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konstantinzolotarev/Untitled.ipynb

Created November 22, 2020 11:07
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Untitled.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import torch\n",
"from fastai.vision.all import *\n",
"from fastbook import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19.])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = torch.arange(0,20).float(); x"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([ 1., 4., 7., 10., 13., 16., 19., 22., 25., 28., 31., 34., 37., 40., 43., 46., 49., 52., 55., 58.])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y = 3*x+1; y"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f779089fc40>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAD9CAYAAABOd5eOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAU1UlEQVR4nO3df6zddX3H8edbILRre621V6ZdSrFTOgurhGtgayYkdWOabCOtyapsShbTBYKZ+0FEY7HyI61h2f4Q1DVBRSEbdil3U0bIWCFTMpmXNeBuUojIql7A3Sqt/YnI3vvjnDtuz+49P+453/Pje56P5MR7vt/v+Z63H8599Xs/3+/7fCMzkSQNttf0ugBJUvsMc0kqAcNckkrAMJekEjDMJakEDHNJKgHDXJJK4MxmN4yIrcAngdXAC8DVmfmNiNgE3FFd/lh1+cF6+1q5cmWuWbNmwUVL0jB6/PHHD2Xm6FzrmgrziPhN4NPA7wP/DryxunwlsBf4EPA14GbgXuDSevtbs2YNExMTzdYvSQIiYt4D5WaPzD8F3JSZ36o+n6rueBswmZl7qs93AIciYl1mHlh4yZKkVjScM4+IM4AxYDQivhsRP4yI2yNiMbAeeGJm28w8DjxTXV67n20RMRERE9PT0537fyBJauoE6DnAWcB7gd8A3g5cBHwCWAocqdn+CLCsdieZuTszxzJzbHR0zikfSdICNRPmJ6v/+5nMfD4zDwF/BbwHOAaM1Gw/AhztXImSpEYahnlmvgj8EJjr6xUngQ0zTyJiCbC2ulyS1CXNXmf+ReDDEfGGiHgd8BHg68B9wAURsSUiFgE3Ak968lOSTje+f4qNu/Zx3g33s3HXPsb3T3V0/81ezXIzsBJ4GjgFfBW4NTNPRcQW4HbgbirXmW/taIWSNODG90/xsb3f4eTLrwAwdfgkH9v7HQCuvGhVR96jqTDPzJeBa6uP2nUPAes6Uo0kldBtDz71f0E+4+TLr3Dbg091LMxt55ekgj13+GRLyxfCMJekgr1p+eKWli+EYS5JBbv+ivNZfNYZpy1bfNYZXH/F+R17j6a/aEuStDAz8+K3PfgUzx0+yZuWL+b6K87v2Hw5GOaS1BVXXrSqo+Fdy2kWSSoBw1ySSsAwl6QScM5ckpowvn+q0BOY7TLMJamBbrTjt8tpFklqoF47fr8wzCWpgW6047fLMJekBrrRjt8uw1ySGuhGO367PAEqSQ10ox2/XYa5JDWh6Hb8djnNIkklYJhLUgk4zSJpKPR7B2e7DHNJpTcIHZztcppFUukNQgdnuwxzSaU3CB2c7TLMJZXeIHRwtsswl1R6g9DB2S5PgEoqvUHo4GxXU2EeEY8AlwI/ry6ayszzq+s2AXcAq4HHgKsz82DnS5Wkhev3Ds52tTLNcl1mLq0+ZoJ8JbAX2A6sACaAeztfpiSpnnbnzDcDk5m5JzNPATuADRGxru3KJElNayXMd0bEoYh4NCIury5bDzwxs0FmHgeeqS4/TURsi4iJiJiYnp5uo2RJw2h8/xQbd+3jvBvuZ+OufYzvn+p1SX2l2TD/KPBmYBWwG/haRKwFlgJHarY9Aiyr3UFm7s7MscwcGx0dbaNkScNmpoNz6vBJklc7OA30VzUV5pn5WGYezcyXMvMu4FHgPcAxYKRm8xHgaGfLlDTMhqGDs10LnTNPIIBJYMPMwohYAqytLpekjhiGDs52NQzziFgeEVdExKKIODMirgLeCTwI3AdcEBFbImIRcCPwZGYeKLZsScNkGDo429XMkflZwC3ANHAI+DBwZWY+lZnTwBbgVuBF4BJga0G1ShpSw9DB2a6GTUPVwH5HnfUPAV6KKKkww9DB2S7b+SUNhLJ3cLbLL9qSpBIwzCWpBAxzSSoB58wldUXZb6jca4a5pMINww2Ve81pFkmFsx2/eIa5pMLZjl88w1xS4WzHL55hLqlwtuMXzxOgkgpnO37xDHNJXWE7frGcZpGkEjDMJakEnGaR1BQ7OPubYS6pITs4+5/TLJIasoOz/xnmkhqyg7P/GeaSGrKDs/8Z5pIasoOz/3kCVFJDdnD2P8NcUlPs4OxvTrNIUgkY5pJUAoa5JJVAS2EeEW+JiFMRcfesZZsi4kBEnIiIhyPi3M6XKald4/un2LhrH+fdcD8bd+1jfP9Ur0tSB7V6ZH4H8O2ZJxGxEtgLbAdWABPAvR2rTlJHzLTjTx0+SfJqO76BXh5Nh3lEbAUOA/8ya/FmYDIz92TmKWAHsCEi1nWySEntsR2//JoK84gYAW4C/rxm1XrgiZknmXkceKa6vHYf2yJiIiImpqenF16xpJbZjl9+zR6Z3wzcmZk/qFm+FDhSs+wIsKx2B5m5OzPHMnNsdHS09UolLZjt+OXXMMwj4u3Au4C/nmP1MWCkZtkIcLTtyiR1jO345ddMB+jlwBrg+xEBlaPxMyLibcDngQ/ObBgRS4C1wGSnC5W0cLbjl19kZv0NIn6B04++/4JKuF9Tff5d4I+A+4FPAZdl5qX19jk2NpYTExMLLFmShlNEPJ6ZY3Ota3hknpkngBOzdnYMOJWZ09XnW4DbgbuBx4CtnShaktS8lr9oKzN31Dx/CPBSREnqIb81URoQ3lBZ9Rjm0gDwhspqxC/akgaAHZxqxDCXBoAdnGrEMJcGgB2casQwlwaAHZxqxBOg0gCwg1ONGObSgPCGyqrHaRZJKgHDXJJKwGkWqUvs4FSRDHOpC+zgVNGcZpG6wA5OFc0wl7rADk4VzTCXusAOThXNMJe6wA5OFc0ToFIX2MGpohnmUpfYwakiOc0iSSVgmEtSCRjmklQCzplLTbIdX/3MMJeaYDu++p3TLFITbMdXv2sqzCPi7oh4PiJ+GhFPR8SHZq3bFBEHIuJERDwcEecWV67UG7bjq981e2S+E1iTmSPA7wK3RMTFEbES2AtsB1YAE8C9hVQq9ZDt+Op3TYV5Zk5m5kszT6uPtcBmYDIz92TmKWAHsCEi1hVRrNQrtuOr3zU9Zx4Rn42IE8AB4Hngn4D1wBMz22TmceCZ6vLa12+LiImImJienm67cKmbrrxoFTs3X8iq5YsJYNXyxezcfKEnP9U3mr6aJTOvjYgPA78GXA68BCwFapP5CLBsjtfvBnYDjI2N5QLrlXrGdnz1s5auZsnMVzLzm8AvAdcAx4CRms1GgKOdKU+S1IyFXpp4JpU580lgw8zCiFgya7kkqUsahnlEvCEitkbE0og4IyKuAN4H7APuAy6IiC0RsQi4EXgyMw8UW7bUuvH9U2zctY/zbrifjbv2Mb5/qtclSR3TzJx5UplS+TyV8D8IfCQz/wEgIrYAtwN3A48BW4spVVo4OzhVdg3DPDOngcvqrH8I8FJE9bV6HZyGucrAdn4NBTs4VXaGuYaCHZwqO8NcQ8EOTpWdX4GroeANlVV2hrmGhh2cKjOnWSSpBAxzSSoBw1ySSsA5cw0Mb6gszc8w10CwHV+qz2kWDQRvqCzVZ5hrINiOL9VnmGsg2I4v1WeYayDYji/V5wlQDQTb8aX6DHMNDNvxpfk5zSJJJWCYS1IJOM2irrGDUyqOYa6usINTKpbTLOoKOzilYhnm6go7OKViGebqCjs4pWIZ5uoKOzilYnkCVF1hB6dUrIZhHhFnA58F3gWsAL4LfDwzH6iu3wTcAawGHgOuzsyDhVWsgWUHp1ScZqZZzgR+AFwGvBbYDnw1ItZExEpgb3XZCmACuLegWiVJ82h4ZJ6Zx4EdsxZ9PSKeBS4GXg9MZuYegIjYARyKiHWZeaDz5UqS5tLynHlEnAO8FZgErgGemFmXmccj4hlgPXCg5nXbgG0Aq1evbqNk9YodnFL/aulqlog4C7gHuKt65L0UOFKz2RFgWe1rM3N3Zo5l5tjo6OhC61WPzHRwTh0+SfJqB+f4/qlelyaJFsI8Il4DfAX4GXBddfExYKRm0xHgaEeqU9+wg1Pqb02FeUQEcCdwDrAlM1+urpoENszabgmwtrpcJWIHp9Tfmj0y/xzwK8DvZObs3977gAsiYktELAJuBJ705Gf52MEp9beGYR4R5wJ/DLwdeCEijlUfV2XmNLAFuBV4EbgE2FpgveoROzil/tbMpYkHgaiz/iFgXSeLUv+xg1Pqb7bzq2l2cEr9yy/akqQSMMwlqQQMc0kqAefMh4jt+FJ5GeZDwhsqS+XmNMuQsB1fKjfDfEjYji+Vm2E+JGzHl8rNMB8StuNL5eYJ0CFhO75Ubob5ELEdXyovp1kkqQQMc0kqAadZBogdnJLmY5gPCDs4JdXjNMuAsINTUj2G+YCwg1NSPYb5gLCDU1I9hvmAsINTUj2eAB0QdnBKqscwHyB2cEqaj9MsklQChrkklYBhLkkl0FSYR8R1ETERES9FxJdq1m2KiAMRcSIiHo6IcwuptATG90+xcdc+zrvhfjbu2sf4/qlelySpJJo9Mn8OuAX4wuyFEbES2AtsB1YAE8C9nSywLGba8acOnyR5tR3fQJfUCU2FeWbuzcxx4Mc1qzYDk5m5JzNPATuADRGxrqNVloDt+JKK1O6c+XrgiZknmXkceKa6/DQRsa06VTMxPT3d5tsOHtvxJRWp3TBfChypWXYEWFa7YWbuzsyxzBwbHR1t820Hj+34korUbpgfA0Zqlo0AR9vcb+nYji+pSO2G+SSwYeZJRCwB1laXa5YrL1rFzs0Xsmr5YgJYtXwxOzdfaEenpI5oqp0/Is6sbnsGcEZELAJ+DtwH3BYRW4D7gRuBJzPzQEH1DjTb8SUVpdkj808AJ4EbgD+o/vyJzJwGtgC3Ai8ClwBbC6hTklRHU0fmmbmDymWHc617CPBSREnqIb81sQXeUFlSvzLMm+QNlSX1M79oq0l2cErqZ4Z5k+zglNTPDPMm2cEpqZ8Z5k2yg1NSP/MEaJO8obKkfmaYt8AOTkn9ymkWSSoBw1ySSsAwl6QSGKo5c9vxJZXV0IS57fiSymxopllsx5dUZkMT5rbjSyqzoQlz2/ElldnQhLnt+JLKbGhOgNqOL6nMhibMwXZ8SeU1NNMsklRmhrkklcBATbPYwSlJcxuYMLeDU5LmNzDTLHZwStL8OhLmEbEiIu6LiOMRcTAi3t+J/c5mB6ckza9TR+Z3AD8DzgGuAj4XEes7tG/ADk5JqqftMI+IJcAWYHtmHsvMbwL/CPxhu/uezQ5OSZpfJ06AvhV4JTOfnrXsCeCy2RtFxDZgG8Dq1atbfhM7OCVpfp0I86XAkZplR4Blsxdk5m5gN8DY2Fgu5I3s4JSkuXVizvwYMFKzbAQ42oF9S5Ka0Ikwfxo4MyLeMmvZBmCyA/uWJDWh7TDPzOPAXuCmiFgSERuB3wO+0u6+JUnN6dSlidcCi4H/Bv4WuCYzPTKXpC7pSDt/Zv4EuLIT+5IktS4yF3RhSXtvGjENHGxjFyuBQx0qpwjW1x7ra4/1taef6zs3M0fnWtGTMG9XRExk5liv65iP9bXH+tpjfe3p9/rmMzBftCVJmp9hLkklMKhhvrvXBTRgfe2xvvZYX3v6vb45DeScuSTpdIN6ZC5JmsUwl6QSMMwlqQT6MsxbuQ1dRPxpRLwQEUci4gsRcXbBtZ0dEXdW6zoaEfsj4t3zbHt1RLwSEcdmPS4vsr7q+z4SEadmvee8N0rtwfgdq3m8EhGfmWfbroxfRFwXERMR8VJEfKlm3aaIOBARJyLi4Yg4t85+Crl94nz1RcSlEfHPEfGTiJiOiD0R8cY6+2n6c9Gh+tZERNb899teZz/dHr+ramo7Ua334nn2U8j4dUpfhjlN3oYuIq4AbgA2AWuANwOfKri2M4EfULn5xmuB7cBXI2LNPNv/W2YunfV4pOD6Zlw36z3nvB1TL8Zv9lhQ+e97EthT5yXdGL/ngFuAL8xeGBErqXyJ3HZgBTAB3FtnP0XdPnHO+oDXUbnyYg1wLpWvnf5ig301/Fx0sL4Zy2e958119tPV8cvMe2o+j9cC3wP+o86+ihi/jui7MI/WbkP3QeDOzJzMzBeBm4Gri6wvM49n5o7M/K/M/J/M/DrwLDDnv+Z9ruvjV+O9VL6c7RtdfM//JzP3ZuY48OOaVZuByczck5mngB3AhohYV7uPFj+3HakvMx+o1vbTzDwB3A5sbPf9OlVfK3oxfnP4IPDlHNBL/PouzJn/NnRz/Qu9vrpu9nbnRMTrC6zvNBFxDpWa5/uWyIsi4lBEPB0R2yOiI19u1oSd1fd9tM7URK/Hr5lfnl6NH9SMT/Xrnp9h7s9iK5/boryTxvcRaOZz0WkHI+KHEfHF6l87c+np+FWnz94JfLnBpr0Yv6b0Y5g3dRu6ebad+XmubTsuIs4C7gHuyswDc2zyr8AFwBuoHHW8D7i+C6V9lMqUySoqf4Z/LSLWzrFdz8YvIlZTmaq6q85mvRq/Ge18Futt23ER8avAjdQfn2Y/F51yCHgHlSmgi6mMxT3zbNvT8QM+AHwjM5+ts023x68l/RjmrdyGrnbbmZ8Lv2VdRLyGyg04fgZcN9c2mfm9zHy2Oh3zHeAmKlMLhcrMxzLzaGa+lJl3AY8C75lj056NH5Vfnm/W++Xp1fjN0s5nsd62HRURvww8APxJZs47ZdXC56IjqtMlE5n588z8EZXfk9+KiNpxgt7ffvID1D+w6Pr4taofw7yV29BNVtfN3u5HmbngubtmREQAd1I5UbMlM19u8qUJRGGFtf6+PRm/qoa/PHPo9vidNj7Ved21zP1Z7MntE6vTAw8BN2dmq3f36vZ4zkynzfWePbv9ZFTujvYm4O9bfGmvfp/nlpl99wD+jsodi5ZQOaFzBFg/x3a/DbwAvI3Kmf19wK4u1Pd54FvA0gbbvRs4p/rzOuA/gU8WXNty4ApgEZUrb64CjgPn99H4/Xq1pmX9MH7VcVoE7KTy19bM2I1WP3tbqss+DXyr3c9tB+tbRWUO//pOfi46WN8lwPlUDhpfT+VKoIf7Zfxmrd9N5dxNT8avY5/jXhcwz8CtAMarg/V94P3V5aup/Dm2eta2fwb8CPgplcuyzi64tnOp/It8qlrLzOOq2vqAv6zWdpzKJU83AWcVXN8o8G0qf54epvKPzm/2y/hV3/NvgK/Msbwn40flKpWseeyornsXcIDKJZSPAGtmve7jwAONPrdF1Qd8svrz7M/hsbnqq/e5KLC+91G50us48DyVk4u/2C/jV123qDoem+Z4XVfGr1MPv2hLkkqgH+fMJUktMswlqQQMc0kqAcNckkrAMJekEjDMJakEDHNJKgHDXJJK4H8BI09BoUtx3VMAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x, y)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"dl = DataLoader(list(zip(x[:16], y[:16])), batch_size=1)\n",
"vdl = DataLoader(list(zip(x[16:], y[16:])), batch_size=1)\n",
"\n",
"dts = DataLoaders(dl, vdl)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>85.064552</td>\n",
" <td>4.117168</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>35.837463</td>\n",
" <td>0.018493</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>19.894436</td>\n",
" <td>0.001610</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>12.322033</td>\n",
" <td>0.001228</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>8.075933</td>\n",
" <td>0.001190</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>5.471631</td>\n",
" <td>0.001169</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>3.785621</td>\n",
" <td>0.001149</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>2.655622</td>\n",
" <td>0.001129</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>1.880581</td>\n",
" <td>0.001110</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>1.340540</td>\n",
" <td>0.001091</td>\n",
" <td>00:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"learner = Learner(dts, torch.nn.Linear(1, 1), opt_func=SGD, loss_func=F.mse_loss)\n",
"learner.fit(10)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"ename": "TypeError",
"evalue": "'NoneType' object cannot be interpreted as an integer",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_with_events\u001b[0;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnoop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 154\u001b[0;31m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'before_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m;\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 155\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_cancel_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/fastai/learner.py\u001b[0m in \u001b[0;36mall_batches\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_iter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mone_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/fastai/learner.py\u001b[0m in \u001b[0;36mone_batch\u001b[0;34m(self, i, b)\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_one_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'batch'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCancelBatchException\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_with_events\u001b[0;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[1;32m 153\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnoop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 154\u001b[0;31m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'before_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m;\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 155\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'after_cancel_{event_type}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_do_one_batch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_do_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 164\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'after_pred'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 743\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 744\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 745\u001b[0m for hook in itertools.chain(\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/torch/nn/modules/linear.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 93\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 94\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mlinear\u001b[0;34m(input, weight, bias)\u001b[0m\n\u001b[1;32m 1666\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1667\u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1668\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mbias\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mRuntimeError\u001b[0m: both arguments to matmul need to be at least 1D, but they are 0D and 2D",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/fastai/torch_core.py\u001b[0m in \u001b[0;36mto_concat\u001b[0;34m(xs, dim)\u001b[0m\n\u001b[1;32m 270\u001b[0m \u001b[0;31m# in this case we return a big list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 271\u001b[0;31m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mretain_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 272\u001b[0m except: return sum([L(retain_type(o_.index_select(dim, tensor(i)).squeeze(dim), xs[0])\n",
"\u001b[0;31mTypeError\u001b[0m: expected Tensor as element 0 in argument 0, but got NoneType",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-693df4efeb6c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlearner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/fastai/learner.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, item, rm_type_tfms, with_input)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrm_type_tfms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwith_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mdl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_dl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrm_type_tfms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrm_type_tfms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_workers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0minp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpreds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdec_preds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_preds\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwith_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwith_decoded\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'n_inp'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0minp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0minp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mtuplify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/fastai/learner.py\u001b[0m in \u001b[0;36mget_preds\u001b[0;34m(self, ds_idx, dl, with_input, with_decoded, with_loss, act, inner, reorder, cbs, **kwargs)\u001b[0m\n\u001b[1;32m 235\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mwith_loss\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mctx_mgrs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_not_reduced\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 236\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mContextManagers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx_mgrs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 237\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_epoch_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 238\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mact\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mact\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'activation'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnoop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 239\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall_tensors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/fastai/learner.py\u001b[0m in \u001b[0;36m_do_epoch_validate\u001b[0;34m(self, ds_idx, dl)\u001b[0m\n\u001b[1;32m 185\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdl\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdls\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mds_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdl\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_with_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall_batches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'validate'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCancelValidException\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_do_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/fastai/callback/core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, event_name)\u001b[0m\n\u001b[1;32m 42\u001b[0m (self.run_valid and not getattr(self, 'training', False)))\n\u001b[1;32m 43\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0m_run\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnoop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 45\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mevent_name\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'after_fit'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m \u001b[0;31m#Reset self.run to True at each end of fit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/fastai/callback/core.py\u001b[0m in \u001b[0;36mafter_validate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'preds'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_input\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdetuplify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mto_concat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_preds\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdetuplify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mto_concat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 124\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_targs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdetuplify\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mto_concat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat_dim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_loss\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlosses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_concat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlosses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/fastai/torch_core.py\u001b[0m in \u001b[0;36mto_concat\u001b[0;34m(xs, dim)\u001b[0m\n\u001b[1;32m 270\u001b[0m \u001b[0;31m# in this case we return a big list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 271\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mretain_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m except: return sum([L(retain_type(o_.index_select(dim, tensor(i)).squeeze(dim), xs[0])\n\u001b[0m\u001b[1;32m 273\u001b[0m for i in range_of(o_)) for o_ in xs], L())\n\u001b[1;32m 274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/fastai/torch_core.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 271\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mretain_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 272\u001b[0m except: return sum([L(retain_type(o_.index_select(dim, tensor(i)).squeeze(dim), xs[0])\n\u001b[0;32m--> 273\u001b[0;31m for i in range_of(o_)) for o_ in xs], L())\n\u001b[0m\u001b[1;32m 274\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 275\u001b[0m \u001b[0;31m# Cell\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.8/site-packages/fastcore/basics.py\u001b[0m in \u001b[0;36mrange_of\u001b[0;34m(a, b, step)\u001b[0m\n\u001b[1;32m 463\u001b[0m \u001b[0;34m\"All indices of collection `a`, if `a` is a collection, otherwise `range`\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 464\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_coll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 465\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mb\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 466\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 467\u001b[0m \u001b[0;31m# Cell\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: 'NoneType' object cannot be interpreted as an integer"
]
}
],
"source": [
"x = tensor(10.0).float()\n",
"learner.predict(x)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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