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@ashtonsix
Created May 28, 2018 22:09
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%reload_ext autoreload\n",
"%autoreload 2\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('/home/salamander/fastai')\n",
"\n",
"import math\n",
"from timeit import default_timer as timer\n",
"from fastai.imports import *\n",
"from fastai.torch_imports import *\n",
"from fastai.learner import *\n",
"from fastai.conv_learner import *"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"class FP16(nn.Module):\n",
" def __init__(self, module):\n",
" super().__init__()\n",
" self.module = module.half()\n",
" def forward(self, input):\n",
" return self.module(input.half())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def get_transforms(trn, val, flip=False):\n",
" x_trn, y_trn = trn\n",
" x_val, y_val = val\n",
" data = np.concatenate((x_trn, x_val), axis=0)\n",
" mean = data.mean(axis=0)\n",
" std = data.std(axis=0)\n",
" stats = (mean, std)\n",
" sz = data.shape[-2]\n",
" aug_tfms = [RandomFlip()] if flip else []\n",
" return tfms_from_stats(stats, sz, aug_tfms=aug_tfms, pad=sz//8)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def read_cifar(file):\n",
" import pickle\n",
" with open(file, 'rb') as fo:\n",
" dict = pickle.load(fo, encoding='bytes')\n",
" x = dict[b'data'].reshape(-1, 3, 32, 32).astype(np.float32) / 256\n",
" y = np.array(dict.get(b'labels', dict.get(b'coarse_labels')))\n",
" return x, y\n",
"\n",
"def get_cifar10():\n",
" train = [read_cifar(f'cifar-10-python/data_batch_{i}') for i in range(1, 6)]\n",
" xs, ys = zip(*train)\n",
" x_trn = np.concatenate(xs, axis=0)\n",
" y_trn = np.concatenate(ys, axis=0)\n",
" x_val, y_val = read_cifar('cifar-10-python/test_batch')\n",
" return (x_trn, y_trn), (x_val, y_val)\n",
"\n",
"def get_cifar100():\n",
" x_trn, y_trn = read_cifar('cifar-100-python/train')\n",
" x_val, y_val = read_cifar('cifar-100-python/test')\n",
" return (x_trn, y_trn), (x_val, y_val)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(x_trn, y_trn), (x_val, y_val) = get_cifar10()\n",
"plt.imshow(np.moveaxis(x_trn[7].reshape(3,32,32),0,-1))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"class ParameterWithLRMixtures(nn.Parameter):\n",
" def __init__(self, *args, lr_mixture_splits=None, lr_mixture_axis=0, **kwargs):\n",
" super().__init__(*args, **kwargs)\n",
" self.lr_mixture_splits = lr_mixture_splits\n",
" self.lr_mixture_axis = lr_mixture_axis\n",
" @classmethod\n",
" def from_instance(cls, param, lr_mixture_splits=None, lr_mixture_axis=0):\n",
" param.__class__ = cls\n",
" param.lr_mixture_splits = lr_mixture_splits\n",
" param.lr_mixture_axis = lr_mixture_axis\n",
" return param"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"class MyConv2d(nn.Conv2d):\n",
" def __init__(self, in_channels, out_channels, kernel_size, *args, groups=1, **kwargs):\n",
" super().__init__(in_channels, out_channels, kernel_size, *args, groups=groups, **kwargs)\n",
" kernel_size = (kernel_size, kernel_size)\n",
" self.weight = ParameterWithLRMixtures.from_instance(self.weight)\n",
" self.reset_parameters()\n",
"\n",
"def conv(ni, nf, ks=3, stride=1):\n",
" return MyConv2d(ni, nf, kernel_size=ks, stride=stride, padding=ks//2, bias=False)\n",
"\n",
"class MyBasicBlock(BasicBlock):\n",
" def __init__(self, inplanes, planes, stride=1, downsample=None):\n",
" super().__init__(inplanes, planes, stride, downsample)\n",
" self.conv1 = conv(inplanes, planes, stride=stride)\n",
"\n",
"def ResNet18(): return ResNet(MyBasicBlock, [2,2,2,2])"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"cache = {}\n",
"def get_lr_mixtures(splits, axis, shape, dtype, reverse):\n",
" key = f'{splits},{axis},{shape},{dtype},{reverse}'\n",
" if getattr(cache, key, None): return getattr(cache, key, None)\n",
" \n",
" arr_sz = shape[axis]\n",
" split_sz = math.ceil(arr_sz / len(splits))\n",
"\n",
" mixtures = []\n",
" for i in range(0, arr_sz, split_sz):\n",
" mixtures.append(min(i + split_sz, arr_sz) - i)\n",
"\n",
" result = np.array([])\n",
" for i, sz in enumerate(mixtures):\n",
" result = np.append(result, np.repeat(splits[i], sz))\n",
"\n",
" dim = np.repeat(1, len(shape))\n",
" dim[axis] = arr_sz\n",
"\n",
" result = result.reshape(*dim)\n",
" if reverse: result = np.reciprocal(result)\n",
" result = torch.Tensor(result).type(dtype)\n",
"\n",
" cache[key] = result\n",
" return result\n",
"\n",
"def fix_grad(param_groups, default_lr_mixture_splits, dtype, reverse=False):\n",
" for group in param_groups:\n",
" for p in group['params']:\n",
" if not isinstance(p, ParameterWithLRMixtures): continue\n",
" splits = p.lr_mixture_splits or default_lr_mixture_splits\n",
" axis = p.lr_mixture_axis\n",
" grad = p.grad\n",
" if grad is None: continue\n",
" mixtures = get_lr_mixtures(splits, axis, grad.data.shape, dtype=dtype, reverse=reverse)\n",
" grad.data.mul_(mixtures)\n",
"\n",
"def decorate_optim_with_lr_mixtures(optim, default_lr_mixture_splits=[1], dtype=torch.FloatTensor, **opt_kwargs):\n",
" class OptimWithLRMixtures(optim):\n",
" def __init__(self, *args, **kwargs):\n",
" super().__init__(*args, **opt_kwargs, **kwargs)\n",
" def step(self, *args, **kwargs):\n",
" fix_grad(self.param_groups, default_lr_mixture_splits, dtype)\n",
" super().step(*args, **kwargs)\n",
" # fix_grad(self.param_groups, default_lr_mixture_splits, dtype, reverse=True)\n",
" return OptimWithLRMixtures"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"class TrainingLog(LossRecorder):\n",
" epochs = []\n",
" def on_epoch_end(self, metrics):\n",
" super().on_epoch_end(metrics)\n",
" self.epochs.append(self.iteration)\n",
"\n",
"class ImageModel(BasicModel):\n",
" def get_layer_groups(self): return self.model\n",
"\n",
"class ImageLearner(Learner):\n",
" def _get_crit(self, data): return F.cross_entropy\n",
"\n",
"def get_learner(dataset, Model, batch_size, opt=optim.SGD, opt_kwargs={}, lr_mixtures=[1], metrics=[], *args, **kwargs):\n",
" loader = {'cifar10': get_cifar10, 'cifar100': get_cifar100}[dataset]\n",
" trn, val = loader()\n",
" tfms = get_transforms(trn, val, flip=dataset!='mnist')\n",
" data = ImageClassifierData.from_arrays('results', trn, val, bs=batch_size)\n",
" model = FP16(Model(*args, **kwargs))\n",
" model = ImageModel(to_gpu(model))\n",
"\n",
" opt = decorate_optim_with_lr_mixtures(\n",
" opt,\n",
" default_lr_mixture_splits=lr_mixtures,\n",
" dtype=torch.cuda.HalfTensor,\n",
" **opt_kwargs\n",
" )\n",
"\n",
" learner = ImageLearner(data, model, opt_fn=opt, metrics=metrics)\n",
" return learner"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1867cc32e0524411be3c140313788f0b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, description='Epoch', max=5), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch trn_loss val_loss accuracy \n",
" 0 2.373032 2.313194 0.1549 \n",
" 1 2.206881 2.190781 0.1395 \n",
" 2 2.049916 2.00605 0.2679 \n",
" 3 1.892894 2.026761 0.2946 \n",
" 4 1.780991 1.717772 0.3895 \n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e6295885d5d441438fdd23b2cfbbb9ad",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, description='Epoch', max=8), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x864 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"l = get_learner('cifar10', ResNet18, lr_mixtures=[1, 100], batch_size=256, metrics=[accuracy])\n",
"l.fit(0.001, 5)\n",
"l.lr_find2(start_lr=1e-6, end_lr=1e-2, num_it=1500)\n",
"l.save('1')\n",
"l.sched.plot()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"l.sched.plot_lr()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a1b3f3fe9ee947fb8385529f8a9ebc7e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, description='Epoch', max=5), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch trn_loss val_loss accuracy \n",
" 0 2.402063 2.433659 0.1 \n",
" 1 2.176807 2.132109 0.2319 \n",
" 2 1.94545 1.880062 0.2999 \n",
" 3 1.752756 1.946859 0.3038 \n",
" 4 1.60548 1.602575 0.4166 \n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "aa9de21026584f0d9b36ac7c7b42a53a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, description='Epoch', max=8), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": []
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x864 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"l2 = get_learner('cifar10', ResNet18, lr_mixtures=[1], batch_size=256, metrics=[accuracy])\n",
"l2.fit(0.1, 5)\n",
"l2.lr_find2(start_lr=1e-5, end_lr=1e-1, num_it=1500)\n",
"l2.sched.plot()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fff156ea1e4a445da933fd559890fdff",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, description='Epoch', max=1), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch trn_loss val_loss accuracy \n",
" 0 nan nan 0.0 \n"
]
},
{
"data": {
"text/plain": [
"[array([nan]), 0.0]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l3 = get_learner('cifar10', ResNet18, lr_mixtures=[1, 100], batch_size=256, metrics=[accuracy])\n",
"l3.fit(0.01, 1)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "a1087b5d05d540449a4bd895ad4172f5",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, description='Epoch', max=1), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch trn_loss val_loss accuracy \n",
" 0 nan nan 0.0 \n"
]
},
{
"data": {
"text/plain": [
"[array([nan]), 0.0]"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l.load('1')\n",
"l.fit(0.01, 1)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f161d5d635eb4cbaabdc1df97d5053eb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, description='Epoch', max=25), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch trn_loss val_loss accuracy \n",
" 0 2.239997 2.019175 0.2254 \n",
" 1 1.776518 1.665847 0.3859 \n",
" 2 1.545735 1.554138 0.4292 \n",
" 3 1.424233 1.352691 0.5075 \n",
" 4 1.31913 1.331203 0.514 \n",
" 5 1.243327 1.301395 0.5312 \n",
" 6 1.144832 1.233899 0.5569 \n",
" 7 1.081252 1.160562 0.5908 \n",
" 8 1.042611 1.128667 0.5945 \n",
" 9 0.96753 1.141177 0.5976 \n",
" 10 0.917126 1.031446 0.6409 \n",
" 11 0.848752 1.050599 0.645 \n",
" 12 0.811277 1.018551 0.6549 \n",
" 13 0.741099 1.025795 0.6572 \n",
" 14 0.685131 1.070499 0.6468 \n",
" 15 0.628841 1.018542 0.6679 \n",
" 16 0.591296 1.078266 0.655 \n",
" 17 0.534801 1.079688 0.6582 \n",
" 18 0.467183 1.075406 0.6685 \n",
" 19 0.429726 1.162095 0.6492 \n",
" 20 0.370178 1.225531 0.6656 \n",
" 21 0.346265 1.172823 0.6637 \n",
" 22 0.291691 1.272323 0.666 \n",
" 23 0.246915 1.332373 0.6706 \n",
" 24 0.209912 1.40163 0.6715 \n"
]
},
{
"data": {
"text/plain": [
"[array([1.40163]), 0.6715]"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l4 = get_learner('cifar10', ResNet18, lr_mixtures=[1], opt_kwargs={'momentum': 0.9}, batch_size=256, metrics=[accuracy])\n",
"l4.fit(0.005, 25)"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "097d3740783c464dbc4fae66c5d8c6b1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, description='Epoch', max=25), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch trn_loss val_loss accuracy \n",
" 0 2.250751 2.032356 0.2034 \n",
" 1 1.833099 1.769058 0.3449 \n",
" 2 1.622073 1.597633 0.4111 \n",
" 3 1.495829 1.74155 0.383 \n",
" 4 1.408664 1.426265 0.4913 \n",
" 5 1.31305 1.312306 0.5242 \n",
" 6 1.258221 1.284384 0.5386 \n",
" 7 1.223252 1.254078 0.5567 \n",
" 8 1.147496 1.192059 0.5832 \n",
" 9 1.096249 1.219606 0.5758 \n",
" 10 1.033867 1.108408 0.6116 \n",
" 11 1.00289 1.149386 0.6024 \n",
" 12 0.967171 1.07411 0.6243 \n",
" 13 0.929024 1.080522 0.6218 \n",
" 14 0.880732 1.045034 0.6428 \n",
" 15 0.872652 1.043377 0.64 \n",
" 16 0.833563 1.066157 0.638 \n",
" 17 0.789204 1.069749 0.6485 \n",
" 18 0.76321 1.079134 0.641 \n",
" 19 0.731103 1.097915 0.6367 \n",
" 20 0.694054 1.055695 0.655 \n",
" 21 0.667591 1.14352 0.6381 \n",
" 22 0.658845 1.092846 0.6499 \n",
" 23 0.610934 1.12308 0.6493 \n",
" 24 0.590886 1.174162 0.6325 \n"
]
},
{
"data": {
"text/plain": [
"[array([1.17416]), 0.6325]"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l5 = get_learner('cifar10', ResNet18, lr_mixtures=[1, 100], opt_kwargs={'momentum': 0.9}, batch_size=256, metrics=[accuracy])\n",
"l5.fit(0.002, 25)"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fb72d5e30f0>]"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(l4.sched.iterations, l4.sched.losses)\n",
"plt.plot(l5.sched.iterations, l5.sched.losses)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fb72d5e35f8>]"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(l4.sched.epochs, l4.sched.val_losses)\n",
"plt.plot(l5.sched.epochs, l5.sched.val_losses)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(199.64356158300143, 199.2686939790001)"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l4.sched.times[-1], l5.sched.times[-1]"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fe74cfa316404a1aa12b8c6ad5be941a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, description='Epoch', max=25), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch trn_loss val_loss accuracy \n",
" 0 2.333693 2.25895 0.1585 \n",
" 1 2.138221 2.286581 0.1665 \n",
" 2 1.99575 2.073075 0.238 \n",
" 3 1.849084 1.764366 0.3616 \n",
" 4 1.71922 1.621802 0.3975 \n",
" 5 1.640688 1.600503 0.4145 \n",
" 6 1.568429 1.584809 0.4165 \n",
" 7 1.520099 1.589278 0.4396 \n",
" 8 1.46776 1.433847 0.4777 \n",
" 9 1.414126 1.487062 0.4435 \n",
" 10 1.385853 1.540156 0.438 \n",
" 11 1.353192 1.382214 0.5017 \n",
" 12 1.29825 1.337673 0.5103 \n",
" 13 1.27531 1.315523 0.5033 \n",
" 14 1.240622 1.304367 0.5277 \n",
" 15 1.202035 1.240528 0.5623 \n",
" 16 1.17695 1.310139 0.532 \n",
" 17 1.136744 1.199139 0.5814 \n",
" 18 1.128084 1.330841 0.5411 \n",
" 19 1.095596 1.157398 0.5951 \n",
" 20 1.072558 1.157097 0.5943 \n",
" 21 1.037252 1.126625 0.6029 \n",
" 22 1.018133 1.157552 0.5878 \n",
" 23 1.00755 1.199969 0.5774 \n",
" 24 0.970134 1.109831 0.6079 \n"
]
},
{
"data": {
"text/plain": [
"[array([1.10983]), 0.6079]"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l6 = get_learner('cifar10', ResNet18, lr_mixtures=[1], batch_size=256, metrics=[accuracy])\n",
"l6.fit(0.01, 25)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "145b82525e9f41de9bc21c24a7277b58",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, description='Epoch', max=25), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch trn_loss val_loss accuracy \n",
" 0 2.362152 2.358469 0.118 \n",
" 1 2.205076 2.369066 0.1446 \n",
" 2 2.068088 1.9681 0.2944 \n",
" 3 1.933769 1.995786 0.2539 \n",
" 4 1.812195 1.876066 0.2893 \n",
" 5 1.719432 1.924503 0.3154 \n",
" 6 1.668425 1.716223 0.3847 \n",
" 7 1.606203 1.606094 0.4121 \n",
" 8 1.571426 1.717853 0.3845 \n",
" 9 1.522738 1.613091 0.4238 \n",
" 10 1.478501 1.776256 0.3562 \n",
" 11 1.457048 1.412983 0.492 \n",
" 12 1.41999 1.415884 0.4782 \n",
" 13 1.386592 1.596297 0.4351 \n",
" 14 1.351642 1.616859 0.423 \n",
" 15 1.323971 1.426475 0.4911 \n",
" 16 1.297602 1.930734 0.3703 \n",
" 17 1.266151 1.327317 0.5138 \n",
" 18 1.239204 1.281262 0.5419 \n",
" 19 1.223504 1.336731 0.529 \n",
" 20 1.215347 1.240484 0.5531 \n",
" 21 1.175136 1.423947 0.4943 \n",
" 22 1.158771 1.326212 0.5188 \n",
" 23 1.140405 1.445459 0.4635 \n",
" 24 1.108813 1.198997 0.5694 \n"
]
},
{
"data": {
"text/plain": [
"[array([1.199]), 0.5694]"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l7 = get_learner('cifar10', ResNet18, lr_mixtures=[1,100], batch_size=256, metrics=[accuracy])\n",
"l7.fit(0.001, 25)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fb72d41ba90>]"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(l6.sched.iterations, l6.sched.losses)\n",
"plt.plot(l7.sched.iterations, l7.sched.losses)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fb72db39550>]"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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kSnooCfBO8PZSDGgbzZIdZhk/p/j4w8Sv4JYfah6OqU5YS7PgyLpPTMaEEBW0htwMiO1QKcDvsbJFwiIS4J00KCWaA8dOsy+vDpksYXEms8ZZvSaapQMz5jp/DOF+jmeZYZmY9hDRxmyTHrxHkgDvpIEpZhx+yQ4nx+Fdod2lENxMhmnE2SoyaGI7mCUlQ1pID95DSYB3UtuYYFqEBbDE2XRJV/D2hR43wPYf4cQh69ohGpeKGawxHcxjZKKkSnooCfBOUkoxMDmaZTuPUF5uYQpaz4mgy8xYvBBgevAB4RDSzPwelSQ9eA8lAb4OBqbEkFdQzLZDJ6xrREwKtB5oUi4l11mA6cHHdDDptGB68McPQGmRpc0SDU8CfB0MSqlYxs/CYRowM1vzdsLepda2QzQOuekQ2/7M75GJgDZ1kIRHkQBfBy3DA2kbE1y3fHhX6DwG/MNgzYfWtkNY71QeFOScGX8HSZX0YLUGeKXUNKVUtlJqUzWvhyulvlFKrVdKbVZK3eL6ZjZeA1OiWbk7j5IyC1fN8QuCrtea2jTn1rcRniXXdoM1tnKAt82xkADvcezpwb8P1DR3/l5gi9a6OzAMeFEp5Vf3pjUNg5JjKCguY/3+Y9Y2pNdEKD1tqkwKz1VRgyam0hBNSDPwCZQA74FqDfBa64VATYUsNBCqlFJAiG1fj5laOSA5GqUszocHiOsFzbvKMI2ny0k36wVEtD6zTSlbqqRMdvI0rhiDnwx0ArKAjcADWmuPWeU3IsiPLnFh1ubDg60A2UQ4uA4ObbS2LcI6uemmHLWX99nbIxOlB++BXBHgRwLrgDigBzBZKRVW1Y5KqUlKqTSlVFpOTo4LPrpxGJQcw9p9RzlVbPEXl27jwNsP1sjMVo+Vs/3sDJoKFbnwkkrrUVwR4G8BvtLGDmA30LGqHbXWU7XWqVrr1NjYWBd8dOMwMCWGkjLNqj1HrW1IUBR0Gg0bPocSJ2rVi6atuADy952dQVMhMhFKCqDA4m+aokG5IsDvAy4CUEo1BzoAu1xw3CajT2Ikvt6KpVanS4IZpik8Btu+tbolrnfiEGT8bHUrGq/cDPNYVQ9eygZ7JHvSJD8FlgEdlFKZSqnblFJ3KaXusu3yDDBQKbUR+AX4i9a6EUS6hhPk50PP1pHWj8MDJF1obrC5483W2ffDJ9dLKmh1cs+pQVOZpEp6JJ/adtBaT6jl9SzgUpe1qIkalBzDK79s52hBMZHBFmaJenlBjz/A/OfMH3NFz62pO7AGMn60PV8NySOsbU9jlJNuFoGJTj7/tYqsGgnwHkVmsrrIJZ2bozVMW9IIvgL3/D2gYO3HVrfEdRb82xTQQsH+VVa3pnHKTTc9dR//81/zDYDQOEmV9DAS4F2kc1wYV3WPY+rCXRw4dtraxoTHQ8pFsO5jKC+zti2ucHA9bJ8DA+6DZp0gc6XVLWqccrafPYP1XJIq6XEkwLvQX0aZ5KHn52yzuCWYm63HD8DOeVa3pO4W/Bv8w6HfnRDfBzJXQbnHTLWwT1mJKTgXU8UN1gpSNtjjSIB3oVYRgdwxpC2z12exZp/FKZMdLoeg6KZ/s/XQRpMR1P9uM0ST0NfcZD2SYXXLGpe83VBeWnsP/kSWpNB6EAnwLnb3sGRiQ/155tstzi/I7Qo+ftB9AqR/Dyeb8KSyBc+bSpn9bUlb8X3N434ZpjlLxTJ9VWXQVKi44X5sb703RzQOEuBdLNjfh0dHdmDtvmPMXp9lbWN6TjS9ug2fWdsOZx3eDFu/gX53QWCk2RadAgERMg5/rt+KjLWrfh8pG+xxJMDXg+t6xdMlLozn52yjsMTCm5zNOpoe75qP6n+Ken4mFLl4ZasF/wa/UDM8U8HLC+JTJZPmXLnbTZZMQJVVQgzJhfc4EuDrgZeX4skrO5OVX8i7iyye1Ntrovn6vv3H+vuMvN3wRn/47ygocVEGUfZWU9++3yRTgqGy+L6Qs00mPFWWk171DNbKgmPAN1gCvAeRAF9P+reNZmSX5rw5fyfZxy28qXXBOGjWBWbda6b6u1pZCcy4DdDmhuj3f3LNcRf8G/yCTWrkuRL6mM/LTHPNZzV15eWmTEFN4+8gZYM9kAT4evTYqE6UlJXzn7np1jXCNwCu/68pRPXVJNenF/76nJlZOuYNGPqoWfy7rtUsc9Jh80zoe8f5vXeAVqmAMumSwqTDlhTU3oMHSZX0MBLg61FiTDA3D0xk+upMNh2wcDghtgNc/m/YvQCWvOy64+5aAItfNot+d7kahj0GbYeZXvzB9c4fd+EL4BsEA/5Y9esBYWbCk2TSGPZk0FSomOwkZYM9ggT4enbfiHZEBvnx/76zOG2y50Szbuu8Z2Hfirofr+AIzLzTZG1c9i+zzcsbrn0PAqPgixvhtBPLGOZmwKYZ0Oc2CI6ufr/4PmaIRiY8mRmsUHMOfIXIRLO048nD9dok0ThIgK9n4YG+PHRxO5bvymPuFgv/qJSCK1+GiASYcTucrsNELK1h9n1w6ogJ6H7BZ14LjoFxH5ismpl3OR6AF75glpwbeH/N+yX0haL8MxUUPVluukkdDbZjjQVJlfQoEuAbwIS+rWnXLIR/fr+V4lILe5wB4XDtNDObcfb9zn9NX/WumUB18d+hZbfzX0/oC5c+a+rHLHnF/uMe2Qkbp0PqrRBSS7CqmPAk4/BnatAoVfu+kirpUSTANwAfby/+dkUn9hw5xYfL9ljbmPjecNHTsHU2pE1z/P2Ht8DcJyDlkrPz08/V707oMhbmPQO7F9p37IX/MUsO1tZ7B5nwVFluun3DM2C+waEkwHsICfANZFiHZlzYPpZXf8kgr6DY2sYMuA9SLoYfHoNDm+x/X8lp+PJWUzrg6rdq7jEqBVe9bgLxl7fC8YM1Hztvl1lqMPVWCG1ee1u8vMw4vKdPeCo4YobK7LnBCqaUcFgrSZX0EBLgG9ATV3TiVHEZr/xs8bixlxdcPQUCI0zwLS6w731zn4CcrXDNlNqHUAD8Q2DcR1B8CqbfbHLmq7PoRfD2hUEP2NcWMENBnj7hqSKDxt4ePEjZYA8iAb4BtWseyg19W/Pxin1kHHbxtH5HhcTC2KnmJuWcv9S+/7bvzNj7gPtMrXl7NesIV70G+5fDz/9X9T5H98D6z6D3zRDawv5jx8uEpzM1aOzIga8QlSgB3kNIgG9gD13SniA/b579fqvVTTE560MehrUfwcYvq98v/4CZCduyuxm/d9QF10HfSbBsMmz++vzXF71olppzpPcO0Ko3Hj/hKXe7mTMQnmD/eyIT4eQh881KuDUJ8A0sKtiP+0e0Y356DvPTs61ujpmclNAPvnmw6nHZ8jKT715abDJwfJxcb/bSZ80M1Fn3Qe6OM9uP7oV1n0CvmyAszrFjBoRBs86ePeEpJ93c5/By4E+5IpNGyga7PQnwFrhxYBvaRAfx7HdbKS2zeKKOty9c+64JEF/eagJ5ZYtfhj2LzEzYmBTnP8fHz+TH+/jBFxPPjPsvftn03gc/5NxxEzx8wlNuLcv0VUVSJT2GBHgL+Pt489ioTmRkn+Sj5XutneEKENEarpoMWWtg3j/ObN+/ytSa6TIWevy+7p8THm/+McneCt8+BMf2m9o1PSdCeCvnjhnvwROeik5C/n77M2gqyGQnj1FrgFdKTVNKZSulqs2nU0oNU0qtU0ptVkotcG0T3dPILs3p3zaKv3+zhcHP/8pfvtzArHUHyDlRZE2DOl8FqbfB0tch4yeTmTLjNhN4r3zZvkk09kgeAcMfNymR/xtrtjnbewfbjVaaVj78yRxTCjm7jvdhKpYttKfIWGVBUabOvqRKuj0fO/Z5H5gMVLm4p1IqAngTuExrvU8p1cx1zXNfSinenpjKrHUHWLIjlzmbDvJ52n4AOrYIZVBKDINSoumbFE2Ivz3/mVxg5LOwb7kZc0/oZ8oN3PqDSad0pSF/MuPmO34ymTMRDtwgPFfFhKf9K03Rs8boVB7sXQK7F5lJXzm2wB6dAveucmz8vLKKGjSO9uArygZLD97t1Ro5tNYLlVKJNexyA/CV1nqfbf9GcOewaQgP9OXGAYncOCCRsnLNpgP5LN6Ry9KduXy0fC/vLd6Nj5eiR0KELeDH0CMhAj+fehpZ8w00pYWnDjOlCIY/YXLNXc3Ly6RoLnkVBtxb92PF92lcmTSF+bB3qQnoexbaJpNpk+2S0A+6XQ9lpTD/Odj1q2Npp5XlpoPyhqi2jr83KvHMPxDCbbmia9ge8FVKzQdCgVe11lX29kX1vL0U3RMi6J4Qwb3DUygsKWP13qMs2ZHLkh25vD4vg1d/ySDIz5tu8eF0jQuna6twurYKIykmBG8vFw2hxHaAse+YnuaQh11zzKoERcElf3fNsRL6mm8Dp4+5/tuGPcrLYdc8c812L4KD60CXg7e/advwxyFxiEnrrMhCKi2CVe/AyqnOB/icdBPcnclsikw0Q3Hl5c5/gxCNnisCvA/QG7gICASWKaWWa63P6x4opSYBkwBat27tgo92XwG+3r/12gHyT5WwbNcRlu3MZX1mPh8t30uRrXBZoK83nVqGmoAfF07nuDDaNw91qqdfVq4pSBqJX8ooAry8XXpO9aZiHP5AminB0NAW/hvm/xO8fE1bhj5qAnp8H7PgSlV8/KH3LaZ6Zt5usxCHo5zJoKkQmQilhSYf3tH0VNFkuCLAZwK5WusCoEAptRDoDpwX4LXWU4GpAKmpqbLigAPCg3y5rGsLLutqZnqWlpWzM6eATQfy2Zx1nE1Z+Xy15gAfLjO5zb7eig4tQukaF0675qGUlZdzsrCUE0WlnCws5WRRKSd++72Ek7btBcVmkfAWYQH88siFBDfU+H9dVEx42r+q4QN8cQEsfwvaXwbX/Rf8gux/b+otsPglM0N45LOOfW5Zianf0/FKx95XoXImjQR4t+WKv95ZwGSllA/gB/QDXLhskKiKj7cXHVqE0qFFKNf2NtvKyzV7806xOSufTQeOszkrnx83H+KzVebmrVIQ4udDSIAPoQE+hPj7EB7oS3xEICH+tm0BPpSXa16bt4P3l+7h3uF1yH1vKBUTnqzIpFn3CRQeg8EPOxbcwQTWTqPNTOLhj59dV782ebugvLQOPfhKufBtBjp3DNHo1RrglVKfAsOAGKVUJvA04AugtZ6itd6qlPoB2ACUA+9qrR0oUShcxctLkRQTTFJMMFd2M70yrTV5BcX4+3oT5OuNl51j9ZuzjvP2gp38oX8bwgN967PZrpHQBzbNbNgx5fIyWP6mmaHr7M3ovnea9Wc3fGF69PZypgZNZeEJZoKZZNK4tVr/ErTWE7TWLbXWvlrreK31e7bAPqXSPi9orTtrrbtqrR1Y4UHUN6UU0SH+hPj72B3cAR6+tD3HC0t5Z+GuemydC1kx4Sl9julJD7zP+XkCrftD8wtg5TuOLcCSW8cA7+MHYfGSC+/m5Pa5qFKXuHCu7NaSaUt2k3vSoslXjqjoQTfkMM2yNyC8NXQc7fwxlIJ+kyB7s8mVt1fOdhOg/UOc/+zINtKDd3MS4EW1HrqkPYUlZbw1f6fVTalddAoERjZc4bEDq2HfUrOqlXcdb2VdcL1p+4q37X9PbrrjM1jPFZUkAd7NSYAX1UqODeHaXvF8tHwvB/NPW92cminVsBOelr1hVrbqNbHux/INNPV4tn1nZg/XprwccjMcn8F6rshEKMg2NW2EW5IAL2p0/0Xt0Frz+rwdte9czw4fL2T13qPV7xBvW+Hp9LH6bcix/aaufe+bwD/UNcfsc7uZHGXPOrnHM6HkVN178BWpklI22G1JgBc1SogKYkLf1nyxaj/7jlizQITWmk9X7uPiFxdw/ZSl7MiuZjWshEoTnurTClt+Qb+7XHfMyDbQYRSsfh9KCmve19kaNOd9ppQNdncS4EWt7huego+3smQt2f15p/jDeyt47KuNdGkVRqCvN6/8nFH1zq16m9S/+lyIu/A4rP4Aulxjyh+7Ut9JZgHtzTNr3s+ZdVirImWD3Z4EeFGrZmEB3DQgkZnrDjTYWrLl5ZoPlu5h5CsLWb8/n+euuYBP7+jPLYOS+HbDQbYdOn7+m/xD63/C05oPofiESY10tbbDTNotz5BKAAAfqElEQVTjyrdrTpnMSYfAKAiOqdvnBUaCf7ikSroxCfDCLnddmEywnw8v/VT/vfhdOSf53dRlPD17M30So5j70FBu6NcapRR3DGlLqL8PL1fXjvg+kLm6flZ4Kis1wzNtBkNcT9cfXynTi89aa7J0qlOXGjTnfp6kSro1CfDCLpHBftw2OIk5mw6xMTO/Xj6jrFwzdeFORr26iPRDJ/jP9d15/5Y+xEUE/rZPeJAvtw1J4sfNh9l0oIp2xPexTXhKd30Dt84yKyjVtcRxTbqPN4tx1JQymZPu/ASnc0mqpFuTAC/sdvuQJCKCfPnPXNcHz+2HTzD2raU89/02hraP5eeHL+S63vGoKmaI3jo4ifBA36q/TVRMeHJ1PrzWsHQyRCWbwmL1xT8Uev7ejMOfOHz+6wW5cDrPNT14MOPwx/aasgvC7UiAF3YLDfDlrguTWbA9h1V78lxyzJKycibPy+DK1xazP+8Ur0/oydSJvWkWVk2ZXSAswJdJQ9syb1s2a/adkzZZMeHJ1ePw+5aZNWsH3FP/tW763AHlJbDmg/Nf+60GjQsDfFkxnDjomuOJRkUCvHDITQMSiQ3154Uf0+u8WPjmrHzGTF7Cf+Zu59IuzfnpoaGM7h5XZa/9XDcPTCQ62O/8sfiKCU+uzqRZ9oa5sdn9BtcetyoxKZB8kcmJLys5+7XfMmhcNEQjmTRuTQK8cEignzf3DU9h5e48FmXkOnWM4tJyXpqbzpjJS8g+UcSUP/Rm8g29iA7xt/sYwf4+3HVhMosyclm5+5xvE/F9TSA8XcOkKEcc2Wlmmfa5zfGSwM7qO8n0qrfOPnt7znaz9F+Yi1I0JRferUmAFw4b3zeBVhGB/Geu47349fuPMfr1xbw2bwdXdY/j54eH/raIiaP+0L8NsaH+vHhuO36b8FRDJoojlr8J3r5m6KShtLvE9K5XvnP29tx0iGnnumGi8HizrqukSrolCfDCYf4+3jxwUTs2ZOYzd0sVNwKrUFhSxr/mbOOaN5eQf7qE/97ch5d+14OIICfWE7UJ9PPm3mHJrNidx9KdR8684MoJT6fyYO3HcME4CG1e9+PZy8vb/IOybxkc3HBme852142/g/mHKzxeevBuSgK8cMrYXq1oGxPMS3O3U1Zecy9+9d48Ln9tEVMW7GRcagJzHx7K8I7NXNKO8X1b0zI84OxevCsnPKVNg9LT5uZqQ+v5ezMcs9KWMll00tShcdX4ewVJlXRbEuCFU3y8vXjwkvakHz7BtxuyqtzndHEZz3y7heumLKOopJyPbuvLv67tRliA61aICvD15r4RKazZd4z523POvOCKCU+lRbByKiSPgOZd6t5YRwVGQrdxsPFL800i10U1aM4VmSgB3k1JgBdOu/KClnRsEcrLP22npOzsQLp81xEue3Uh7y3ezR/6teHHh4YypF1svbTj+t4JxEcG8vJP28/04hP61n3C06YZcPIwDKiHsgT26jsJSgtNiYSKAO+qHPgKkYlwKheKGqYMhWg4EuCF07y8FI9c2oE9R04xY7WpY15QVMpTszYxfupytIZP7+jPM1d3JcTfFeu7V83Px4v7bfcEfqq4JxBfxwlPFRObmnU2PXirNO9iSiOseg+yt4CXD0S1de1nSKqk8w6shu1zrW5FtSTAizq5uFMzuidE8OovGczbdphLX17IR8v3cuugJH54cAgDkqMbpB1je7YiKSaYl37aTnm5huhkk7fu7Dj8rvlmGb0B9zq/3qqr9JsE+ftgzUcmuHu7eBF0SZV0TkEufDwOvryl9hLPFpEAL+pEKcWjl3bgYH4ht76fhr+PF9PvHMBTozsT5Fd/vfZz+Xh78cBF7dh26ARzNh2q+4SnZZMhuJlZTs9qHa6AsFamRIGratBUVtGDl1RJ+2kN3z5khraKT8LOeVa3qEoS4EWdDUqJ5pZBidw7PJnvHxhCamKUJe0Y3T2Ods1CePlnW2ZPQh/nJjxlb4UdP5vxbx/7J1/VG28fSL3VPHf1+DtAYAQERLhfD764Hheo2TTDTEIb/jdz7bbMqr/PqgMJ8KLOlFI8PboLj47sSICvt2Xt8PZSPHhxe3ZknzSZPRXj8JkOTnha9gb4BJ4Jqo1B75vr936Au2XSFByBl7vA7D/WXFvfGScOwXePmG+Igx+GjldA+hwoLXbt57hArd+hlVLTgCuBbK111xr26wMsB36ntf7SdU0Uwn6juragY4tQXvk5gyvu6YmP8jKpjllrTT57SaHt0fZTWnj+45GdZr3V4Ia5f2CX4Bi4Z1n9HT8q6ewJVU3d6mlmSGvNhyat1FULtGgN3zxg/n9y9Vvm21XnMbDuY9i9wMxAbkTsGSR9H5gMfFjdDkopb+B54EfXNEsI53h5KR66pD13frSamVuOc33rAZDxo/lR3uAbCD4B5rHyc58Ac1PWNwBa94chf7L6VBpWZCJs/daUDfay7luYS5QWw8p3oe1wM+ntpyfN0JYrgu+6j2H7DzDyn6ZkBJiVuPzDYMvXTS/Aa60XKqUSa9ntj8AMoI8L2iREnVzauTkXtArntXkZXP3Q1/iWnTZDLt6+1mfENFaRiaZE8fEDENHa6tbUzeaZcPIQjHkD2gyAaSPhy1vh9p/rdg/j2H744TGTtlp5wXUff7Ng+rbv4MpXXJ/lVAd1HoNXSrUCrgGm2LHvJKVUmlIqLScnp7bdhXCKUoqHL2nP/rzTTF9zCALCwcdPgntN3CVVUmtY/obJNkoeAX7BMP5TE4Q/+Z2ZEezscWffZ77hjJl8frG3zmPMzfw9i+p+Di7kipusrwB/0VrXuiSM1nqq1jpVa50aG1s/sxqFABjWIZaerSOYPC+DolJZrahW7jLZae9SOLge+t99JghHJMDvPjbfTqbfdH6NfXukvWfmRoz8f+Z+xbmSR4BfSKPLpnFFgE8FPlNK7QGuA95USl3tguMK4TSlFI9c0oGs/EImz9tRa0E0jxfWysySbeq58MvftNXwGX/29tb9YPSrsHuhGWZxRN4umPukCeK9b6l6H99AaD/S3McoK3Wu7fWgzgFea52ktU7UWicCXwL3aK2/rnPLhKijQSnRjOzSnNfn7eCK1xaxYLsMC1bL2wfCE5p2Dz5vtxkHT7216oVZetwAA/8Iq96BVe/ad8zyMvj6XvDyhater3mYr/MYM/Fp31Ln2l8Pag3wSqlPgWVAB6VUplLqNqXUXUqpu2p7rxBWUkox5Q+9eX1CTwqKS7lp2komvreCLVnHrW5a49TUywaveNtWR//26ve5+O/Q7lL4/s+mN1+b5W+ZgD3qX6Zufk1SLjHlnRvRME2tAV5rPUFr3VJr7au1jtdav6e1nqK1Pu+mqtb6ZsmBF42JUorR3eP4+eELeeKKTmzIzOeK1xfxp+nrOZh/2urmNS6RiXC0iQ7RFB6Htf+DLmMhLK76/by84dr3TIrjFzea4Zfq5GyHX/4BHS6H7hNqb4NfkEmT3PqN6fk3AjKTVXgEfx9vbh/SloWPDueOIW2ZvS6L4f+Zzws/buNEoRM33dxRZKLJBDl9zOqWOG7tR1B8wr6FWQLCYMKn5vkn480/DucqK4Wv7zJB+8pX7M/A6jzGlJjev8L+ttcjCfDCo4QH+fL45Z345ZELGdmlBW/8upNhL8znw2V7zqtp73EqMmmO7bW0GQ4rL4MVU6D1QIjrad97otrCuA8hbyfMuO38HveSV0wp4CtedGypxnaXgrc/bJld+74NQAK88EgJUUG8Or4ns+8bREqzEJ6atZmRLy/kh02HHF5I3G001Vz4bd/CsX0mNdIRSUNh1POQMRd+/r8z2w9tgvn/gi7XQNdrHTumfyikXGwKkdVlNTEXkQAvPFq3+Ag+m9Sfd29MRSm463+ruX7KMr5YtZ+sYx42Rh/ZxjzWd6rkicNmGUJXBcDlb0FEG1P0y1F9bjc/S1+DdZ+YMgcz7zIVNi9/0bn2dB5jcu4POFjkrh40XMFuIRoppRQXd27OsA6xfJ62n8nzdvDnGabwVkqzEIa0i2Fo+1j6JUU1aI37BhcQburxONCD11ozc+0BhraPJSbEjtLKm782ddRP55kVqi56yvn2AhxYA/uWmdowztbQuexfZjnEbx6A7T/C4Y1m9quzxeY6XGbSKrd8bUpWW0hZ9XU0NTVVp6WlWfLZQtREa0364RMs2p7LwowcVu7Oo6i0HD9vL1ITIxnSLpYh7WLo3DIMLy83K3/wzkXmJuE1b0PioFp3/2pNJg9/sZ6rusfx2oQaxr9PH4XvH4WN0yGul0nJ3DTDfE738dW/rzYzbof0H+DhLebmqbNO5cE7I0wWUfcJcE2tlVdq9vE4s67AgxtcXiJDKbVaa51q174S4IWoWWFJGav25LFwew6LMnLZdsgsTh0T4sfglBiGtIulf3I0ceEBqKZe72b3Qph5NxzPNEMNl/zjzM3Xc+SfKmHEi/PJP11CudYseHQ4CVFVTDDa8QvMug8KsuHCv5ga6mj46BqTbXLTN6aCp6OOZ8ErF5iFWS77p+PvP1duBqx8B4Y/boZo6mLtxzDrHrjjV2jVq+5tq0QCvBD16PDxQhZnmN794oxcjhSYhR4CfL1oHRVEm+hg2kQF0SbG9hgdRKuIQHy8G/aWV1FpGZlHT5McG+LYG4tPmSULF78M5aVmXdrBD5/XQ/7bzI18unIf796Uyp0frWZC39b8Y0ylJSOKC8wU/7T3TE32sW+fneVyKg/evcikKd4x78w9AHv9/HeT7XL/2mr/EbLMqTz4TzsYcB9c8neXHloCvBANpLxcs+XgcdbuO8reI6fYc+QU+/IK2HvkFEWlZ24i+ngpWkUG0joqiMToYNpEBzGsQywpzULrpV25J4u4/YM01u0/xvu39GFYh2aOH+R4lpnos/5Tsz7tRU9Cj9+Dlzfr9h/jmjeXcPPARJ4e3YU/f7meWeuyWPLXEWYsft8Kk0eet9v8AzHiCVOv5byGZpggHxoHt821f5il+BS83BkSB8Pv/uf4uTWEj8aaiVT3r3XpMI0EeCEsVl6uyT5RxN4jJtjvtQV981PA8cJS/H28eO6aC7i2dy1T4B20I/sEt7y/ipwTRcSG+nO6uIzvHxhCs9AA5w54YDX88DjsXw4tLqDskue46jtF7skifn74QkIDfNmRfZJLXl7A/Re24SGfGaZnHRYP17xlgnBNdv4K/7sWUi6CCZ/Zd7N01Xvw3cNwyxxoM9C586pvqz+Ab+6HOxdBy24uO6wEeCEauYP5p3no83Us35XHjQPa8MQVnfHzqfsQztKdudz10Wr8fLx496Y+BPt5M3ryYvokRvHBLX2dvymstVlI46enIX8fc8r64D/q/zFi4Jmx83+8+znjMp+lI3uh50QY+Zz9PfKKgN3/ntrH08vL4c1+pu7LpPmNt85/wREzTDP4IfPtx0UcCfCSBy+EBVqGB/K/2/pxx5AkPly2lxveWU728cI6HfPL1ZncNG0lzcICmHnPIHokRNCueShPXdmFRRm5vLOohrortVEKuo7l8I0LeVWPZ7jPRob/Mhp+espkyCx+mSey7iNaH2Vut1fMohiOZLX0uc2skrT8TUj7b8377vzFpDUOuLfxBncwaZaJg03xMYs60hLghbCIj7cXf7uiM69N6MnmrONc8fpi0vY4vuKQ1pqXftrOn6avp09iFDPuHnhWNsuEvgmM6tqCF35MZ/3+utWZeebH3bxRdjU5Ny9DXTAOlrwGL7SDn/8Prw4jeaz5VJ5Ob01xqROTmC591swC/f5PsGtB9fstewNCW0LnJrDsROcxcCQDcrZZ8vES4IWw2FXd45h570CC/LwZP3U5Hy7bY3e5hKLSMh76fB2v/ZLBdb3jef+WvoQHnr0mqFKKf43tRvOwAP746Vqni6stysjh2w0HuXdYCglt2sLVb8CkX80M0mumwriP+P1FvTmYX8isdQcc/wBvH7huGkSnwBcTIXfH+fsc3gK7fjWzT338nDqPBtVpNKAsKyEsAV6IRqBjizBm3zeYoe1jeWrWZv40fQOFJTWXnD12qpiJ763k63VZ/OnS9rxwXbdqx/HDg3x5dXwPMo+e4omvNzlcb6ewpIwnv95EUkwwdw1re+aFuJ4w7gPo/jtQimHtY+nYIpS3F+6i3JlVtALC4YbPzepSn4w7fw3VFW+ZBdRTb3X82FYIaQZtBkmAF8LThQf68u6NqTxwUTtmrMnk2reWsj/vVJX77sktYOybS1m37xivju/BfSPa1TrJKjUxigcvbs+sdVl8tcaxHvaUBTvZc+QUz4zpir9P9VkuSinuHpbMjuyT/LIt26HP+E1kollDNX//2WuoFuTC+s/NzNegKOeObYXOV5myDDnbG/yjJcAL0Yh4eSkeuqQ9792Uyr68U1w1eTGLMs5eanD13jzGvrWUvFPFfHxHP8b0aGX38e8dnkK/pCienLWJXTkn7XrPntwC3py/k9Hd4xjcLqbW/a+4oCXxkYG8NX+H85U52ww4s4bq938yNynTpkFZkeNVI63WabR53NrwvXgJ8EI0Qhd1as7s+wYTG+rPTdNW8tb8nWit+WZ9FhPeWUFYgA8z7xlEn0THerLeXopXxvfAz8eLP366lqLSmoeBtNY8OWsT/t5ePHlFJ7s+w8fbizuGtGXNvmOs2nPUofadpccNMOhBWP0+LHnVrKOacjHEdnD+mFYIi4OEfpYM00iAF6KRSooJZuY9gxh1QUue/2EbV7+xhD9+upZurcL56p5BJMUEO3XcluGB/PvabmzOOs4LP6TXuO93Gw+yKCOXRy5tT7Mw+ydKjUtNICrYjykLdjrVxt9c9DR0vBJ+ftoUQetvx4pNjVHnMXBoIxyp4/VwkAR4IRqxYH8fJk/oyd8u78TGA/mM7h7H/27vR1Rw3TJILu3SghsHtOHdxbv5Nb3qsfIThSX845stdG0VxsQBiQ4dP9DPm5sHJjJvWzbptuJsTvHygrFToWUPc0M3eYTzx7JSp6vM49aGXelJArwQjZxSijuGtmXtU5fy2vgeBPg6Wff8HI9f3omOLUL50xfrq5xk9dJP28k5WcSzV1+AtxMzYG8c0IYgP2/ermsv3i/YFCO7ZU7jnthUk4gEaNW7wYdpJMAL0USEB/q6tBxxgK83r0/oSUFxKQ9/sf6stMZNB/L5YOkeft+vNd0TnCudGxHkx/g+rZm1PovMo1VnA9nNy7vqYmVNSecxkLUWjjbcmre1Bnil1DSlVLZSalM1r/9eKbXB9rNUKdXd9c0UQtSHds1DeXp0FxbvyOXthaaUQXm55omvNxEV7MejIzvW6fi3D0lCAe8uqudlAJuC34Zpvmmwj7SnB/8+cFkNr+8GLtRadwOeAaa6oF1CiAYyvk8CV1zQkhfnprN231E+XbWPdfuP8bcrOp03K9ZRcRGBXNUjjs9X7eeorW6+x4pKgpbdG3SYptYAr7VeCFRbIENrvVRrXZELtRxwbe1TIUS9Ukrx3NgLaB4WwP2freX5OdsY0Daaqx3Ir6/JXRcmc7qkjA+W7XHJ8Zq0zmMgcyXkO1HKwQmuHoO/DZjj4mMKIepZeKAvr03oQdaxQk6XlPHM1V1dNt7fvnkoF3dqxvtL93CquNQlx2wMlu86wrgpyxj75hKO21vfp9MY89hAwzQuC/BKqeGYAP+XGvaZpJRKU0ql5eTkVLebEMICvdtE8dr4nrz8ux6kNHNwmb9a3HVhMsdOlfD5qv0uPa4VNmbmc+O0lYyfupw9RwrYkJnPnR+urnXSGAAxKdD1OgiKrv+GYueCH0qpROBbrXXXal7vBswERmmt7Sq4IAt+COFZrntrKQfzC5n/6DB8G3h9WlfYkX2CF+duZ86mQ0QE+XLPsGRuHJDI9xsP8vAX67myW0teG9/T+UVV7OTIgh8+Lviw1sBXwER7g7sQwvPcPSyZ2z5I49sNWVzTs+ncqss8eopXf85gxppMAn29uf+idtwxJInQAHMDemyveLJPFPGvOdtoFhrAk1d2cmk6a13UGuCVUp8Cw4AYpVQm8DTgC6C1ngI8BUQDb9pOqtTef12EEJ5jeIdmtG8ewpT5u7i6R6tGEwSrk3OiiDd+3cEnK/aBglsHJXH3sGSiQ/zP2/fOoW05lF/ItCW7aRHuz6ShyRa0+Hy1Bnit9YRaXr8duN1lLRJCuCUvL8WdQ5N5ZPp6fk3PZkTH5lY3qUr5p0uYunAn0xbvobisnHGp8fxxRDviIqqfaKWU4qkrO5Nzoojnvt9GbKh/o/iWUuchGiGEsNdVPeJ4cW46r/6yg/JyU3lAKVAobP9DKWV7NO9RKJSChMggWkcH1XT4Osk/XcInK/YxZcFO8k+XMLp7HA9f0t7uom5eXooXx3XnSEERj07fQEyIP0PaxdZbe+1h103W+iA3WYXwTO8v2c3/fbPFqff2SYzk+t4JXN6tJSH+de+flpdrlu06whdp+/lh0yGKSssZ0bEZj1zani5x4U4d83hhCeOmLGN/3ik+v3MAXVs5d5zqOHKTVQK8EKJBlZdr0g+foKSsHK1BY+rOm0cAXWn7mdfW7jvG9NX72ZVTQKCvN6MuaMF1vePpnxTtcOZK5tFTfLk6k+lpmRw4dpqwAB+u7tmKcakJLgnIh/ILufatpRSVlvHV3YNc+s1DArwQwi1prVm7/xjT0zL5dn0WJ4pKiY8M5Npe8VzXO56EqOoDaWFJGT9uPsT0tEyW7MwFYHBKDNenJnBp5+Yuq9JZYUf2Ca59axmRQb58efdAYqq4OesMCfBCCLdXEbC/XJ3J4h25aA3920Zxfe8ERl3QgiA/H7TWbDyQzxdp+5m9LovjheYfhOt7J3Bt71bER9bfmD6Y5RVveGcFHVuE8skd/Ql2wbCSBHghhEc5cOw0M9dk8uXqTPYcOUWwnzeXdG7OtkMn2HboBP4+Xozq2oJxqQn0b+v4kE5d/LTlMHd+lMbQ9rG8c2NqnSd5SYAXQngkrTVpe48yPW0/czYeIik2mHGpCYzuHlfnyph18cmKfTw+cyPX9ornP9d3q9McgAadySqEEI2FUoo+iVH0SYzi39c1nqUpbujXmsPHC3n1lwxahPvXuc6+vSTACyFEA3jw4nZknyjkjV930jwsgBsdXOfWGRLghRCiASileGZMV04WldGqhlmxriQBXgghGoiPtxevT+jZYJ/X9Gp2CiGEsIsEeCGEcFMS4IUQwk1JgBdCCDclAV4IIdyUBHghhHBTEuCFEMJNSYAXQgg3ZVmxMaVUDrDX9msMkGtJQxoHTz9/kGsAcg1AroE9599Ga23XWoCWBfizGqFUmr3V0dyRp58/yDUAuQYg18DV5y9DNEII4aYkwAshhJtqLAF+qtUNsJinnz/INQC5BiDXwKXn3yjG4IUQQrheY+nBCyGEcDFLA7xS6jKlVLpSaodS6q9WtsXVlFLTlFLZSqlNlbZFKaV+Ukpl2B4jbduVUuo123XYoJTqVek9N9n2z1BK3WTFuThDKZWglPpVKbVVKbVZKfWAbbsnXYMApdRKpdR62zX4u217klJqhe18PldK+dm2+9t+32F7PbHSsR6zbU9XSo205oyco5TyVkqtVUp9a/vdo84fQCm1Rym1USm1TimVZttW/38LWmtLfgBvYCfQFvAD1gOdrWpPPZzfUKAXsKnStn8Df7U9/yvwvO355cAcQAH9gRW27VHALttjpO15pNXnZuf5twR62Z6HAtuBzh52DRQQYnvuC6ywndsXwHjb9inA3bbn9wBTbM/HA5/bnne2/X34A0m2vxtvq8/PgevwMPAJ8K3td486f9s57AFiztlW738LVp7wAODHSr8/Bjxm9X8IF59j4jkBPh1oaXveEki3PX8bmHDufsAE4O1K28/aryn9ALOASzz1GgBBwBqgH2Yii49t+29/B8CPwADbcx/bfurcv43K+zX2HyAe+AUYAXxrOx+POf9Kba4qwNf734KVQzStgP2Vfs+0bXNnzbXWBwFsj81s26u7Fm5xjWxftXtierAedQ1swxPrgGzgJ0zv85jWutS2S+Xz+e1cba/nA9E07WvwCvBnoNz2ezSedf4VNDBXKbVaKTXJtq3e/xasXJNVVbHNU1N6qrsWTf4aKaVCgBnAg1rr40pVdUpm1yq2NflroLUuA3oopSKAmUCnqnazPbrVNVBKXQlka61XK6WGVWyuYle3PP9zDNJaZymlmgE/KaW21bCvy66DlT34TCCh0u/xQJZFbWkoh5VSLQFsj9m27dVdiyZ9jZRSvpjg/rHW+ivbZo+6BhW01seA+Zgx1QilVEXnqvL5/HauttfDgTya7jUYBFyllNoDfIYZpnkFzzn/32its2yP2Zh/6PvSAH8LVgb4VUA72x11P8xNldkWtqchzAYq7nzfhBmXrth+o+3ueX8g3/aV7UfgUqVUpO0O+6W2bY2eMl3194CtWuuXKr3kSdcg1tZzRykVCFwMbAV+Ba6z7XbuNai4NtcB87QZbJ0NjLdlmSQB7YCVDXMWztNaP6a1jtdaJ2L+vudprX+Ph5x/BaVUsFIqtOI55v/Dm2iIvwWLbzxcjsmu2An8zeobIS4+t0+Bg0AJ5l/e2zDjib8AGbbHKNu+CnjDdh02AqmVjnMrsMP2c4vV5+XA+Q/GfH3cAKyz/VzuYdegG7DWdg02AU/ZtrfFBKgdwHTA37Y9wPb7DtvrbSsd62+2a5MOjLL63Jy4FsM4k0XjUedvO9/1tp/NFbGuIf4WZCarEEK4KZnJKoQQbkoCvBBCuCkJ8EII4aYkwAshhJuSAC+EEG5KArwQQrgpCfBCCOGmJMALIYSb+v8Jx82297TmxQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(l6.sched.epochs, l6.sched.val_losses)\n",
"plt.plot(l7.sched.epochs, l7.sched.val_losses)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Environment (conda_fastai)",
"language": "python",
"name": "conda_fastai"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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