mlpy_test_multilinearperceptron.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from mlpy.neural import MultiLayerPerceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8, 3), (8, 3))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# x and y are counting in binary\n",
    "\n",
    "X = np.array([[0, 0, 0],\n",
    "             [0, 0, 1],\n",
    "             [0, 1, 0],\n",
    "             [0, 1, 1],\n",
    "             [1, 0, 0],\n",
    "             [1, 0, 1],\n",
    "             [1, 1, 0],\n",
    "             [1, 1, 1]])\n",
    "\n",
    "y = np.array([[0, 0, 1],\n",
    "              [0, 1, 0],\n",
    "              [0, 1, 1],\n",
    "              [1, 0, 0],\n",
    "              [1, 0, 1],\n",
    "              [1, 1, 0],\n",
    "              [1, 1, 1],\n",
    "              [0, 0, 0]])\n",
    "\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp = MultiLayerPerceptron(X.shape[1], 3, y.shape[1], hidden_layers=2, epochs=40000, batch_size=3, \n",
    "                           lr=0.025, print_iters=5000, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 0 : error 7.85285\n",
      "epoch 5000 : error 4.71623\n",
      "epoch 10000 : error 3.60514\n",
      "epoch 15000 : error 4.23015\n",
      "epoch 20000 : error 2.66795\n",
      "epoch 25000 : error 2.36743\n",
      "epoch 30000 : error 3.18055\n",
      "epoch 35000 : error 2.38633\n"
     ]
    }
   ],
   "source": [
    "mlp.train(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.10818134,  0.71735427,  0.96900404]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp.predict_proba(np.array([[0, 1, 0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  1.]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp.predict(np.array([[0, 1, 0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0.]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp.predict(np.array([[0, 1, 1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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