created cuda testing notebook
This commit is contained in:
@@ -658,16 +658,16 @@
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 6,
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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{
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"data": {
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"data": {
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"text/plain": [
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"text/plain": [
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"False"
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"True"
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]
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]
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},
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},
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"execution_count": 11,
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"execution_count": 6,
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"metadata": {},
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"metadata": {},
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"output_type": "execute_result"
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"output_type": "execute_result"
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}
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}
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@@ -678,7 +678,7 @@
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 7,
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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@@ -687,7 +687,7 @@
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"True"
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"True"
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]
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]
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},
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},
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"execution_count": 10,
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"execution_count": 7,
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"metadata": {},
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"metadata": {},
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"output_type": "execute_result"
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"output_type": "execute_result"
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}
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}
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@@ -698,32 +698,15 @@
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 23,
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"execution_count": 8,
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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{
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{
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"name": "stdout",
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"name": "stdout",
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"output_type": "stream",
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"output_type": "stream",
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"text": [
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"text": [
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"Device = cpu; Time per batch: 2.646 seconds\n"
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"Device = cpu; Time per batch: 2.370 seconds\n",
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]
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"Device = cuda; Time per batch: 0.019 seconds\n"
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},
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{
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"ename": "AssertionError",
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"evalue": "Torch not compiled with CUDA enabled",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m<ipython-input-23-3723cf3e57d3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0moptimizer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moptim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mAdam\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.001\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mii\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrainloader\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36mto\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 379\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_floating_point\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_blocking\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 380\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 381\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 382\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 383\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mregister_backward_hook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_apply\u001b[1;34m(self, fn)\u001b[0m\n\u001b[0;32m 185\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 186\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 187\u001b[1;33m \u001b[0mmodule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 188\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 189\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mparam\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parameters\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_apply\u001b[1;34m(self, fn)\u001b[0m\n\u001b[0;32m 185\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 186\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 187\u001b[1;33m \u001b[0mmodule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 188\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 189\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mparam\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parameters\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_apply\u001b[1;34m(self, fn)\u001b[0m\n\u001b[0;32m 191\u001b[0m \u001b[1;31m# Tensors stored in modules are graph leaves, and we don't\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 192\u001b[0m \u001b[1;31m# want to create copy nodes, so we have to unpack the data.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 193\u001b[1;33m \u001b[0mparam\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 194\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mparam\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_grad\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 195\u001b[0m \u001b[0mparam\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_grad\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_grad\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36mconvert\u001b[1;34m(t)\u001b[0m\n\u001b[0;32m 377\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 378\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 379\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_floating_point\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_blocking\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 380\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 381\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\cuda\\__init__.py\u001b[0m in \u001b[0;36m_lazy_init\u001b[1;34m()\u001b[0m\n\u001b[0;32m 159\u001b[0m raise RuntimeError(\n\u001b[0;32m 160\u001b[0m \"Cannot re-initialize CUDA in forked subprocess. \" + msg)\n\u001b[1;32m--> 161\u001b[1;33m \u001b[0m_check_driver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 162\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cuda_init\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 163\u001b[0m \u001b[0m_cudart\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_load_cudart\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\cuda\\__init__.py\u001b[0m in \u001b[0;36m_check_driver\u001b[1;34m()\u001b[0m\n\u001b[0;32m 73\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_check_driver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 74\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'_cuda_isDriverSufficient'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 75\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Torch not compiled with CUDA enabled\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 76\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cuda_isDriverSufficient\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 77\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_cuda_getDriverVersion\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
|
||||||
"\u001b[1;31mAssertionError\u001b[0m: Torch not compiled with CUDA enabled"
|
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@@ -778,23 +761,521 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 5,
|
"execution_count": 9,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
|
||||||
"name": "stderr",
|
|
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"output_type": "stream",
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||||||
"text": [
|
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"/opt/conda/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/models/densenet.py:212: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n"
|
|
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]
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|
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},
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{
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{
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"data": {
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"data": {
|
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"text/plain": [
|
"text/plain": [
|
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"device(type='cuda')"
|
"DenseNet(\n",
|
||||||
|
" (features): Sequential(\n",
|
||||||
|
" (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
|
||||||
|
" (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu0): ReLU(inplace)\n",
|
||||||
|
" (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
|
||||||
|
" (denseblock1): _DenseBlock(\n",
|
||||||
|
" (denselayer1): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
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" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer2): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
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" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
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|
" )\n",
|
||||||
|
" (denselayer3): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
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|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer4): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer5): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer6): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" )\n",
|
||||||
|
" (transition1): _Transition(\n",
|
||||||
|
" (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu): ReLU(inplace)\n",
|
||||||
|
" (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denseblock2): _DenseBlock(\n",
|
||||||
|
" (denselayer1): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer2): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer3): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer4): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer5): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer6): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer7): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer8): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer9): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer10): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer11): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer12): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" )\n",
|
||||||
|
" (transition2): _Transition(\n",
|
||||||
|
" (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu): ReLU(inplace)\n",
|
||||||
|
" (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denseblock3): _DenseBlock(\n",
|
||||||
|
" (denselayer1): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer2): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer3): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer4): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer5): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer6): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer7): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer8): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer9): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer10): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer11): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer12): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer13): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer14): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer15): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer16): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer17): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer18): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer19): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer20): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer21): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer22): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer23): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer24): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" )\n",
|
||||||
|
" (transition3): _Transition(\n",
|
||||||
|
" (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu): ReLU(inplace)\n",
|
||||||
|
" (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denseblock4): _DenseBlock(\n",
|
||||||
|
" (denselayer1): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer2): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer3): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer4): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer5): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer6): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer7): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer8): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer9): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer10): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer11): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer12): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer13): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer14): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer15): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" (denselayer16): _DenseLayer(\n",
|
||||||
|
" (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu1): ReLU(inplace)\n",
|
||||||
|
" (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||||||
|
" (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" (relu2): ReLU(inplace)\n",
|
||||||
|
" (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||||||
|
" )\n",
|
||||||
|
" )\n",
|
||||||
|
" (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
|
||||||
|
" )\n",
|
||||||
|
" (classifier): Sequential(\n",
|
||||||
|
" (0): Linear(in_features=1024, out_features=256, bias=True)\n",
|
||||||
|
" (1): ReLU()\n",
|
||||||
|
" (2): Dropout(p=0.2)\n",
|
||||||
|
" (3): Linear(in_features=256, out_features=2, bias=True)\n",
|
||||||
|
" (4): LogSoftmax()\n",
|
||||||
|
" )\n",
|
||||||
|
")"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"execution_count": 5,
|
"execution_count": 9,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"output_type": "execute_result"
|
"output_type": "execute_result"
|
||||||
}
|
}
|
||||||
@@ -822,13 +1303,12 @@
|
|||||||
"# Only train the classifier parameters, feature parameters are frozen\n",
|
"# Only train the classifier parameters, feature parameters are frozen\n",
|
||||||
"optimizer = optim.Adam(model.classifier.parameters(), lr=0.003)\n",
|
"optimizer = optim.Adam(model.classifier.parameters(), lr=0.003)\n",
|
||||||
"\n",
|
"\n",
|
||||||
"model.to(device)\n",
|
"model.to(device)"
|
||||||
"device"
|
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": null,
|
"execution_count": 10,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
@@ -836,21 +1316,109 @@
|
|||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"Epoch: 1 out of 1\n",
|
"Epoch: 1 out of 1\n",
|
||||||
"Training Loss: 0.208\n",
|
"Training Loss: 0.745\n",
|
||||||
"Test Loss: 0.076\n",
|
"Test Loss: 0.237\n",
|
||||||
"Test Accuracy: 0.971\n",
|
"Test Accuracy: 0.968\n",
|
||||||
"\n",
|
"\n",
|
||||||
"Epoch: 1 out of 1\n",
|
"Epoch: 1 out of 1\n",
|
||||||
"Training Loss: 0.214\n",
|
"Training Loss: 0.335\n",
|
||||||
"Test Loss: 0.068\n",
|
"Test Loss: 0.124\n",
|
||||||
|
"Test Accuracy: 0.969\n",
|
||||||
|
"\n",
|
||||||
|
"Epoch: 1 out of 1\n",
|
||||||
|
"Training Loss: 0.197\n",
|
||||||
|
"Test Loss: 0.073\n",
|
||||||
"Test Accuracy: 0.978\n",
|
"Test Accuracy: 0.978\n",
|
||||||
"\n",
|
"\n",
|
||||||
"Epoch: 1 out of 1\n",
|
"Epoch: 1 out of 1\n",
|
||||||
"Training Loss: 0.216\n",
|
"Training Loss: 0.248\n",
|
||||||
"Test Loss: 0.075\n",
|
"Test Loss: 0.066\n",
|
||||||
"Test Accuracy: 0.971\n",
|
"Test Accuracy: 0.978\n",
|
||||||
|
"\n",
|
||||||
|
"Epoch: 1 out of 1\n",
|
||||||
|
"Training Loss: 0.203\n",
|
||||||
|
"Test Loss: 0.067\n",
|
||||||
|
"Test Accuracy: 0.976\n",
|
||||||
|
"\n",
|
||||||
|
"Epoch: 1 out of 1\n",
|
||||||
|
"Training Loss: 0.223\n",
|
||||||
|
"Test Loss: 0.053\n",
|
||||||
|
"Test Accuracy: 0.980\n",
|
||||||
|
"\n",
|
||||||
|
"Epoch: 1 out of 1\n",
|
||||||
|
"Training Loss: 0.174\n",
|
||||||
|
"Test Loss: 0.051\n",
|
||||||
|
"Test Accuracy: 0.982\n",
|
||||||
|
"\n",
|
||||||
|
"Epoch: 1 out of 1\n",
|
||||||
|
"Training Loss: 0.111\n",
|
||||||
|
"Test Loss: 0.051\n",
|
||||||
|
"Test Accuracy: 0.980\n",
|
||||||
|
"\n",
|
||||||
|
"Epoch: 1 out of 1\n",
|
||||||
|
"Training Loss: 0.158\n",
|
||||||
|
"Test Loss: 0.046\n",
|
||||||
|
"Test Accuracy: 0.982\n",
|
||||||
|
"\n",
|
||||||
|
"Epoch: 1 out of 1\n",
|
||||||
|
"Training Loss: 0.136\n",
|
||||||
|
"Test Loss: 0.046\n",
|
||||||
|
"Test Accuracy: 0.985\n",
|
||||||
|
"\n",
|
||||||
|
"Epoch: 1 out of 1\n",
|
||||||
|
"Training Loss: 0.174\n",
|
||||||
|
"Test Loss: 0.057\n",
|
||||||
|
"Test Accuracy: 0.977\n",
|
||||||
|
"\n",
|
||||||
|
"Epoch: 1 out of 1\n",
|
||||||
|
"Training Loss: 0.170\n",
|
||||||
|
"Test Loss: 0.050\n",
|
||||||
|
"Test Accuracy: 0.984\n",
|
||||||
|
"\n",
|
||||||
|
"Epoch: 1 out of 1\n",
|
||||||
|
"Training Loss: 0.179\n",
|
||||||
|
"Test Loss: 0.046\n",
|
||||||
|
"Test Accuracy: 0.984\n",
|
||||||
|
"\n",
|
||||||
|
"Epoch: 1 out of 1\n",
|
||||||
|
"Training Loss: 0.207\n",
|
||||||
|
"Test Loss: 0.051\n",
|
||||||
|
"Test Accuracy: 0.982\n",
|
||||||
|
"\n",
|
||||||
|
"Epoch: 1 out of 1\n",
|
||||||
|
"Training Loss: 0.204\n",
|
||||||
|
"Test Loss: 0.111\n",
|
||||||
|
"Test Accuracy: 0.961\n",
|
||||||
|
"\n",
|
||||||
|
"Epoch: 1 out of 1\n",
|
||||||
|
"Training Loss: 0.233\n",
|
||||||
|
"Test Loss: 0.164\n",
|
||||||
|
"Test Accuracy: 0.935\n",
|
||||||
|
"\n",
|
||||||
|
"Epoch: 1 out of 1\n",
|
||||||
|
"Training Loss: 0.367\n",
|
||||||
|
"Test Loss: 0.191\n",
|
||||||
|
"Test Accuracy: 0.919\n",
|
||||||
"\n"
|
"\n"
|
||||||
]
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"ename": "KeyboardInterrupt",
|
||||||
|
"evalue": "",
|
||||||
|
"output_type": "error",
|
||||||
|
"traceback": [
|
||||||
|
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||||
|
"\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
|
||||||
|
"\u001b[1;32m<ipython-input-10-232d4a613c21>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 35\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 36\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 37\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtestloader\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 38\u001b[0m \u001b[1;31m# Move input and label tensors to the device\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 39\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 558\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_workers\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# same-process loading\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 559\u001b[0m \u001b[0mindices\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample_iter\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# may raise StopIteration\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 560\u001b[1;33m \u001b[0mbatch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcollate_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mindices\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 561\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 562\u001b[0m \u001b[0mbatch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpin_memory_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torch\\utils\\data\\dataloader.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 558\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnum_workers\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# same-process loading\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 559\u001b[0m \u001b[0mindices\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample_iter\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# may raise StopIteration\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 560\u001b[1;33m \u001b[0mbatch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcollate_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mindices\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 561\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 562\u001b[0m \u001b[0mbatch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpin_memory_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torchvision\\datasets\\folder.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, index)\u001b[0m\n\u001b[0;32m 136\u001b[0m \"\"\"\n\u001b[0;32m 137\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msamples\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m \u001b[0msample\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 139\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 140\u001b[0m \u001b[0msample\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msample\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torchvision\\datasets\\folder.py\u001b[0m in \u001b[0;36mdefault_loader\u001b[1;34m(path)\u001b[0m\n\u001b[0;32m 172\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0maccimage_loader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 173\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 174\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mpil_loader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 175\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 176\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\torchvision\\datasets\\folder.py\u001b[0m in \u001b[0;36mpil_loader\u001b[1;34m(path)\u001b[0m\n\u001b[0;32m 155\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'rb'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 156\u001b[0m \u001b[0mimg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 157\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'RGB'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 158\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 159\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\PIL\\Image.py\u001b[0m in \u001b[0;36mconvert\u001b[1;34m(self, mode, matrix, dither, palette, colors)\u001b[0m\n\u001b[0;32m 913\u001b[0m \"\"\"\n\u001b[0;32m 914\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 915\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 916\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 917\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mmode\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"P\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\PIL\\ImageFile.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 239\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 240\u001b[0m \u001b[0mb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mb\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 241\u001b[1;33m \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merr_code\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdecoder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 242\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||||
|
"\u001b[1;31mKeyboardInterrupt\u001b[0m: "
|
||||||
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"source": [
|
"source": [
|
||||||
@@ -925,6 +1493,15 @@
|
|||||||
" model.train()"
|
" model.train()"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"device"
|
||||||
|
]
|
||||||
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": null,
|
"execution_count": null,
|
||||||
|
|||||||
@@ -1,332 +0,0 @@
|
|||||||
{
|
|
||||||
"cells": [
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 2,
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"torch.Size([64, 10])\n",
|
|
||||||
"tensor([[9],\n",
|
|
||||||
" [9],\n",
|
|
||||||
" [9],\n",
|
|
||||||
" [9],\n",
|
|
||||||
" [9],\n",
|
|
||||||
" [5],\n",
|
|
||||||
" [9],\n",
|
|
||||||
" [5],\n",
|
|
||||||
" [2],\n",
|
|
||||||
" [9]])\n",
|
|
||||||
"Accuracy: 1.5625%\n",
|
|
||||||
"Epoch: 1 out of 30\n",
|
|
||||||
"Training Loss: 0.510\n",
|
|
||||||
"Test Loss: 0.454\n",
|
|
||||||
"Test Accuracy: 0.836\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 2 out of 30\n",
|
|
||||||
"Training Loss: 0.389\n",
|
|
||||||
"Test Loss: 0.412\n",
|
|
||||||
"Test Accuracy: 0.852\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 3 out of 30\n",
|
|
||||||
"Training Loss: 0.353\n",
|
|
||||||
"Test Loss: 0.388\n",
|
|
||||||
"Test Accuracy: 0.861\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 4 out of 30\n",
|
|
||||||
"Training Loss: 0.330\n",
|
|
||||||
"Test Loss: 0.428\n",
|
|
||||||
"Test Accuracy: 0.847\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 5 out of 30\n",
|
|
||||||
"Training Loss: 0.315\n",
|
|
||||||
"Test Loss: 0.381\n",
|
|
||||||
"Test Accuracy: 0.865\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 6 out of 30\n",
|
|
||||||
"Training Loss: 0.303\n",
|
|
||||||
"Test Loss: 0.388\n",
|
|
||||||
"Test Accuracy: 0.864\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 7 out of 30\n",
|
|
||||||
"Training Loss: 0.292\n",
|
|
||||||
"Test Loss: 0.364\n",
|
|
||||||
"Test Accuracy: 0.872\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 8 out of 30\n",
|
|
||||||
"Training Loss: 0.281\n",
|
|
||||||
"Test Loss: 0.370\n",
|
|
||||||
"Test Accuracy: 0.869\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 9 out of 30\n",
|
|
||||||
"Training Loss: 0.270\n",
|
|
||||||
"Test Loss: 0.365\n",
|
|
||||||
"Test Accuracy: 0.877\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 10 out of 30\n",
|
|
||||||
"Training Loss: 0.267\n",
|
|
||||||
"Test Loss: 0.366\n",
|
|
||||||
"Test Accuracy: 0.877\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 11 out of 30\n",
|
|
||||||
"Training Loss: 0.260\n",
|
|
||||||
"Test Loss: 0.369\n",
|
|
||||||
"Test Accuracy: 0.873\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 12 out of 30\n",
|
|
||||||
"Training Loss: 0.254\n",
|
|
||||||
"Test Loss: 0.377\n",
|
|
||||||
"Test Accuracy: 0.876\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 13 out of 30\n",
|
|
||||||
"Training Loss: 0.244\n",
|
|
||||||
"Test Loss: 0.369\n",
|
|
||||||
"Test Accuracy: 0.879\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 14 out of 30\n",
|
|
||||||
"Training Loss: 0.243\n",
|
|
||||||
"Test Loss: 0.371\n",
|
|
||||||
"Test Accuracy: 0.879\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 15 out of 30\n",
|
|
||||||
"Training Loss: 0.237\n",
|
|
||||||
"Test Loss: 0.377\n",
|
|
||||||
"Test Accuracy: 0.883\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 16 out of 30\n",
|
|
||||||
"Training Loss: 0.230\n",
|
|
||||||
"Test Loss: 0.407\n",
|
|
||||||
"Test Accuracy: 0.874\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 17 out of 30\n",
|
|
||||||
"Training Loss: 0.228\n",
|
|
||||||
"Test Loss: 0.370\n",
|
|
||||||
"Test Accuracy: 0.879\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 18 out of 30\n",
|
|
||||||
"Training Loss: 0.221\n",
|
|
||||||
"Test Loss: 0.376\n",
|
|
||||||
"Test Accuracy: 0.878\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 19 out of 30\n",
|
|
||||||
"Training Loss: 0.222\n",
|
|
||||||
"Test Loss: 0.376\n",
|
|
||||||
"Test Accuracy: 0.881\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 20 out of 30\n",
|
|
||||||
"Training Loss: 0.217\n",
|
|
||||||
"Test Loss: 0.387\n",
|
|
||||||
"Test Accuracy: 0.880\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 21 out of 30\n",
|
|
||||||
"Training Loss: 0.209\n",
|
|
||||||
"Test Loss: 0.401\n",
|
|
||||||
"Test Accuracy: 0.877\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 22 out of 30\n",
|
|
||||||
"Training Loss: 0.210\n",
|
|
||||||
"Test Loss: 0.392\n",
|
|
||||||
"Test Accuracy: 0.883\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 23 out of 30\n",
|
|
||||||
"Training Loss: 0.204\n",
|
|
||||||
"Test Loss: 0.411\n",
|
|
||||||
"Test Accuracy: 0.878\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 24 out of 30\n",
|
|
||||||
"Training Loss: 0.202\n",
|
|
||||||
"Test Loss: 0.391\n",
|
|
||||||
"Test Accuracy: 0.882\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 25 out of 30\n",
|
|
||||||
"Training Loss: 0.195\n",
|
|
||||||
"Test Loss: 0.392\n",
|
|
||||||
"Test Accuracy: 0.883\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 26 out of 30\n",
|
|
||||||
"Training Loss: 0.195\n",
|
|
||||||
"Test Loss: 0.471\n",
|
|
||||||
"Test Accuracy: 0.878\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 27 out of 30\n",
|
|
||||||
"Training Loss: 0.191\n",
|
|
||||||
"Test Loss: 0.431\n",
|
|
||||||
"Test Accuracy: 0.881\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 28 out of 30\n",
|
|
||||||
"Training Loss: 0.195\n",
|
|
||||||
"Test Loss: 0.418\n",
|
|
||||||
"Test Accuracy: 0.882\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 29 out of 30\n",
|
|
||||||
"Training Loss: 0.192\n",
|
|
||||||
"Test Loss: 0.390\n",
|
|
||||||
"Test Accuracy: 0.887\n",
|
|
||||||
"\n",
|
|
||||||
"Epoch: 30 out of 30\n",
|
|
||||||
"Training Loss: 0.185\n",
|
|
||||||
"Test Loss: 0.428\n",
|
|
||||||
"Test Accuracy: 0.875\n",
|
|
||||||
"\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"import torch\n",
|
|
||||||
"from torchvision import datasets, transforms\n",
|
|
||||||
"from torch import nn, optim\n",
|
|
||||||
"import torch.nn.functional as F\n",
|
|
||||||
"\n",
|
|
||||||
"# Define a transform to normalize the data\n",
|
|
||||||
"transform = transforms.Compose([transforms.ToTensor(),\n",
|
|
||||||
" transforms.Normalize((0.5, 0.5, 0.5),\n",
|
|
||||||
" (0.5, 0.5, 0.5))])\n",
|
|
||||||
"\n",
|
|
||||||
"# Download and load the training data\n",
|
|
||||||
"trainset = datasets.FashionMNIST(\n",
|
|
||||||
" '.pytorch/F_MNIST_data/', download=True, train=True, transform=transform)\n",
|
|
||||||
"\n",
|
|
||||||
"trainloader = torch.utils.data.DataLoader(\n",
|
|
||||||
" trainset, batch_size=64, shuffle=True)\n",
|
|
||||||
"\n",
|
|
||||||
"# Download and load the test data\n",
|
|
||||||
"testset = datasets.FashionMNIST(\n",
|
|
||||||
" '.pytorch/F_MNIST_data/', download=True, train=False,\n",
|
|
||||||
" transform=transform)\n",
|
|
||||||
"\n",
|
|
||||||
"testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=True)\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"class Classifier(nn.Module):\n",
|
|
||||||
" def __init__(self):\n",
|
|
||||||
" super().__init__()\n",
|
|
||||||
" self.fc1 = nn.Linear(784, 256)\n",
|
|
||||||
" self.fc2 = nn.Linear(256, 128)\n",
|
|
||||||
" self.fc3 = nn.Linear(128, 64)\n",
|
|
||||||
" self.fc4 = nn.Linear(64, 10)\n",
|
|
||||||
"\n",
|
|
||||||
" def forward(self, x):\n",
|
|
||||||
" # make sure input tensor is flattened\n",
|
|
||||||
" x = x.view(x.shape[0], -1)\n",
|
|
||||||
"\n",
|
|
||||||
" x = F.relu(self.fc1(x))\n",
|
|
||||||
" x = F.relu(self.fc2(x))\n",
|
|
||||||
" x = F.relu(self.fc3(x))\n",
|
|
||||||
" x = F.log_softmax(self.fc4(x), dim=1)\n",
|
|
||||||
"\n",
|
|
||||||
" return x\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"model = Classifier()\n",
|
|
||||||
"\n",
|
|
||||||
"images, labels = next(iter(testloader))\n",
|
|
||||||
"\n",
|
|
||||||
"# Get the class probabilities\n",
|
|
||||||
"ps = torch.exp(model(images))\n",
|
|
||||||
"\n",
|
|
||||||
"# Make sure the shape is appropriate, we should get 10 class probabilities for\n",
|
|
||||||
"# 64 examples\n",
|
|
||||||
"print(ps.shape)\n",
|
|
||||||
"\n",
|
|
||||||
"top_p, top_class = ps.topk(1, dim=1)\n",
|
|
||||||
"# Look at the most likely classes for the first 10 examples\n",
|
|
||||||
"print(top_class[:10, :])\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"equals = top_class == labels.view(*top_class.shape)\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"accuracy = torch.mean(equals.type(torch.FloatTensor))\n",
|
|
||||||
"print(f'Accuracy: {accuracy.item()*100}%')\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"# Model begins\n",
|
|
||||||
"\n",
|
|
||||||
"model = Classifier()\n",
|
|
||||||
"criterion = nn.NLLLoss()\n",
|
|
||||||
"optimizer = optim.Adam(model.parameters(), lr=0.003)\n",
|
|
||||||
"\n",
|
|
||||||
"epochs = 30\n",
|
|
||||||
"steps = 0\n",
|
|
||||||
"\n",
|
|
||||||
"trainLosses, testLosses = [], []\n",
|
|
||||||
"for e in range(epochs):\n",
|
|
||||||
" runningLoss = 0\n",
|
|
||||||
" for images, labels in trainloader:\n",
|
|
||||||
"\n",
|
|
||||||
" optimizer.zero_grad()\n",
|
|
||||||
"\n",
|
|
||||||
" log_ps = model(images)\n",
|
|
||||||
" loss = criterion(log_ps, labels)\n",
|
|
||||||
" loss.backward()\n",
|
|
||||||
" optimizer.step()\n",
|
|
||||||
"\n",
|
|
||||||
" runningLoss += loss.item()\n",
|
|
||||||
"\n",
|
|
||||||
" else:\n",
|
|
||||||
" testLoss = 0\n",
|
|
||||||
" accuracy = 0\n",
|
|
||||||
"\n",
|
|
||||||
" # Turn off gradients for validation step\n",
|
|
||||||
" with torch.no_grad():\n",
|
|
||||||
" for images, labels in testloader:\n",
|
|
||||||
" # Get the output\n",
|
|
||||||
" log_ps = model(images)\n",
|
|
||||||
" # Get the loss\n",
|
|
||||||
" testLoss += criterion(log_ps, labels)\n",
|
|
||||||
"\n",
|
|
||||||
" # Get the probabilities\n",
|
|
||||||
" ps = torch.exp(log_ps)\n",
|
|
||||||
" # Get the most likely class for each prediction\n",
|
|
||||||
" top_p, top_class = ps.topk(1, dim=1)\n",
|
|
||||||
" # Check if the predictions match the actual label\n",
|
|
||||||
" equals = top_class == labels.view(*top_class.shape)\n",
|
|
||||||
" # Update accuracy\n",
|
|
||||||
" accuracy += torch.mean(equals.type(torch.FloatTensor))\n",
|
|
||||||
"\n",
|
|
||||||
" # Update train loss\n",
|
|
||||||
" trainLosses.append(runningLoss / len(trainloader))\n",
|
|
||||||
" # Update test loss\n",
|
|
||||||
" testLosses.append(testLoss / len(testloader))\n",
|
|
||||||
"\n",
|
|
||||||
" # Print output\n",
|
|
||||||
" print(f'Epoch: {e+1} out of {epochs}')\n",
|
|
||||||
" print(f'Training Loss: {runningLoss/len(trainloader):.3f}')\n",
|
|
||||||
" print(f'Test Loss: {testLoss/len(testloader):.3f}')\n",
|
|
||||||
" print(f'Test Accuracy: {accuracy/len(testloader):.3f}')\n",
|
|
||||||
" print()\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": []
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"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": "3.7.3"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"nbformat": 4,
|
|
||||||
"nbformat_minor": 2
|
|
||||||
}
|
|
||||||
Reference in New Issue
Block a user