUGW19lfZryI992gX9Ax2a6cxdh16Unsj9lfyy59cBWXdd8b0A85mwpp7CNonTHX/QHnknfGGpjyWZsq4jMg61U6ytYv079c3ODeY1qO5yhcOmWOujbXpNwyx9rqce6kmvS0RsKbG1ncM0yS8rcNGC/iblk4LJf4d9eusl3lMudO6y7v5/busrbf32dtSzn0V2+tslYt4vLOc4jTFTG3RM1snTMvGj3h9z1m/057/PvSxg7isnAJLuK6TZdkLsk1N3LoWmwccw/64BbdiB1POHTXObdWLebj6jiei7YhHKRyDvsRx321zD3P5hWu2fmUECB3xf3ziPfPjhtvD9qxOI/CCfMvOOR5U0Ou6dk0+ytR4P1clGM/7PuUag6nnHeG8W1z23nZoa97Wmd/umIP7vfFvUaf7d6ss+Zu2eJ4ZeFqLYn7ZeML5razX+MaP+xzDbn/+x9H/OQKfz/ibZti77HGe4LrOc6T2kPeI6Rb8daky9dYH4dj5nmryzrly73tFebbjZusO2sm4/I5fy8gN4z3+xLFDu+hC8LNu13i3s8UtzRRk/l8W/jqhx3W+anYmySjeL8vEXjMN2/McRt0eC87m3DNtiPOA8dnfY08ztOCy3YaY17HIvZN1gV/Kn4npMV88sUe1BO/YzERexlP/AZPcsR71EIn3u8h6CdJFUVRFEVRFEVRFEVRFEW50OhDUkVRFEVRFEVRFEVRFEVRLjT6kFRRFEVRFEVRFEVRFEVRlAvNI6U7d84/hbgtPFRHBt0Y+Qn9UCtT4QyZ7CAuT+io2bTouKmn4jktvGX625I225FI8TL9IV8fTei4yBp0vaRDOha6IWUTbTue67M5oAujINwjOY/9uUrFirG+zGfaWZtOECfgdd09pZPlnMqbhZyW9hBPZnQ7DDw6K4YHRcS9XTp/2iOO68zkdSa22G77Cp04i8gO6CKxs8JdkqMbqiM8k8EbdNzYkyPEZp3tiAZ0CiUmMTvUMIywQC/INMm29Tzm+prwaSxleK5Vm7ngGvRrjD3m7GwkBCkLyA+Z+xPh0LyXpUNzp/oY4rdm6Twb7tL703ydfVwqcG7W6vGcZXaWfq1Ukn+Xd+jTGc7Yv50Jr2P2gD6TyRlzI5wwt5xpPA/Yzarw4mbotWnPOJfGI3rW9kdinAv0ps3y9A7J2mUm4tXQ7Dbzc9Vk7X5G+LaqW+z/zOV1xKfnnDvDY9bI8TFfdw+5tiwilaFXJiywf/ur9NhYvvRDcdzDQ/59cMLYc0XNePSS+WdUH/L6MmLtdFPsT9fkdVl94V5sfw7x8JzXVd6gXy9ViuemTB/ST2SMOQ9GSfZXfZV1aXmH7e6/xlrv+8zfWpr1pFyOl5/2Fr1SVpKySLPF+hYVxFqwxPoYFrn3CEQ9OU9zXu7N4vmgrtTpvxvZHMeOwzXkMYt16j0WvVbuk8I/uMN5Omozr3rDeO3cjpgv04j1bhRwvRlP+XrTE/P7nP0/Ff6tQoJ5ljbi1c9RjvnihcIFnaXPbCIU0Gch+2c65Z4r5TLfZ2KPkO/z+ItwxV7MDNk/1ojjYveZ99aQ+ZkRnr/8gP3rJxmPSsITaDxpzOPpba7XGeFsLid5/VGS7Z6mOB7JY65HiTOub80mx300jDfuhmEYjXPOUfNc7Pfuc62z84wLK9xjra8w5y/vvBlxoiRc+xnZp/OpnnOP41U459NLzKGUx/uVvCf2KhZzZ5Rin/rCvR9G8dak4zJztLrBffjmm3j9lvCEH7zOmni0x3a22vz7y5fogK3XmHuLWN+4iTjIizVa+ODbnT3E948/g9jt8Dos/wnEVYc5XK/SH76I7ZC+Xd/nnB25zIvGKdu5d+sWYi/JPVR+g3lSCVkz6j7n7iL6Hr3ig5lw3JrMn+0cr+Np8XsI44lY04S3OT/i2lGcxXOSTg3uacIJrzcaMk4GLPZmQtwP5+hSTFXEfXuLTtgDl/fLi9hMctxHaba7XRW/WTJiu6WTtJJhzS1OWee8nNgrZ+KNu1dh7e2dsj9O79xDnL7OPdATqxzHQpl1Z6vEvV9S/M5D5MXb0y9tivp4yvNMz9hfM4trSLTM/MwIf32iznlVPOLzikyXe9xF5MQeKJfivUbN2kHcFY7bY+F5P2pxL3LS4riviOcLhUS8vZ3p8n3+gPnSE7/N4gvf/sQQ9w7iXjk1ZjvNAf/eHMUb9zOD94T5qfTicz2Zijo1EHtIUR4Mz+G8cTq8F6n04/2uiH6SVFEURVEURVEURVEURVGUC40+JFUURVEURVEURVEURVEU5UKjD0kVRVEURVEURVEURVEURbnQPFJm8+TyexH3ynQNLFM5Y6T6dJ0UTulkmDl0SxgjOnGmwvvXn8Zz7bhP0Fdmzuio6Lt0P2Srwv90g/LP4gFdSKk+HQ/dIp8tj4qNWO1sWOyP0wZdQ2/cf4j4co1+s6fW6fQJUryOlSX2Zyct/HAu+2kRz042EPemPM7hRPjJuhynBy06RbaL7N/tKh1M65HwYvXjOVc2phwnP8t2uRn6L3sOHR+exfeXxN8XUnRamOL1hB2vnYZhGMd99kFWTL1qhmKfcEov0nGLnqaGcB62Dnltkz79MzM3npP08dwziJsGfUb+lDk6EN6Vu206NTtHwkXZ4PuXA15HvhjPZ7KSYfEJhPYouEUfVCexi/g0cxuxeUC/TPiAtWrsUngymtGXsojpYxznwKXHx2jTTRkUWbu8y8zhiSlyOCHci1PhaXPj5ejXZ+g2Hdv0GTXSwuM8ZD5VX6RvZ9rhuFtdtisMRW2K6aa8U6VHyhXea+8ltts36dfpJugjSgtna2qZNXQ85vGmU+bJIu6NnkMcjll7t13mxVmK/dm3WWu8NPtvVXjDcjX+fWqd3qBFTCPOEzPB+TiwuKY7SfqesmmuSW6dHrVwk56rQDgkfYv9vYjhMr1SSeHMvSzKW1TjvOqss11F4cuKOnSFhgdc05Mt4Z/+uvntrKyIPY8v/ed0jvrCKZxY5tprGmLNOeVexHHF+PjxfN4l752IraLw9r2Fe6ZZkt41N3WIuJfjWtx32P9hn2u+4dIft4id3XcgnmQ4DzpVjkvOYT0sZ7hHOh5wnT0O6NuK0ryOYiGet+rasvCbO6x/yZD9VwiZb0tjjmvF5XX6HeZV0ryP2ExIr9oXzW3nbiT8dz222+9yT5pOcS9n5dk/RoevR9JXvyM8gHkhMHwEBx5rWM5i7ajW6V1NbQsv7FPcc03qjE/zdBo2T/mbAYN91oC3v/vL57YzKTzAE+HcHwq3qecIx+ZTrGWhJ1yoYg1Pe8ytwnE8V125wuNEwpt7dI9jP9rl9fde4ZwPfa6lz2wzNy7nWQNqpsidBQzLwosvfruhN+Ees+sy921Rc50KxznhCI+xweO32vH2TOEp3ZQt8aMPu/eZv60x+8vMcU+wVZFOTfbfhN1ivBFPO27klvnGhnDYHjS5J13yOE+ih6zdQU84IdvMoxXhNc+m4v12w47HcTADjpNpc54kh5wnTpO1JVNi7XduXEfcfsh55LXi3XvkA86TKCHWeJt5EfpiXg2Fq7LH2NnjfN67xn5o1VlfvmZBOzdyfE6xKn67Zcd4O88b8Po/+Yn/A7H7Fu793Kt0PDd2eM85qXI8F7E75bxwHeZnf539m2kxX0sNXtdJhnuifnQHcVL8tkha7KEWkbrBe4FZkv17mhf10uS9cy/k64X/u7076W3bCqMwzFEiJVGyLKvxgCYoUKBF9/09/dFBgWYRI41nW5atieLMrs8HKOC6ep/dhSOZusN3LxnhONN50Te55YNW11EYdvtO49n6b73OOy0cy8/6e2/M2ePa5NN/arW/po2O69e91otXM55/HbjOpNHrGNZ6bxWVWoc3pX7+21Lrer3T17cm/3xs/h7CrNetzvNNUgAAAAAAAABHjYekAAAAAAAAAI4aD0kBAAAAAAAAHLUfhn5ejTRvzMYRhRea5+ZFmgliYtScdanZGlmmz2irlWZP5DvNHDikvNDcsyLVrJHsTbNhwkSvI57p6+NUs2CGJgwm7WsGSxZ1y95Yufo+u41mMiy/aLCiO9X+mO/+kPbgF81cSK60Perp+Ayrs07X+XOhmR9xodkYaabtl71pbzTr4fepZlR8TDTb48TRTKE40/E5ZFrpOG1Nftsu1P58i/Q6Fybj5qeeZnNUgY5z3NP5Grrd/4/hfa994Id6rdFec3vawuTLtppVt16YbNClvn9eaJ9WVbfMx4u+5sw0lebU3BeaPbnPdU4/ZZrDslhqDsh+pX0cjjQvye225J2kZ/Jhcl1L+Y2OfRVqbVrNNCOuv9RcG89EpxW5zp2iYyZpbTIum4WZ2w+aQ9VMdNzKM20XlckszbXDmo2+v59pZtwhf5p846+h1pLvsW4Vhcmy6y10XJuNZtF5qY5z6+naquNu+TDPQ831aVcmS/ZB5/k+0P5JR7rmJ2OT/5bofN972g95rvPukKdM86Rnhdbe2uTrpKH2x62v9aAfaP+ZLcyJRiYTcdItg7h2TIamo6/LXd3bMl/rQxZoDa5GZjympu20P2wfkk2030cmevP0VK97O9VxfjF70O5F/30/1f5uHvTM4N+b0OMDBmNdB0GqA1WkJt+tr3WnN9O9t59rPe2ZM0PU6OcI2m7jHheamxadaJ0eXernbUxWbV7qOoynejiMhnqm2rk6b3JPf37IbKG5ffupjktwqvtREmg9PQvPpf3g6jivTB66F+h6j6NuG9I8MfXLjEu70w3FNTl0p6YeRqW2G5Mb5nmaJRx6mtN4yKvJOWsykwlu1oVr8uHiSvvH2ZpsX7MtZnPt7+1lt3xCx3GcZalzsnX1WiYjPRM559p2f9U5Xg70Wjetvv/3a51bz083na7TN33Yhlqrc9NHqw/ax7tzkwec6et7a62R8Y3Onehd24fEAx2cvckkfXs2Z7lbrT2P3zRz8OOlfq5Pc117V67WvjO3W553Fuvn37U6Z94qk31Y6drwa50nzdicgRJtFyaTNE27ZRS2K61haaF7+t3O3IcOtJb4iZ4JTk3+8Xiq/fdlp/39bI4uh/TGWpvqrV7nMtI9fGOyZp03c0Z9NTVxqf0/LnW+DoJumbnzyqyDVj9v6Gvt8czfyvCWuje6c51v5QfNTpyudfwmebf7+H5jzuCtzqfA0Zzx1uRP54Ve5/tS3++x1H74d6Zn86dht5zsk572nz/S9wlckyV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"text/plain": [
"<Figure size 1728x144 with 64 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"99.3% accurate\n"
]
}
],
"source": [
"show_filts(model)\n",
"plt.show()\n",
"loss, acc = learn_big.validate()\n",
"print(f'{round(acc.item()*100, 2)}% accurate')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Here, we obviously didn't have to train for a whole epoch, but this was the fastest way to implement\n",
"There were just very little things to change in the model as we've trained with 1e-100 learning rate\n",
"\n",
"It is interesting to see that the accuracy is better as was with the smaller pictures (99.13% <-> 99.3%) even though we \"didn't actually train\" on the bigger dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
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@nofreewill42
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I have found the source of the accuracy drop present in cells 30-34.
With the 5x5 kernel size, we are adding 5x5=25 numbers together.
With the 9x9 kernel size, we are adding 9x9=81 numbers together.
The numbers in both case have the same mean and std, so the outputs of the 9x9 kernel will be 81/25 times the outputs of the 5x5 kernel.
This is important because there are biases in the network and ReLUs can go from inactive to active and vice versa.

So, all we have to do is to divide the weights of the 9x9 kernel with (81/25).
image
Here is the corrected version of the 9x9 kernel without any "fine-tuning". (This was a fresh training, without this "scaling", I got the same "accuracy drop" as before.)
image

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