Adding some final things.
This commit is contained in:
Alex Huddleston
parent
e09143ccbc
commit
ec429ffb91
2 changed files with 12827 additions and 32 deletions
12576
nn_puzzle_solver.html
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12576
nn_puzzle_solver.html
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@ -9,9 +9,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using TensorFlow backend.\n"
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]
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}
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],
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"source": [
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"from functools import reduce\n",
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"import numpy as np\n",
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@ -29,8 +37,10 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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"outputs": [],
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"source": [
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"# Used to format our input binary state.\n",
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@ -127,8 +137,10 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"execution_count": 3,
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"metadata": {
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"collapsed": true
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"outputs": [],
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"source": [
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"target = []\n",
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 4,
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"metadata": {
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"collapsed": true
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},
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@ -213,9 +225,185 @@
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0\n",
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"Epoch 1/8\n",
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"1/1 [==============================] - 0s 474ms/step - loss: 0.2470 - acc: 0.0000e+00\n",
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"Epoch 2/8\n",
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"1/1 [==============================] - 0s 2ms/step - loss: 0.2458 - acc: 0.0000e+00\n",
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"Epoch 3/8\n",
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"1/1 [==============================] - 0s 2ms/step - loss: 0.2446 - acc: 0.0000e+00\n",
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"Epoch 4/8\n",
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"1/1 [==============================] - 0s 1ms/step - loss: 0.2435 - acc: 0.0000e+00\n",
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"Epoch 5/8\n",
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"1/1 [==============================] - 0s 2ms/step - loss: 0.2423 - acc: 0.0000e+00\n",
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"Epoch 6/8\n",
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"1/1 [==============================] - 0s 2ms/step - loss: 0.2411 - acc: 0.0000e+00\n",
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"Epoch 7/8\n",
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"1/1 [==============================] - 0s 2ms/step - loss: 0.2399 - acc: 0.0000e+00\n",
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"Epoch 8/8\n",
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"1/1 [==============================] - 0s 2ms/step - loss: 0.2388 - acc: 0.0000e+00\n",
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"1\n",
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"Epoch 1/8\n",
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"2/2 [==============================] - 0s 827us/step - loss: 0.2530 - acc: 0.0000e+00\n",
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"Epoch 2/8\n",
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"2/2 [==============================] - 0s 1ms/step - loss: 0.2519 - acc: 0.0000e+00\n",
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"Epoch 3/8\n",
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"2/2 [==============================] - 0s 3ms/step - loss: 0.2508 - acc: 0.0000e+00\n",
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"Epoch 4/8\n",
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"2/2 [==============================] - 0s 1ms/step - loss: 0.2497 - acc: 0.0000e+00\n",
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"Epoch 5/8\n",
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"2/2 [==============================] - 0s 782us/step - loss: 0.2487 - acc: 0.0000e+00\n",
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"Epoch 6/8\n",
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"2/2 [==============================] - 0s 1000us/step - loss: 0.2476 - acc: 0.0000e+00\n",
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"Epoch 7/8\n",
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"2/2 [==============================] - 0s 813us/step - loss: 0.2466 - acc: 0.0000e+00\n",
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"Epoch 8/8\n",
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"2/2 [==============================] - 0s 740us/step - loss: 0.2456 - acc: 0.0000e+00\n",
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"2\n",
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"Epoch 1/8\n",
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"4/4 [==============================] - 0s 475us/step - loss: 0.2426 - acc: 0.0000e+00\n",
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"Epoch 2/8\n",
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"4/4 [==============================] - 0s 721us/step - loss: 0.2416 - acc: 0.0000e+00\n",
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"Epoch 3/8\n",
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"4/4 [==============================] - 0s 403us/step - loss: 0.2407 - acc: 0.0000e+00\n",
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"Epoch 4/8\n",
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"4/4 [==============================] - 0s 612us/step - loss: 0.2397 - acc: 0.0000e+00\n",
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"Epoch 5/8\n",
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"4/4 [==============================] - 0s 905us/step - loss: 0.2388 - acc: 0.0000e+00\n",
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"Epoch 6/8\n",
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"4/4 [==============================] - 0s 590us/step - loss: 0.2379 - acc: 0.0000e+00\n",
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"Epoch 7/8\n",
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"4/4 [==============================] - 0s 480us/step - loss: 0.2369 - acc: 0.0000e+00\n",
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"Epoch 8/8\n",
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"4/4 [==============================] - 0s 378us/step - loss: 0.2360 - acc: 0.0000e+00\n",
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"3\n",
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"Epoch 1/8\n",
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"10/10 [==============================] - 0s 194us/step - loss: 0.2350 - acc: 0.0000e+00\n",
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"Epoch 2/8\n",
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"10/10 [==============================] - 0s 203us/step - loss: 0.2342 - acc: 0.0000e+00\n",
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"Epoch 3/8\n",
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"10/10 [==============================] - 0s 280us/step - loss: 0.2335 - acc: 0.0000e+00\n",
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"Epoch 4/8\n",
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"10/10 [==============================] - 0s 241us/step - loss: 0.2327 - acc: 0.0000e+00\n",
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"Epoch 5/8\n",
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"10/10 [==============================] - 0s 202us/step - loss: 0.2319 - acc: 0.0000e+00\n",
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"Epoch 6/8\n",
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"10/10 [==============================] - 0s 185us/step - loss: 0.2312 - acc: 0.0000e+00\n",
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"Epoch 7/8\n",
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"10/10 [==============================] - 0s 200us/step - loss: 0.2304 - acc: 0.0000e+00\n",
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"Epoch 8/8\n",
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"10/10 [==============================] - 0s 254us/step - loss: 0.2297 - acc: 0.0000e+00\n",
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"4\n",
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"Epoch 1/8\n",
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"24/24 [==============================] - 0s 103us/step - loss: 0.2265 - acc: 0.0417\n",
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"Epoch 2/8\n",
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"24/24 [==============================] - 0s 81us/step - loss: 0.2259 - acc: 0.0417\n",
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"Epoch 3/8\n",
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"24/24 [==============================] - 0s 88us/step - loss: 0.2253 - acc: 0.0417\n",
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"Epoch 4/8\n",
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"24/24 [==============================] - 0s 74us/step - loss: 0.2247 - acc: 0.0417\n",
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"Epoch 5/8\n",
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"24/24 [==============================] - 0s 79us/step - loss: 0.2241 - acc: 0.0417\n",
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"Epoch 6/8\n",
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"24/24 [==============================] - 0s 95us/step - loss: 0.2235 - acc: 0.0417\n",
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"Epoch 7/8\n",
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"24/24 [==============================] - 0s 92us/step - loss: 0.2229 - acc: 0.0417\n",
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"Epoch 8/8\n",
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"24/24 [==============================] - 0s 87us/step - loss: 0.2223 - acc: 0.0417\n",
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"5\n",
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"Epoch 1/8\n",
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"54/54 [==============================] - 0s 31us/step - loss: 0.2333 - acc: 0.0000e+00\n",
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"Epoch 2/8\n",
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"54/54 [==============================] - 0s 32us/step - loss: 0.2327 - acc: 0.0000e+00\n",
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"Epoch 3/8\n",
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"54/54 [==============================] - 0s 32us/step - loss: 0.2322 - acc: 0.0000e+00\n",
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"Epoch 4/8\n",
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"54/54 [==============================] - 0s 40us/step - loss: 0.2316 - acc: 0.0000e+00\n",
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"Epoch 5/8\n",
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"54/54 [==============================] - 0s 36us/step - loss: 0.2310 - acc: 0.0000e+00\n",
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"Epoch 6/8\n",
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"54/54 [==============================] - 0s 144us/step - loss: 0.2304 - acc: 0.0000e+00\n",
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"Epoch 7/8\n",
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"54/54 [==============================] - 0s 41us/step - loss: 0.2299 - acc: 0.0000e+00\n",
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"Epoch 8/8\n",
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"54/54 [==============================] - 0s 91us/step - loss: 0.2293 - acc: 0.0000e+00\n",
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"6\n",
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"Epoch 1/8\n",
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"107/107 [==============================] - 0s 16us/step - loss: 0.2261 - acc: 0.0467\n",
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"Epoch 2/8\n",
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"107/107 [==============================] - 0s 15us/step - loss: 0.2255 - acc: 0.0467\n",
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"Epoch 3/8\n",
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"107/107 [==============================] - 0s 18us/step - loss: 0.2250 - acc: 0.0654\n",
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"Epoch 4/8\n",
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"107/107 [==============================] - 0s 25us/step - loss: 0.2245 - acc: 0.0654\n",
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"Epoch 5/8\n",
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"107/107 [==============================] - 0s 29us/step - loss: 0.2240 - acc: 0.0654\n",
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"Epoch 6/8\n",
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"107/107 [==============================] - 0s 23us/step - loss: 0.2235 - acc: 0.0654\n",
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"Epoch 7/8\n",
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"107/107 [==============================] - 0s 19us/step - loss: 0.2230 - acc: 0.0654\n",
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"Epoch 8/8\n",
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"107/107 [==============================] - 0s 16us/step - loss: 0.2225 - acc: 0.0654\n",
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"7\n",
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"Epoch 1/8\n",
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"222/222 [==============================] - 0s 8us/step - loss: 0.2266 - acc: 0.0000e+00\n",
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"Epoch 2/8\n",
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"222/222 [==============================] - 0s 10us/step - loss: 0.2261 - acc: 0.0000e+00\n",
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"Epoch 3/8\n",
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"222/222 [==============================] - 0s 9us/step - loss: 0.2257 - acc: 0.0000e+00\n",
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"Epoch 4/8\n",
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"222/222 [==============================] - 0s 9us/step - loss: 0.2252 - acc: 0.0000e+00\n",
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"Epoch 5/8\n",
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"222/222 [==============================] - 0s 11us/step - loss: 0.2247 - acc: 0.0000e+00\n",
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"Epoch 6/8\n",
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"222/222 [==============================] - 0s 11us/step - loss: 0.2243 - acc: 0.0000e+00\n",
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"Epoch 7/8\n",
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"222/222 [==============================] - 0s 8us/step - loss: 0.2238 - acc: 0.0000e+00\n",
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"Epoch 8/8\n",
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"222/222 [==============================] - 0s 12us/step - loss: 0.2233 - acc: 0.0000e+00\n",
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"8\n",
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"Epoch 1/8\n",
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"496/496 [==============================] - 0s 5us/step - loss: 0.2231 - acc: 0.0786\n",
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"Epoch 2/8\n",
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"496/496 [==============================] - 0s 5us/step - loss: 0.2227 - acc: 0.0806\n",
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"Epoch 3/8\n",
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"496/496 [==============================] - 0s 6us/step - loss: 0.2223 - acc: 0.0827\n",
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"Epoch 4/8\n",
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"496/496 [==============================] - 0s 6us/step - loss: 0.2219 - acc: 0.0867\n",
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"Epoch 5/8\n",
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"496/496 [==============================] - 0s 4us/step - loss: 0.2214 - acc: 0.0887\n",
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"Epoch 6/8\n",
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"496/496 [==============================] - 0s 5us/step - loss: 0.2210 - acc: 0.0887\n",
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"Epoch 7/8\n",
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"496/496 [==============================] - 0s 5us/step - loss: 0.2206 - acc: 0.0887\n",
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"Epoch 8/8\n",
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"496/496 [==============================] - 0s 7us/step - loss: 0.2202 - acc: 0.0907\n",
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"9\n"
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]
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},
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{
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"ename": "ValueError",
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"evalue": "Input arrays should have the same number of samples as target arrays. Found 1151 input samples and 1000 target samples.",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-5-2d935fb7ef0f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;31m# Train the network.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 26\u001b[0m \u001b[0;31m#model.train_on_batch(np.array(temp), np.array(target))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/usr/lib/python3.6/site-packages/keras/models.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m 958\u001b[0m \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 959\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 960\u001b[0;31m validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m 961\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 962\u001b[0m def evaluate(self, x, y, batch_size=32, verbose=1,\n",
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"\u001b[0;32m/usr/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m 1572\u001b[0m \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1573\u001b[0m \u001b[0mcheck_batch_axis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1574\u001b[0;31m batch_size=batch_size)\n\u001b[0m\u001b[1;32m 1575\u001b[0m \u001b[0;31m# Prepare validation data.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1576\u001b[0m \u001b[0mdo_validation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/usr/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[0;34m(self, x, y, sample_weight, class_weight, check_batch_axis, batch_size)\u001b[0m\n\u001b[1;32m 1417\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mref\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1418\u001b[0m in zip(y, sample_weights, class_weights, self._feed_sample_weight_modes)]\n\u001b[0;32m-> 1419\u001b[0;31m \u001b[0m_check_array_lengths\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1420\u001b[0m _check_loss_and_target_compatibility(y,\n\u001b[1;32m 1421\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_feed_loss_fns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[0;32m/usr/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_check_array_lengths\u001b[0;34m(inputs, targets, weights)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0;34m'the same number of samples as target arrays. '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0;34m'Found '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' input samples '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m 'and ' + str(list(set_y)[0]) + ' target samples.')\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset_w\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m raise ValueError('All sample_weight arrays should have '\n",
|
||||
"\u001b[0;31mValueError\u001b[0m: Input arrays should have the same number of samples as target arrays. Found 1151 input samples and 1000 target samples."
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for i in range(29):\n",
|
||||
" filename = join('data', str(i) + '.bin')\n",
|
||||
|
|
@ -228,7 +416,7 @@
|
|||
" counter = 0\n",
|
||||
" training = []\n",
|
||||
"\n",
|
||||
" while(data and counter < 1000):\n",
|
||||
" while(data and counter < 2000):\n",
|
||||
" bin_data = reduce(format_input, list(data), [])\n",
|
||||
" bin_data.reverse()\n",
|
||||
" bin_data = bin_data[16:]\n",
|
||||
|
|
@ -241,7 +429,7 @@
|
|||
" #print(training[0])\n",
|
||||
" # Train the network.\n",
|
||||
"\n",
|
||||
" model.fit(np.array(training), np.array(target[i]), epochs=5, batch_size=100)\n",
|
||||
" model.fit(np.array(training), np.array(target[i]), epochs=8, batch_size=2000)\n",
|
||||
" #model.train_on_batch(np.array(temp), np.array(target))"
|
||||
]
|
||||
},
|
||||
|
|
@ -265,26 +453,41 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"18\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Used for testing data\n",
|
||||
"\n",
|
||||
"with open('data/18.bin', 'rb') as f:\n",
|
||||
" \n",
|
||||
" for i in range(1000):\n",
|
||||
"i = 18\n",
|
||||
"\n",
|
||||
"filename = join('data', str(i) + '.bin')\n",
|
||||
"\n",
|
||||
"# Debugging to print the current file from which states are being parsed.\n",
|
||||
"print(i)\n",
|
||||
"\n",
|
||||
"with open(filename, 'rb') as f:\n",
|
||||
"\n",
|
||||
" for i in range(2000):\n",
|
||||
" data = f.read(8)\n",
|
||||
" \n",
|
||||
"\n",
|
||||
" data = f.read(8)\n",
|
||||
" \n",
|
||||
"\n",
|
||||
" counter = 0\n",
|
||||
" \n",
|
||||
"\n",
|
||||
" testing = []\n",
|
||||
" \n",
|
||||
"\n",
|
||||
" testing_target = []\n",
|
||||
" \n",
|
||||
" while(data):\n",
|
||||
"\n",
|
||||
" while(data and counter < 10000):\n",
|
||||
" bin_data = reduce(format_input, list(data), [])\n",
|
||||
" bin_data.reverse()\n",
|
||||
" bin_data = bin_data[16:]\n",
|
||||
|
|
@ -294,20 +497,18 @@
|
|||
" pos_data = reduce(format_pos, enumerate(list(data)), [])\n",
|
||||
" pos_data.reverse()\n",
|
||||
" pos_data = pos_data[1:]\n",
|
||||
" \n",
|
||||
"\n",
|
||||
" state_pos = []\n",
|
||||
"\n",
|
||||
" for p in pos_data:\n",
|
||||
" state_pos.append(p[1])\n",
|
||||
" \n",
|
||||
"\n",
|
||||
" testing_target_pos = reduce(generate_pos, pos_data, [])\n",
|
||||
"\n",
|
||||
" testing_target.append(format_man_dist(man_dist_state(state_pos, testing_target_pos)))\n",
|
||||
" \n",
|
||||
"\n",
|
||||
" counter += 1\n",
|
||||
" data = f.read(8)\n",
|
||||
" \n",
|
||||
"#print(testing_target)"
|
||||
" data = f.read(8)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -319,9 +520,17 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"10000/10000 [==============================] - 0s 2us/step\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Evaluate accuracy\n",
|
||||
" \n",
|
||||
|
|
@ -334,9 +543,19 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"52.7983333333\n",
|
||||
"[0.23647564649581909, 0.039100000075995921]\n",
|
||||
"['loss', 'acc']\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"output = []\n",
|
||||
"\n",
|
||||
|
|
|
|||
Reference in a new issue