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added figs

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This commit is contained in:
mhjensen
2018-10-18 11:54:00 +02:00
parent 05f321f985
commit cce84f3dcb
4 changed files with 250 additions and 192 deletions
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doc/pub/NeuralNet/html/figslides/cnn.jpeg
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doc/pub/NeuralNet/html/figslides/nn.jpeg
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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",
@@ -1691,9 +1689,7 @@
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# import necessary packages\n",
@@ -1760,9 +1756,7 @@
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
@@ -1884,9 +1878,7 @@
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# building our neural network\n",
@@ -1963,9 +1955,7 @@
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# setup the feed-forward pass, subscript h = hidden layer\n",
@@ -2130,9 +2120,7 @@
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# to categorical turns our integer vector into a onehot representation\n",
@@ -2231,9 +2219,7 @@
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"class NeuralNetwork:\n",
@@ -2356,9 +2342,7 @@
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"epochs = 100\n",
@@ -2392,9 +2376,7 @@
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"eta_vals = np.logspace(-5, 1, 7)\n",
@@ -2429,9 +2411,7 @@
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# visual representation of grid search\n",
@@ -2491,9 +2471,7 @@
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"from sklearn.neural_network import MLPClassifier\n",
@@ -2524,9 +2502,7 @@
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# optional\n",
@@ -2609,9 +2585,7 @@
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"pip3 install tensorflow"
@@ -2627,9 +2601,7 @@
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"conda install tensorflow"
@@ -2645,9 +2617,7 @@
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# import necessary packages\n",
@@ -2697,9 +2667,7 @@
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"from keras.utils import to_categorical\n",
@@ -2729,9 +2697,7 @@
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"import tensorflow as tf\n",
@@ -2877,9 +2843,7 @@
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"epochs = 100\n",
@@ -2894,9 +2858,7 @@
{
"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",
@@ -2919,9 +2881,7 @@
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# optional\n",
@@ -2960,9 +2920,7 @@
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# optional\n",
@@ -2986,9 +2944,7 @@
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"conda install keras"
@@ -3004,9 +2960,7 @@
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"pip3 install keras"
@@ -3022,9 +2976,7 @@
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"from keras.models import Sequential\n",
@@ -3047,9 +2999,7 @@
{
"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",
@@ -3072,9 +3022,7 @@
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# optional\n",
@@ -3439,7 +3387,25 @@
]
}
],
"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
}
doc/pub/Regression/ipynb/Regression.ipynb
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