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@Drakensberge
Last active June 13, 2020 00:40
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Self Normalising Networks empirical experiments\n",
"\n",
"We examine the effect of using the selu (\"scaled exponential linear unit\") activation defined by <a href=\"https://arxiv.org/abs/1706.02515\">Klambauer et al</a> on the distribution of activations.\n",
"This code is meant to accompany the full write-up that can be found <a href=\"medium.com\">here</a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n",
"\n",
"* Klambauer et al <a href=\"https://arxiv.org/abs/1706.02515\">\"Self-Normalizing Neural Networks\"</a> \n",
"* <a href=\"https://news.ycombinator.com/item?id=14527686\">Hacker news discussion\n",
"* Code below adapted from user CaseOfTuesday on <a href=\"https://www.reddit.com/r/MachineLearning/comments/6g5tg1/r_selfnormalizing_neural_networks_improved_elu/\"> reddit</a>"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import seaborn as sns\n",
"\n",
"\n",
"def selu(x):\n",
" alpha = 1.6732632423543772848170429916717\n",
" scale = 1.0507009873554804934193349852946\n",
" return scale*np.where(x>=0.0, x, alpha*np.exp(x)-alpha)"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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+zS/V7oPVkqQ+qQm69sIM5RJdAB2I+CLsHK6s12f5h7Ruy2HVN7kkScMyU3RZ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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b920850>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set(style=\"dark\", palette=\"deep\")\n",
"plt.plot(np.linspace(-5, 5),selu(np.linspace(-5, 5)));"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean after 100 layers: min=-0.168811 max=0.149980\n",
"Stdev after 100 layers: min=0.848149 max=1.242707\n"
]
}
],
"source": [
"x = np.random.normal(size=(300, 200))\n",
"means = []\n",
"stds = []\n",
"for _ in range(100):\n",
" w = np.random.normal(size=(200, 200), scale=np.sqrt(1/200.0)) # their initialization scheme\n",
" z = np.dot(x, w)\n",
" x = selu(z)\n",
" m = np.mean(x, axis=1)\n",
" s = np.std(x, axis=1)\n",
" means.append(np.mean(m))\n",
" stds.append(np.mean(s))\n",
"print \"Mean after 100 layers: min={:2f} max={:2f}\".format( m.min(), m.max())\n",
"print \"Stdev after 100 layers: min={:2f} max={:2f}\".format( s.min(), s.max())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The distribution over means of activations y = selu(w,x)\n",
"The means of activations should be very near to 0 most of the time"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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KGdVNQzjVOox5hcmYX5QidRyKIgtLeJ2Xpo7FSzHB7fFh955GyAQBG75SBoGj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\ni5eIiCiEWLxEREQh9P8B/JOImYoWuLcAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11c9f9ed0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_style({\"text.color\": \".9\"})\n",
"sns.distplot(means);\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The distribution over stdev of activations y = selu(w,x)\n",
"The stdev of the distribution of activations should be very near to 1 most of the time"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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HJzA45kZBpgXJlgTZcWLWmkXZEAI40NgrOwpFKRYvUYw4fn60W8XRbkitXJgJnVbBvuO9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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11c5d0210>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# distribution over stdev of activations y = selu(w,x)\n",
"sns.set_style({\"text.color\": \".9\"})\n",
"sns.distplot(stds);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The distribution of activations in a layer\n",
"Each unit should act as some kind of binary feature. So we'd hope to see a bimodal distribution representing whether the feature is present or not. "
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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KVVm+Mtfm/tsqMeqT+M2h9iWVjdk95ECbpCInTvPB5iJFp+HWtYWMjruX3Cy3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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11907ad90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set(style=\"dark\", palette=\"deep\")\n",
"f, axes = plt.subplots(4, 4, figsize=(7, 7), sharex=True, sharey=True)\n",
"sns.despine(left=True)\n",
"for i in range(4):\n",
" for j in range(4):\n",
" sns.distplot(x[-i-j],hist=False, ax=axes[i,j]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The distribution of pre-activations in a layer\n",
"If you look at the distribution of inputs to the activation function, you'd expect to see a Gaussian distribution because they are of the form \n",
"$\n",
"z= \\sum_{i=1}^n w_i x_i\n",
"$\n",
"You can't quite use the classical Central Limit Theorem for this as you don't have the sums of iid variables. The authors instead appeal to Lyapunov CLT :)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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9Qa5f5zLkXNVcakqsPPnVKm7fkkvnUIgn/qWbMyuoLJOqafzkzSF6R8Ps2Z5n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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12115aad0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set(style=\"dark\", palette=\"muted\", color_codes=True)\n",
"f, axes = plt.subplots(4, 4, figsize=(7, 7), sharex=True, sharey=True)\n",
"sns.despine(left=True)\n",
"for i in range(4):\n",
" for j in range(4):\n",
" sns.distplot(z[-i-j],hist=False, ax=axes[i,j]);"
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initial stdev: min=8.528792 max=11.309719\n",
"Mean after 1 layers: min=2.119823 max=5.047522\n",
"Stdev after 1 layers: min=5.346847 max=7.990360\n"
]
}
],
"source": [
"# Show shrink in variance\n",
"x = np.random.normal(size=(300, 200), scale=np.sqrt(100.0))\n",
"print 'Initial stdev: min={:2f} max={:2f}'.format(np.std(x, axis=1).min(), np.std(x, axis=1).max())\n",
"means = []\n",
"stds = []\n",
"for _ in range(1):\n",
" w = np.random.normal(size=(200, 200), scale=np.sqrt(1/200.0)) # their initialization scheme\n",
" z = np.dot(x, w)\n",
" x = selu(z)\n",
" m = np.mean(x, axis=1)\n",
" s = np.std(x, axis=1)\n",
" means.append(np.mean(m))\n",
" stds.append(np.mean(s))\n",
"print \"Mean after 1 layers: min={:2f} max={:2f}\".format( m.min(), m.max())\n",
"print \"Stdev after 1 layers: min={:2f} max={:2f}\".format( s.min(), s.max())"
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initial stdev: min=0.026893 max=0.035684\n",
"Mean after 1 layers: min=-0.017219 max=-0.000215\n",
"Stdev after 1 layers: min=0.033331 max=0.055207\n"
]
}
],
"source": [
"# Show growth in variance\n",
"x = np.random.normal(size=(300, 200), scale=np.sqrt(1/1000.0))\n",
"print 'Initial stdev: min={:2f} max={:2f}'.format(np.std(x, axis=1).min(), np.std(x, axis=1).max())\n",
"means = []\n",
"stds = []\n",
"for _ in range(1):\n",
" w = np.random.normal(size=(200, 200), scale=np.sqrt(1/200.0)) # their initialization scheme\n",
" z = np.dot(x, w)\n",
" x = selu(z)\n",
" m = np.mean(x, axis=1)\n",
" s = np.std(x, axis=1)\n",
" means.append(np.mean(m))\n",
" stds.append(np.mean(s))\n",
"print \"Mean after 1 layers: min={:2f} max={:2f}\".format( m.min(), m.max())\n",
"print \"Stdev after 1 layers: min={:2f} max={:2f}\".format( s.min(), s.max())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"celltoolbar": "Raw Cell Format",
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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