AISE/iui-group-l-name-zensiert
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Canceled hyperparameter at 3x2400/2x1200, added future plans to presentation

Browse Source
This commit is contained in:
Tuan-Dat Tran
2021-06-09 08:12:50 +00:00
parent 33941658b9
commit d3807c1fc6
4 changed files with 638 additions and 171 deletions
Download Patch File Download Diff File Expand all files Collapse all files

459
1-first-project/tdt/Hyperparameter.ipynb
View File

@@ -3,7 +3,7 @@
{
"cell_type": "code",
"execution_count": 1,
"id": "25685460",
"id": "71b073fd",
"metadata": {},
"outputs": [],
"source": [
@@ -14,7 +14,7 @@
{
"cell_type": "code",
"execution_count": 2,
"id": "a7b5d6ab",
"id": "faccec4b",
"metadata": {},
"outputs": [],
"source": [
@@ -30,7 +30,7 @@
{
"cell_type": "code",
"execution_count": 3,
"id": "8a37e95b",
"id": "557006eb",
"metadata": {},
"outputs": [],
"source": [
@@ -52,7 +52,7 @@
{
"cell_type": "code",
"execution_count": 4,
"id": "53e288a8",
"id": "311c9b66",
"metadata": {},
"outputs": [],
"source": [
@@ -73,7 +73,7 @@
{
"cell_type": "code",
"execution_count": 5,
"id": "0e638fb8",
"id": "53b9bb75",
"metadata": {},
"outputs": [],
"source": [
@@ -97,7 +97,7 @@
{
"cell_type": "code",
"execution_count": 6,
"id": "5080bf16",
"id": "34c391e7",
"metadata": {},
"outputs": [],
"source": [
@@ -111,7 +111,7 @@
{
"cell_type": "code",
"execution_count": 7,
"id": "41f26d6c",
"id": "22346c9c",
"metadata": {},
"outputs": [],
"source": [
@@ -138,7 +138,7 @@
{
"cell_type": "code",
"execution_count": 8,
"id": "9bf4ea28",
"id": "dc81d9b9",
"metadata": {},
"outputs": [],
"source": [
@@ -174,7 +174,7 @@
{
"cell_type": "code",
"execution_count": 9,
"id": "fde0b3cf",
"id": "1a80e403",
"metadata": {},
"outputs": [],
"source": [
@@ -197,7 +197,7 @@
{
"cell_type": "code",
"execution_count": 10,
"id": "9712dd25",
"id": "e3dd7348",
"metadata": {},
"outputs": [],
"source": [
@@ -218,7 +218,7 @@
{
"cell_type": "code",
"execution_count": 11,
"id": "6decb953",
"id": "5bbb81d7",
"metadata": {},
"outputs": [],
"source": [
@@ -229,8 +229,8 @@
},
{
"cell_type": "code",
"execution_count": null,
"id": "ff6a75e9",
"execution_count": 12,
"id": "6f2dc487",
"metadata": {},
"outputs": [
{
@@ -939,7 +939,427 @@
" Dense Count 1: 2\n",
" Dense Neurons 1: 2400\n",
" Dense Count 2: 1\n",
" Dense Neurons 2: 2400\n"
" Dense Neurons 2: 2400\n",
"Accuracy: 78.00\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 2\n",
" Dense Neurons 1: 2400\n",
" Dense Count 2: 2\n",
" Dense Neurons 2: 600\n",
"Accuracy: 78.61\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 2\n",
" Dense Neurons 1: 2400\n",
" Dense Count 2: 2\n",
" Dense Neurons 2: 1200\n",
"Accuracy: 78.68\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 2\n",
" Dense Neurons 1: 2400\n",
" Dense Count 2: 2\n",
" Dense Neurons 2: 1800\n",
"Accuracy: 78.00\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 2\n",
" Dense Neurons 1: 2400\n",
" Dense Count 2: 2\n",
" Dense Neurons 2: 2400\n",
"Accuracy: 78.21\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 2\n",
" Dense Neurons 1: 2400\n",
" Dense Count 2: 3\n",
" Dense Neurons 2: 600\n",
"Accuracy: 78.99\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 2\n",
" Dense Neurons 1: 2400\n",
" Dense Count 2: 3\n",
" Dense Neurons 2: 1200\n",
"Accuracy: 78.20\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 2\n",
" Dense Neurons 1: 2400\n",
" Dense Count 2: 3\n",
" Dense Neurons 2: 1800\n",
"Accuracy: 77.88\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 2\n",
" Dense Neurons 1: 2400\n",
" Dense Count 2: 3\n",
" Dense Neurons 2: 2400\n",
"Accuracy: 77.83\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 600\n",
" Dense Count 2: 1\n",
" Dense Neurons 2: 600\n",
"Accuracy: 77.78\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 600\n",
" Dense Count 2: 1\n",
" Dense Neurons 2: 1200\n",
"Accuracy: 77.93\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 600\n",
" Dense Count 2: 1\n",
" Dense Neurons 2: 1800\n",
"Accuracy: 77.98\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 600\n",
" Dense Count 2: 1\n",
" Dense Neurons 2: 2400\n",
"Accuracy: 78.02\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 600\n",
" Dense Count 2: 2\n",
" Dense Neurons 2: 600\n",
"Accuracy: 78.04\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 600\n",
" Dense Count 2: 2\n",
" Dense Neurons 2: 1200\n",
"Accuracy: 77.59\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 600\n",
" Dense Count 2: 2\n",
" Dense Neurons 2: 1800\n",
"Accuracy: 77.97\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 600\n",
" Dense Count 2: 2\n",
" Dense Neurons 2: 2400\n",
"Accuracy: 77.55\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 600\n",
" Dense Count 2: 3\n",
" Dense Neurons 2: 600\n",
"Accuracy: 77.38\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 600\n",
" Dense Count 2: 3\n",
" Dense Neurons 2: 1200\n",
"Accuracy: 77.40\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 600\n",
" Dense Count 2: 3\n",
" Dense Neurons 2: 1800\n",
"Accuracy: 77.10\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 600\n",
" Dense Count 2: 3\n",
" Dense Neurons 2: 2400\n",
"Accuracy: 76.91\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1200\n",
" Dense Count 2: 1\n",
" Dense Neurons 2: 600\n",
"Accuracy: 78.61\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1200\n",
" Dense Count 2: 1\n",
" Dense Neurons 2: 1200\n",
"Accuracy: 78.61\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1200\n",
" Dense Count 2: 1\n",
" Dense Neurons 2: 1800\n",
"Accuracy: 78.44\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1200\n",
" Dense Count 2: 1\n",
" Dense Neurons 2: 2400\n",
"Accuracy: 78.40\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1200\n",
" Dense Count 2: 2\n",
" Dense Neurons 2: 600\n",
"Accuracy: 78.46\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1200\n",
" Dense Count 2: 2\n",
" Dense Neurons 2: 1200\n",
"Accuracy: 78.59\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1200\n",
" Dense Count 2: 2\n",
" Dense Neurons 2: 1800\n",
"Accuracy: 78.17\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1200\n",
" Dense Count 2: 2\n",
" Dense Neurons 2: 2400\n",
"Accuracy: 78.02\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1200\n",
" Dense Count 2: 3\n",
" Dense Neurons 2: 600\n",
"Accuracy: 78.15\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1200\n",
" Dense Count 2: 3\n",
" Dense Neurons 2: 1200\n",
"Accuracy: 77.58\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1200\n",
" Dense Count 2: 3\n",
" Dense Neurons 2: 2400\n",
"Accuracy: 77.02\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1800\n",
" Dense Count 2: 1\n",
" Dense Neurons 2: 600\n",
"Accuracy: 78.60\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1800\n",
" Dense Count 2: 1\n",
" Dense Neurons 2: 1200\n",
"Accuracy: 78.39\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1800\n",
" Dense Count 2: 1\n",
" Dense Neurons 2: 1800\n",
"Accuracy: 78.58\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1800\n",
" Dense Count 2: 1\n",
" Dense Neurons 2: 2400\n",
"Accuracy: 78.49\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1800\n",
" Dense Count 2: 2\n",
" Dense Neurons 2: 600\n",
"Accuracy: 78.37\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1800\n",
" Dense Count 2: 2\n",
" Dense Neurons 2: 1200\n",
"Accuracy: 78.45\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1800\n",
" Dense Count 2: 2\n",
" Dense Neurons 2: 1800\n",
"Accuracy: 78.02\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1800\n",
" Dense Count 2: 2\n",
" Dense Neurons 2: 2400\n",
"Accuracy: 77.69\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1800\n",
" Dense Count 2: 3\n",
" Dense Neurons 2: 600\n",
"Accuracy: 77.95\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1800\n",
" Dense Count 2: 3\n",
" Dense Neurons 2: 1200\n",
"Accuracy: 77.59\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1800\n",
" Dense Count 2: 3\n",
" Dense Neurons 2: 1800\n",
"Accuracy: 77.53\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 1800\n",
" Dense Count 2: 3\n",
" Dense Neurons 2: 2400\n",
"Accuracy: 77.26\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 2400\n",
" Dense Count 2: 1\n",
" Dense Neurons 2: 600\n",
"Accuracy: 78.33\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 2400\n",
" Dense Count 2: 1\n",
" Dense Neurons 2: 1200\n",
"Accuracy: 78.39\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 2400\n",
" Dense Count 2: 1\n",
" Dense Neurons 2: 1800\n",
"Accuracy: 78.46\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 2400\n",
" Dense Count 2: 1\n",
" Dense Neurons 2: 2400\n",
"Accuracy: 78.38\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 2400\n",
" Dense Count 2: 2\n",
" Dense Neurons 2: 600\n",
"Accuracy: 78.62\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 2400\n",
" Dense Count 2: 2\n",
" Dense Neurons 2: 1200\n",
"Accuracy: 78.02\n",
"Testing with: Threshold: 70\n",
" Leeway: 0\n",
" Epoch: 20\n",
" Dense Count 1: 3\n",
" Dense Neurons 1: 2400\n",
" Dense Count 2: 2\n",
" Dense Neurons 2: 1800\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
"\u001b[0;32m<ipython-input-9-47e2893956f1>\u001b[0m in \u001b[0;36mget_avg_acc\u001b[0;34m(X_train, y_train, X_test, y_test, epoch, dcount, dnons, dcount2, dnons2)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mAVG_FROM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdcount\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdnons\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdcount2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdnons2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m model.fit(X_train, y_train, \n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/jupyterhub/lib/python3.8/site-packages/tensorflow/python/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, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1129\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1130\u001b[0m steps_per_execution=self._steps_per_execution)\n\u001b[0;32m-> 1131\u001b[0;31m val_logs = self.evaluate(\n\u001b[0m\u001b[1;32m 1132\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_x\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1133\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/jupyterhub/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, return_dict)\u001b[0m\n\u001b[1;32m 1387\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'test'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep_num\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_r\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1388\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_test_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1389\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1390\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1391\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/jupyterhub/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 826\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 827\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtm\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 828\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 829\u001b[0m \u001b[0mcompiler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"xla\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_experimental_compile\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"nonXla\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 830\u001b[0m \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/jupyterhub/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 860\u001b[0m \u001b[0;31m# In this case we have not created variables on the first call. So we can\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 861\u001b[0m \u001b[0;31m# run the first trace but we should fail if variables are created.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 862\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 863\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_created_variables\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 864\u001b[0m raise ValueError(\"Creating variables on a non-first call to a function\"\n",
"\u001b[0;32m/opt/jupyterhub/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2940\u001b[0m (graph_function,\n\u001b[1;32m 2941\u001b[0m filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m-> 2942\u001b[0;31m return graph_function._call_flat(\n\u001b[0m\u001b[1;32m 2943\u001b[0m filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access\n\u001b[1;32m 2944\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/jupyterhub/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1916\u001b[0m and executing_eagerly):\n\u001b[1;32m 1917\u001b[0m \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1918\u001b[0;31m return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m 1919\u001b[0m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m 1920\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n",
"\u001b[0;32m/opt/jupyterhub/lib/python3.8/site-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 553\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 554\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 555\u001b[0;31m outputs = execute.execute(\n\u001b[0m\u001b[1;32m 556\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 557\u001b[0m \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/jupyterhub/lib/python3.8/site-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m 60\u001b[0m inputs, attrs, num_outputs)\n\u001b[1;32m 61\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
@@ -974,13 +1394,14 @@
" 'DENSE_COUNT2': dc2,\n",
" 'DENSE_NEURON2': dn2,\n",
" 'Accuracy': acc}, ignore_index=True)\n",
" print(f\"Accuracy: {acc*100:.2f}\\n\\n\")"
" print(f\"Accuracy: {acc*100:.2f}\\n\\n\")\n",
" result.to_csv('results.csv', header=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "89a47c03",
"execution_count": 13,
"id": "88b3193a",
"metadata": {},
"outputs": [],
"source": [
@@ -989,8 +1410,8 @@
},
{
"cell_type": "code",
"execution_count": null,
"id": "7520408c",
"execution_count": 14,
"id": "5219e081",
"metadata": {},
"outputs": [],
"source": [