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{
"cells": [
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": true,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"from jax import numpy as jnp\n",
"from jax import jit, vmap"
]
},
{
"cell_type": "code",
"execution_count": 22,
"outputs": [],
"source": [
"@jit\n",
"def sigmoid(x):\n",
" return 1 / (1 + jnp.exp(-1 * x))"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 75,
"outputs": [],
"source": [
"@jit\n",
"def relu(x):\n",
" return x * (x > 0)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 98,
"outputs": [],
"source": [
"@jit\n",
"@vmap\n",
"def softmax(x):\n",
" \"\"\"\n",
" >>> jnp.sum(softmax(jnp.array([[1, 2, 4], [1, 2, 3], [1, 2, 3]])), axis=1)\n",
" DeviceArray([1., 1., 1.], dtype=float32)\n",
" \"\"\"\n",
" return jnp.exp(x) / jnp.sum(jnp.exp(x))"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
} |