nn420-private-pine64backup/nn_puzzle_solver.ipynb

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 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Setting up our imported libraries.\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "240000\n",
      "240\n",
      "[[ 0.28588903  0.05308564  0.99171479 ...,  0.92657084  0.09114427\n",
      "   0.76495161]\n",
      " [ 0.50998915  0.74032164  0.04898317 ...,  0.77742777  0.46720853\n",
      "   0.01731216]\n",
      " [ 0.31522802  0.11448062  0.40291163 ...,  0.87519373  0.31255597\n",
      "   0.7202333 ]\n",
      " ..., \n",
      " [ 0.13906598  0.99536312  0.36709839 ...,  0.68740262  0.9536678\n",
      "   0.53053495]\n",
      " [ 0.13696298  0.91392043  0.5846018  ...,  0.84365665  0.92837426\n",
      "   0.18738981]\n",
      " [ 0.05775272  0.7919279   0.51444914 ...,  0.53078037  0.67684536\n",
      "   0.25327729]]\n"
     ]
    }
   ],
   "source": [
    "# Generating dummy data.\n",
    "\n",
    "data = np.random.random((1000,240))\n",
    "output = np.random.random((1000, 29))\n",
    "\n",
    "# Replace this with parser code later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1000/1000 [==============================] - 4s 4ms/step - loss: 0.0986 - acc: 0.0250\n",
      "Epoch 2/5\n",
      "1000/1000 [==============================] - 0s 155us/step - loss: 0.0936 - acc: 0.0350\n",
      "Epoch 3/5\n",
      "1000/1000 [==============================] - 0s 157us/step - loss: 0.0912 - acc: 0.0380\n",
      "Epoch 4/5\n",
      "1000/1000 [==============================] - 0s 154us/step - loss: 0.0901 - acc: 0.0370\n",
      "Epoch 5/5\n",
      "1000/1000 [==============================] - 0s 162us/step - loss: 0.0894 - acc: 0.0370\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fe4fae027f0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sets up a Sequential model, Sequential is all\n",
    "# that should need to be used for this project,\n",
    "# considering that it will only be dealing with\n",
    "# a linear stack of layers of neurons.\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "# Adding layers to the model.\n",
    "\n",
    "model.add(Dense(units=240, activation='tanh', input_dim=240))\n",
    "model.add(Dense(units=120, activation='tanh'))\n",
    "model.add(Dense(units=29, activation='sigmoid'))\n",
    "\n",
    "# Configure the learning process.\n",
    "\n",
    "model.compile(optimizer='sgd',\n",
    "             loss='mean_squared_error',\n",
    "             metrics=['accuracy'])\n",
    "\n",
    "# Train the network.\n",
    "\n",
    "model.fit(data, output, epochs=5, batch_size=10)\n",
    "\n",
    "# Generating predictions should look like this:\n",
    "\n",
    "# predictions = model.predict(testing_data, batch_size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0xf23456789abcdef0\n"
     ]
    }
   ],
   "source": [
    "with open('data/0.bin', 'rb') as f:\n",
    "    data = f.read()\n",
    "\n",
    "print(hex(int.from_bytes(data, byteorder='little', signed=False)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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