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updating splines

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This commit is contained in:
mhjensen
2018-10-12 04:55:15 +02:00
parent 74e29912f4
commit c71c008945
83 changed files with 14530 additions and 4991 deletions
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doc/pub/NeuralNet/ipynb/NeuralNet.ipynb
+45 -79
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@@ -597,9 +597,7 @@
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
@@ -1520,9 +1518,7 @@
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# import necessary packages\n",
@@ -1589,9 +1585,7 @@
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
@@ -1713,9 +1707,7 @@
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# building our neural network\n",
@@ -1792,9 +1784,7 @@
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# setup the feed-forward pass, subscript h = hidden layer\n",
@@ -1957,9 +1947,7 @@
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# to categorical turns our integer vector into a onehot representation\n",
@@ -2061,9 +2049,7 @@
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"class NeuralNetwork:\n",
@@ -2186,9 +2172,7 @@
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"epochs = 100\n",
@@ -2222,9 +2206,7 @@
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"eta_vals = np.logspace(-5, 1, 7)\n",
@@ -2259,9 +2241,7 @@
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# visual representation of grid search\n",
@@ -2321,9 +2301,7 @@
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"from sklearn.neural_network import MLPClassifier\n",
@@ -2354,9 +2332,7 @@
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# optional\n",
@@ -2439,9 +2415,7 @@
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"pip3 install tensorflow"
@@ -2457,9 +2431,7 @@
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"conda install tensorflow"
@@ -2475,9 +2447,7 @@
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# import necessary packages\n",
@@ -2527,9 +2497,7 @@
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"from keras.utils import to_categorical\n",
@@ -2559,9 +2527,7 @@
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"import tensorflow as tf\n",
@@ -2707,9 +2673,7 @@
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"epochs = 100\n",
@@ -2724,9 +2688,7 @@
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"DNN_tf = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n",
@@ -2749,9 +2711,7 @@
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# optional\n",
@@ -2790,9 +2750,7 @@
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# optional\n",
@@ -2816,9 +2774,7 @@
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"conda install keras"
@@ -2834,9 +2790,7 @@
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"pip3 install keras"
@@ -2852,9 +2806,7 @@
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"from keras.models import Sequential\n",
@@ -2877,9 +2829,7 @@
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"DNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n",
@@ -2902,9 +2852,7 @@
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# optional\n",
@@ -2941,7 +2889,25 @@
]
}
],
"metadata": {},
"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": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}