adding dft slides
This commit is contained in:
Morten Hjorth-Jensen
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"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/wrap_util.py:20\u001b[0m, in \u001b[0;36munary_to_nary.<locals>.nary_operator.<locals>.nary_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 19\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(args[i] \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m argnum)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43munary_operator\u001b[49m\u001b[43m(\u001b[49m\u001b[43munary_f\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnary_op_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/differential_operators.py:29\u001b[0m, in \u001b[0;36mgrad\u001b[0;34m(fun, x)\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m vspace(ans)\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGrad only applies to real scalar-output functions. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTry jacobian, elementwise_grad or holomorphic_grad.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 29\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mvjp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvspace\u001b[49m\u001b[43m(\u001b[49m\u001b[43mans\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mones\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:14\u001b[0m, in \u001b[0;36mmake_vjp.<locals>.vjp\u001b[0;34m(g)\u001b[0m\n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvjp\u001b[39m(g): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mbackward_pass\u001b[49m\u001b[43m(\u001b[49m\u001b[43mg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_node\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:23\u001b[0m, in \u001b[0;36mbackward_pass\u001b[0;34m(g, end_node)\u001b[0m\n\u001b[1;32m 21\u001b[0m ingrads \u001b[38;5;241m=\u001b[39m node\u001b[38;5;241m.\u001b[39mvjp(outgrad[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m parent, ingrad \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(node\u001b[38;5;241m.\u001b[39mparents, ingrads):\n\u001b[0;32m---> 23\u001b[0m outgrads[parent] \u001b[38;5;241m=\u001b[39m \u001b[43madd_outgrads\u001b[49m\u001b[43m(\u001b[49m\u001b[43moutgrads\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparent\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mingrad\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m outgrad[\u001b[38;5;241m0\u001b[39m]\n",
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"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:165\u001b[0m, in \u001b[0;36madd_outgrads\u001b[0;34m(prev_g_flagged, g)\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mutable:\n\u001b[1;32m 164\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sparse:\n\u001b[0;32m--> 165\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msparse_add\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprev_g\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mg\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 166\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 167\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m vs\u001b[38;5;241m.\u001b[39mmut_add(prev_g, g), \u001b[38;5;28;01mTrue\u001b[39;00m\n",
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"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/tracer.py:48\u001b[0m, in \u001b[0;36mprimitive.<locals>.f_wrapped\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new_box(ans, trace, node)\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf_raw\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:186\u001b[0m, in \u001b[0;36msparse_add\u001b[0;34m(vs, x_prev, x_new)\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[38;5;129m@primitive\u001b[39m\n\u001b[1;32m 184\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msparse_add\u001b[39m(vs, x_prev, x_new):\n\u001b[1;32m 185\u001b[0m x_prev \u001b[38;5;241m=\u001b[39m x_prev \u001b[38;5;28;01mif\u001b[39;00m x_prev \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m vs\u001b[38;5;241m.\u001b[39mzeros()\n\u001b[0;32m--> 186\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mx_new\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmut_add\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_prev\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/miniforge3/envs/myenv/lib/python3.9/site-packages/autograd/core.py:23\u001b[0m, in \u001b[0;36mbackward_pass\u001b[0;34m(g, end_node)\u001b[0m\n\u001b[1;32m 21\u001b[0m ingrads \u001b[38;5;241m=\u001b[39m node\u001b[38;5;241m.\u001b[39mvjp(outgrad[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m parent, ingrad \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(node\u001b[38;5;241m.\u001b[39mparents, ingrads):\n\u001b[0;32m---> 23\u001b[0m outgrads[parent] \u001b[38;5;241m=\u001b[39m add_outgrads(\u001b[43moutgrads\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparent\u001b[49m\u001b[43m)\u001b[49m, ingrad)\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m outgrad[\u001b[38;5;241m0\u001b[39m]\n",
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"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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"text": [
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"/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
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" super(SGD, self).__init__(name, **kwargs)\n",
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"2023-10-15 21:48:58.909327: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
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"2023-10-25 15:31:33.965944: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
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"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31718/1326197715.py:6: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11068/1326197715.py:6: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
|
||||
" data_pandas=data_pandas.append(pd.DataFrame(new_hobbit, index=['Pippin']))\n"
|
||||
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},
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"0.9955273625597437\n"
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||||
]
|
||||
}
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],
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@@ -1550,7 +1550,7 @@
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||||
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||||
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||||
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|
||||
"0.008900933315885705\n"
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||||
]
|
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}
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],
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@@ -1585,23 +1585,23 @@
|
||||
"name": "stdout",
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||||
"text": [
|
||||
"[0.00552246 0.00115669 0.02017377 0.01896127 0.01389847 0.01591021\n",
|
||||
" 0.02816083 0.0100949 0.02757522 0.00182747 0.00584432 0.02081274\n",
|
||||
" 0.01365363 0.0191717 0.01068907 0.02763182 0.02950229 0.02485679\n",
|
||||
" 0.01456159 0.00644939 0.0297291 0.08272096 0.01335857 0.00825399\n",
|
||||
" 0.04478101 0.05834444 0.04198166 0.034985 0.00562524 0.01524072\n",
|
||||
" 0.01529708 0.02083512 0.01161357 0.01708691 0.02279888 0.02912421\n",
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||||
" 0.00076617 0.02329285 0.00626773 0.01054509 0.00460405 0.01476097\n",
|
||||
" 0.0036718 0.00569405 0.07804489 0.03894873 0.02103178 0.00726135\n",
|
||||
" 0.00353575 0.00857028 0.00923278 0.01616709 0.02881357 0.00550379\n",
|
||||
" 0.02942218 0.00946636 0.03982972 0.0149713 0.04103307 0.05526765\n",
|
||||
" 0.00463639 0.00254359 0.00915433 0.02588522 0.00090992 0.00739382\n",
|
||||
" 0.02075115 0.024632 0.00115506 0.01963203 0.00086063 0.01580414\n",
|
||||
" 0.01059601 0.03376827 0.02745507 0.02109939 0.05977068 0.04662395\n",
|
||||
" 0.00283853 0.03903968 0.0001225 0.02385515 0.02089297 0.04214702\n",
|
||||
" 0.01289962 0.00798188 0.04746791 0.04822955 0.02066371 0.01045774\n",
|
||||
" 0.01164198 0.03633213 0.00183398 0.0105301 0.00880924 0.015244\n",
|
||||
" 0.01596986 0.01176096 0.01448147 0.00610607]\n"
|
||||
"[0.00643899 0.04246989 0.0607062 0.02997344 0.0011878 0.00123457\n",
|
||||
" 0.00324986 0.03285652 0.01028728 0.01571866 0.03940381 0.0814985\n",
|
||||
" 0.038844 0.01828593 0.03967758 0.00303693 0.02227466 0.02702328\n",
|
||||
" 0.00026861 0.00883798 0.02876697 0.00472251 0.03141454 0.02911162\n",
|
||||
" 0.02387339 0.00585113 0.00110716 0.00587564 0.00693821 0.00604105\n",
|
||||
" 0.00804985 0.02058094 0.01151984 0.01782721 0.0286851 0.08874631\n",
|
||||
" 0.01678538 0.0065912 0.03611471 0.02706508 0.00313125 0.04977093\n",
|
||||
" 0.00415289 0.02760079 0.00518122 0.00628874 0.00739489 0.01619456\n",
|
||||
" 0.00996972 0.02210753 0.02030107 0.02100763 0.04699527 0.01512934\n",
|
||||
" 0.00717079 0.01784714 0.01095703 0.01281486 0.0231703 0.04482932\n",
|
||||
" 0.00287871 0.0565419 0.04028659 0.03102525 0.01617722 0.0271761\n",
|
||||
" 0.01736502 0.03394827 0.00328494 0.05750876 0.0059888 0.01915888\n",
|
||||
" 0.01423609 0.01024227 0.03660869 0.01012951 0.00534938 0.03375068\n",
|
||||
" 0.02699539 0.04083439 0.04965227 0.00565625 0.02250553 0.00893027\n",
|
||||
" 0.02244755 0.00741987 0.00189101 0.02042476 0.02036545 0.06362348\n",
|
||||
" 0.03145163 0.02987833 0.07393685 0.0033575 0.02791218 0.00214832\n",
|
||||
" 0.0111154 0.01344581 0.00368581 0.01436601]\n"
|
||||
]
|
||||
}
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],
|
||||
@@ -1655,15 +1655,15 @@
|
||||
"name": "stdout",
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||||
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||||
"text": [
|
||||
"[ 2.04899609 -0.34193915 5.64527549 -0.69997503 0.31290684]\n",
|
||||
"[ 2.09851217 -1.48209629 10.27096183 -6.79998516 2.87206824]\n",
|
||||
"Training R2\n",
|
||||
"0.9952222065466447\n",
|
||||
"0.9957273060382023\n",
|
||||
"Training MSE\n",
|
||||
"0.008897354602673473\n",
|
||||
"0.010053880703541525\n",
|
||||
"Test R2\n",
|
||||
"0.9915165982451293\n",
|
||||
"0.9888005551376943\n",
|
||||
"Test MSE\n",
|
||||
"0.009442796383765939\n"
|
||||
"0.008043926731954223\n"
|
||||
]
|
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|
||||
" param_distributions={'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x156346610>})\n",
|
||||
" param_distributions={'alpha': <scipy.stats._distn_infrastructure.rv_frozen object at 0x1045a7eb0>})\n",
|
||||
"Best estimated lambda-value: 0.9849967686928113\n",
|
||||
"MSE score: 1.0853136633465326\n",
|
||||
"R2 score: -0.0002382102844775691\n"
|
||||
|
||||
@@ -1160,14 +1160,14 @@
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"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31749/3838917029.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11106/3838917029.py:18: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\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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||||
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|
||||
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|
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|
||||
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|
||||
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|
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||||
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|
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|
||||
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|
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|
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||||
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|
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|
||||
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|
||||
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|
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|
||||
" exp_term = np.exp(self.z_o)\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
|
||||
" self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
|
||||
]
|
||||
},
|
||||
@@ -4192,7 +4192,7 @@
|
||||
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||||
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||||
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|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
|
||||
" return 1/(1 + np.exp(-x))\n"
|
||||
]
|
||||
},
|
||||
@@ -4210,11 +4210,11 @@
|
||||
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||||
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||||
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|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
|
||||
" return 1/(1 + np.exp(-x))\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
|
||||
" exp_term = np.exp(self.z_o)\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
|
||||
" self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
|
||||
]
|
||||
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|
||||
@@ -4232,11 +4232,11 @@
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||||
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||||
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|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
|
||||
" return 1/(1 + np.exp(-x))\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
|
||||
" exp_term = np.exp(self.z_o)\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
|
||||
" self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
|
||||
]
|
||||
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|
||||
@@ -4254,11 +4254,11 @@
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||||
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|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
|
||||
" return 1/(1 + np.exp(-x))\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
|
||||
" exp_term = np.exp(self.z_o)\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
|
||||
" self.probabilities = exp_term / np.sum(exp_term, axis=1, keepdims=True)\n"
|
||||
]
|
||||
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|
||||
@@ -4276,11 +4276,11 @@
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|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
|
||||
" return 1/(1 + np.exp(-x))\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
|
||||
" exp_term = np.exp(self.z_o)\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
|
||||
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|
||||
]
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||||
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@@ -4298,11 +4298,11 @@
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|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
|
||||
" return 1/(1 + np.exp(-x))\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
|
||||
" exp_term = np.exp(self.z_o)\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
|
||||
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|
||||
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|
||||
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||||
@@ -4320,11 +4320,11 @@
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||||
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|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
|
||||
" return 1/(1 + np.exp(-x))\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
|
||||
" exp_term = np.exp(self.z_o)\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
|
||||
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|
||||
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|
||||
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|
||||
@@ -4342,11 +4342,11 @@
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||||
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||||
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||||
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||||
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|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
|
||||
" return 1/(1 + np.exp(-x))\n",
|
||||
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|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:43: RuntimeWarning: overflow encountered in exp\n",
|
||||
" exp_term = np.exp(self.z_o)\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
|
||||
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|
||||
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||||
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||||
@@ -4364,11 +4364,11 @@
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||||
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|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/1630775253.py:44: RuntimeWarning: invalid value encountered in true_divide\n",
|
||||
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|
||||
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|
||||
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|
||||
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@@ -4429,15 +4429,15 @@
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||||
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|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
|
||||
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|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
|
||||
" return 1/(1 + np.exp(-x))\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
|
||||
" return 1/(1 + np.exp(-x))\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
|
||||
" return 1/(1 + np.exp(-x))\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_31761/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
|
||||
"/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11118/953065564.py:4: RuntimeWarning: overflow encountered in exp\n",
|
||||
" return 1/(1 + np.exp(-x))\n"
|
||||
]
|
||||
},
|
||||
@@ -4980,7 +4980,13 @@
|
||||
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|
||||
"Lambda = 1.0\n",
|
||||
"Accuracy score on test set: 0.9722222222222222\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
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|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Learning rate = 0.01\n",
|
||||
"Lambda = 10.0\n",
|
||||
"Accuracy score on test set: 0.9527777777777777\n",
|
||||
@@ -5072,7 +5078,13 @@
|
||||
"Learning rate = 1.0\n",
|
||||
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|
||||
"Accuracy score on test set: 0.08888888888888889\n",
|
||||
"\n",
|
||||
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|
||||
]
|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
"text": [
|
||||
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|
||||
"Lambda = 10.0\n",
|
||||
"Accuracy score on test set: 0.09444444444444444\n",
|
||||
@@ -5098,7 +5110,13 @@
|
||||
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|
||||
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|
||||
"Accuracy score on test set: 0.1388888888888889\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
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|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Learning rate = 10.0\n",
|
||||
"Lambda = 0.1\n",
|
||||
"Accuracy score on test set: 0.11388888888888889\n",
|
||||
@@ -5627,7 +5645,13 @@
|
||||
"Learning rate = 1.0\n",
|
||||
"Lambda = 10.0\n",
|
||||
"Accuracy score on data set: 0.5\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Learning rate = 10.0\n",
|
||||
"Lambda = 1e-05\n",
|
||||
"Accuracy score on data set: 0.5\n",
|
||||
@@ -5693,7 +5717,7 @@
|
||||
},
|
||||
"metadata": {
|
||||
"filenames": {
|
||||
"image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week41_211_2.png"
|
||||
"image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week41_211_3.png"
|
||||
}
|
||||
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|
||||
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||||
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||||
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@@ -604,7 +604,13 @@
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||||
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|
||||
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|
||||
"Accuracy score on data set: 0.5\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
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|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Learning rate = 0.0001\n",
|
||||
"Lambda = 10.0\n",
|
||||
"Accuracy score on data set: 0.5\n",
|
||||
@@ -786,7 +792,7 @@
|
||||
},
|
||||
"metadata": {
|
||||
"filenames": {
|
||||
"image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week43_30_2.png"
|
||||
"image/png": "/Users/mhjensen/Teaching/MachineLearning/doc/LectureNotes/_build/jupyter_execute/week43_30_3.png"
|
||||
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|
||||
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|
||||
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|
||||
@@ -10894,20 +10900,7 @@
|
||||
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|
||||
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|
||||
"text": [
|
||||
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|
||||
]
|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
"text": [
|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
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|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Adam: Eta=0.001, Lambda=0\n",
|
||||
"\r",
|
||||
" [----------------------------------------] 0.000% | train_error: 12.9 | train_acc: 0.376 | val_error: 12.6 | val_acc: 0.392 "
|
||||
]
|
||||
@@ -43593,7 +43586,14 @@
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"predictions = (n_inputs) = (1437,)\n",
|
||||
"predictions = (n_inputs) = (1437,)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"prediction for image 0: 8\n",
|
||||
"correct label for image 0: 6\n"
|
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]
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Reference in New Issue
Block a user