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nn420-private-pine64backup/nn_puzzle_solver.ipynb

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Initial commit.
2017-12-01 14:27:20 -06:00
{
"cells": [
Cleaning up code.
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Setting up our imported libraries."
]
},
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{
"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
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"source": [
Should be feature complete. Onto debugging and extracting data.
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"from functools import reduce\n",
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"import numpy as np\n",
"from keras.models import Sequential\n",
Should be feature complete. Onto debugging and extracting data.
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"from keras.layers import Dense\n",
"from os.path import join"
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]
},
{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
"# Function definitions"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
Should be feature complete. Onto debugging and extracting data.
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"outputs": [],
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"source": [
Cleaning up code.
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"# Used to format our input binary state.\n",
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"\n",
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"def format_input(acc, elem):\n",
" hex_elem = (elem - (elem >> 4 << 4))\n",
" for x in range(16):\n",
" if x == hex_elem:\n",
" acc.append(1)\n",
" else:\n",
" acc.append(0)\n",
" hex_elem = (elem >> 4) % 16\n",
" for x in range(16):\n",
" if x == hex_elem:\n",
" acc.append(1)\n",
" else:\n",
" acc.append(0)\n",
" return acc\n",
"\n",
"# Calculate Manhattan distance between two points.\n",
"\n",
"def man_dist(x, y):\n",
" for a, b in zip(x, y):\n",
" a_one, a_two = x\n",
" b_one, b_two = y\n",
" \n",
" return (abs(a_one - b_one) + abs(a_two - b_two))\n",
" \n",
"# Calculate Manhattan distance between each set of two points in a list.\n",
" \n",
"def man_dist_state(x, y):\n",
" return sum(man_dist(a, b) for a, b in zip(x, y))\n",
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"\n",
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"# Used to format the positions we parsed from our binary input.\n",
"\n",
"def format_pos(acc, elem):\n",
" hex_elem = (elem[1] - (elem[1] >> 4 << 4))\n",
" if hex_elem == 0:\n",
" acc.append((hex_elem, (3,3)))\n",
" else:\n",
" acc.append((hex_elem, ((15 - ((elem[0]) * 2)) % 4,int((15 - ((elem[0]) * 2)) / 4))))\n",
" hex_elem = (elem[1] >> 4) % 16\n",
" if hex_elem == 0:\n",
" acc.append((hex_elem, (3,3)))\n",
" else:\n",
" acc.append((hex_elem, ((15 - ((elem[0]) * 2 + 1)) % 4,int((15 - ((elem[0]) * 2 + 1)) / 4))))\n",
" \n",
" return acc\n",
"\n",
"# The title of this function is slightly misleading.\n",
"# I'm simply generating a list of positions that each\n",
"# puzzle piece in the current parsed state SHOULD be at.\n",
"# I organize this in order of the pieces as they were\n",
"# parsed so the two lists line up perfectly.\n",
"\n",
"def generate_pos(acc, elem):\n",
" if(elem[0] == 0):\n",
" acc.append((3,3))\n",
" else:\n",
" acc.append((((elem[0] - 1) % 4), (int((elem[0] - 1)/4))))\n",
" \n",
" return acc\n",
"\n",
"# Used to format our ending Manhattan distance into a format\n",
"# that can be compared with our 29 output neurons.\n",
"\n",
"def format_man_dist(elem):\n",
" acc = []\n",
" for x in range(28, -1, -1):\n",
" if x == elem:\n",
" acc.append(1)\n",
" else:\n",
" acc.append(0)\n",
" return acc\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Parsing input"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Creating Target data\n",
"\n",
"For this cell, I wanted to clearly explain how the script past this point works. Since I don't want to parse all of the binary states from each file that I'm going to use all at once and hold them in RAM, this instead parses 1 binary state at a time (8 bytes, meaning 64 bits, and we ignore the 1st 4 bits), does the calculations and input formatting needed, and appends the end result per state to a list to be used later."
Initial commit.
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]
},
{
"cell_type": "code",
Cleaning up code.
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"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"for i in range(29):\n",
" filename = join('data', str(i) + '.bin')\n",
" \n",
" # Debugging to print the current file from which states are being parsed.\n",
" #print(i)\n",
" \n",
" with open(filename, 'rb') as f:\n",
" data = f.read(8)\n",
" counter = 0\n",
" target = []\n",
"\n",
" while(data and counter < 1000):\n",
" target.append(format_man_dist(i))\n",
" \n",
" data = f.read(8)\n",
" counter += 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Parsing and Training the neural network"
]
},
{
"cell_type": "markdown",
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"metadata": {},
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"source": [
"### Setting up the neural network\n",
"\n",
"Before we run our training/parsing, we need to make sure Keras has configured the neural network properly to our specifications."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
Should be feature complete. Onto debugging and extracting data.
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"outputs": [],
Initial commit.
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"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",
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"\n",
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"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",
Cleaning up code.
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" loss='mean_absolute_percentage_error',\n",
Should be feature complete. Onto debugging and extracting data.
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" metrics=['accuracy'])"
]
},
Cleaning up code.
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Training the neural network\n",
"\n",
"Again, this part has the parsing and training in hand, since as we parse and format the states from our file input, we feed those batches to our neural network to train with."
]
},
Should be feature complete. Onto debugging and extracting data.
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{
"cell_type": "code",
Cleaning up code.
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"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"1/1 [==============================] - 0s 490ms/step - loss: 500344896.0000 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1/1 [==============================] - 0s 2ms/step - loss: 1.2887 - acc: 1.0000\n",
"Epoch 3/5\n",
"1/1 [==============================] - 0s 2ms/step - loss: 0.6327 - acc: 1.0000\n",
"Epoch 4/5\n",
"1/1 [==============================] - 0s 2ms/step - loss: 0.3702 - acc: 1.0000\n",
"Epoch 5/5\n",
"1/1 [==============================] - 0s 2ms/step - loss: 0.2573 - acc: 1.0000\n",
"Epoch 1/5\n",
"2/2 [==============================] - 0s 1ms/step - loss: 32517106.0000 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"2/2 [==============================] - 0s 721us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"2/2 [==============================] - 0s 738us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"2/2 [==============================] - 0s 783us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"2/2 [==============================] - 0s 1ms/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"4/4 [==============================] - 0s 547us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"4/4 [==============================] - 0s 550us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"4/4 [==============================] - 0s 480us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"4/4 [==============================] - 0s 451us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"4/4 [==============================] - 0s 367us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"10/10 [==============================] - 0s 155us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"10/10 [==============================] - 0s 197us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"10/10 [==============================] - 0s 157us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"10/10 [==============================] - 0s 167us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
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"Epoch 1/5\n",
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"Epoch 2/5\n",
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"Epoch 3/5\n",
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"Epoch 4/5\n",
"24/24 [==============================] - 0s 85us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"24/24 [==============================] - 0s 85us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"54/54 [==============================] - 0s 48us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"54/54 [==============================] - 0s 67us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"54/54 [==============================] - 0s 47us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"54/54 [==============================] - 0s 46us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"54/54 [==============================] - 0s 37us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"107/107 [==============================] - 0s 37us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"107/107 [==============================] - 0s 41us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"107/107 [==============================] - 0s 60us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"107/107 [==============================] - 0s 37us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"107/107 [==============================] - 0s 55us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"222/222 [==============================] - 0s 26us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"222/222 [==============================] - 0s 25us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
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"Epoch 4/5\n",
"222/222 [==============================] - 0s 34us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"222/222 [==============================] - 0s 26us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"496/496 [==============================] - 0s 26us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"496/496 [==============================] - 0s 24us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"496/496 [==============================] - 0s 21us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"496/496 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
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"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 15us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
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"Epoch 3/5\n",
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"Epoch 4/5\n",
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"Epoch 5/5\n",
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"Epoch 1/5\n",
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"Epoch 2/5\n",
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"Epoch 3/5\n",
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"Epoch 4/5\n",
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"Epoch 5/5\n",
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"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 13us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 15us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 15us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 13us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 15us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 15us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 20us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 20us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 21us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 20us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 23us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 14us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 15us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 22us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 15us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 25us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 21us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 23us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 14us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 15us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 15us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 14us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 21us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 15us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 14us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 20us/step - loss: 3.4483 - acc: 1.0000\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 20us/step - loss: 3.4483 - acc: 1.0000\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 1.0000\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 1.0000\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 1.0000\n"
]
}
],
"source": [
"for i in range(29):\n",
" filename = join('data', str(i) + '.bin')\n",
" \n",
" # Debugging to print the current file from which states are being parsed.\n",
" #print(i)\n",
" \n",
" with open(filename, 'rb') as f:\n",
" data = f.read(8)\n",
" counter = 0\n",
" \n",
" temp = []\n",
" target = []\n",
"\n",
" while(data and counter < 1000):\n",
" bin_data = reduce(format_input, list(data), [])\n",
" bin_data.reverse()\n",
" bin_data = bin_data[16:]\n",
"\n",
" pos_data = reduce(format_pos, enumerate(list(data)), [])\n",
" pos_data.reverse()\n",
"\n",
" state_pos = []\n",
"\n",
" for p in pos_data:\n",
" state_pos.append(p[1])\n",
"\n",
" target_pos = reduce(generate_pos, pos_data, [])\n",
"\n",
" #for i in range(int(len(bin_data)/256)):\n",
" # for x in range((i*16) + 1,(i*16) + 16):\n",
" # temp = []\n",
" # for y in range(16):\n",
" # temp.append(bin_data[(x*16) + y])\n",
" # print(temp)\n",
"\n",
" target.append(format_man_dist(i))\n",
"\n",
" temp.append(bin_data)\n",
"\n",
" data = f.read(8)\n",
" counter += 1\n",
"\n",
" # Train the network.\n",
"\n",
" model.fit(np.array(temp), np.array(target), epochs=5, batch_size=100)\n",
" #model.train_on_batch(np.array(temp), np.array(target))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Testing the neural network"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Creating the testing target data\n",
"\n",
"Similarly, here is a separate cell that just parses and creates the necessary target and testing arrays that we will need to test the neural network using Keras."
]
},
{
"cell_type": "code",
"execution_count": 21,
Should be feature complete. Onto debugging and extracting data.
2017-12-02 11:08:16 -06:00
"metadata": {},
"outputs": [],
Cleaning up code.
2017-12-02 14:31:27 -06:00
"source": [
"# Used for testing data\n",
"\n",
"with open('data/18.bin', 'rb') as f:\n",
" \n",
" for i in range(1000):\n",
" data = f.read(8)\n",
" \n",
" data = f.read(8)\n",
" \n",
" counter = 0\n",
" \n",
" temp = []\n",
" \n",
" target = []\n",
" \n",
" while(data):\n",
" bin_data = reduce(format_input, list(data), [])\n",
" bin_data.reverse()\n",
" bin_data = bin_data[16:]\n",
"\n",
" pos_data = reduce(format_pos, enumerate(list(data)), [])\n",
" pos_data.reverse()\n",
" pos_data = pos_data[16:]\n",
"\n",
" target_pos = reduce(generate_pos, pos_data, [])\n",
"\n",
" target.append(format_man_dist(target_pos))\n",
"\n",
" temp.append(bin_data)\n",
" \n",
" counter += 1\n",
" data = f.read(8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluating our test data"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"446342/446342 [==============================] - 1s 2us/step\n"
]
}
],
"source": [
"# Evaluate accuracy\n",
" \n",
"loss_and_metrics = model.evaluate(np.array(temp),np.array(target), batch_size=1000)\n",
" \n",
"# Generating predictions:\n",
" \n",
"predictions = model.predict(np.array(temp), batch_size=1000)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0\n",
"[0.0, 1.0]\n",
"['loss', 'acc']\n"
]
}
],
"source": [
"output = []\n",
"\n",
"for p in predictions:\n",
" output.append(np.argmax(p))\n",
" \n",
"print(np.array(output).mean())\n",
"\n",
"print(loss_and_metrics)\n",
"\n",
"print(model.metrics_names)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n"
]
}
],
Should be feature complete. Onto debugging and extracting data.
2017-12-02 11:08:16 -06:00
"source": [
Initial commit.
2017-12-01 14:27:20 -06:00
"\n",
"\n",
Should be feature complete. Onto debugging and extracting data.
2017-12-02 11:08:16 -06:00
"with open('data/0.bin', 'rb') as f:\n",
" data = f.read(8)\n",
" counter = 0\n",
Initial commit.
2017-12-01 14:27:20 -06:00
"\n",
Should be feature complete. Onto debugging and extracting data.
2017-12-02 11:08:16 -06:00
" while(data):\n",
" bin_data = reduce(format_input, list(data), [])\n",
" bin_data.reverse()\n",
" bin_data = bin_data[16:]\n",
Initial commit.
2017-12-01 14:27:20 -06:00
"\n",
Should be feature complete. Onto debugging and extracting data.
2017-12-02 11:08:16 -06:00
" print(counter)\n",
" \n",
" for i in range(int(len(bin_data)/240)):\n",
" for x in range((i*16),(i*16) + 15):\n",
" temp = []\n",
" for y in range(16):\n",
" temp.append(bin_data[(x*16) + y])\n",
" \n",
" data = f.read(8)\n",
" counter += 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"I'm going to need to calculate Manhattan Distances for each of the states at some point.\n",
"\n",
"This website might be helpful for that formula:\n",
"https://heuristicswiki.wikispaces.com/Manhattan+Distance"
Removed comment code, cleaned up notebook.
2017-12-01 14:31:39 -06:00
]
},
{
"cell_type": "code",
Cleaning up code.
2017-12-02 14:31:27 -06:00
"execution_count": 14,
Removed comment code, cleaned up notebook.
2017-12-01 14:31:39 -06:00
"metadata": {},
Cleaning up code.
2017-12-02 14:31:27 -06:00
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]\n"
]
}
],
Removed comment code, cleaned up notebook.
2017-12-01 14:31:39 -06:00
"source": [
Should be feature complete. Onto debugging and extracting data.
2017-12-02 11:08:16 -06:00
"\n",
Removed comment code, cleaned up notebook.
2017-12-01 14:31:39 -06:00
"with open('data/0.bin', 'rb') as f:\n",
Should be feature complete. Onto debugging and extracting data.
2017-12-02 11:08:16 -06:00
" data = f.read(8)\n",
" counter = 0\n",
" \n",
" while(data):\n",
" pos_data = reduce(format_pos, enumerate(list(data)), [])\n",
" pos_data.reverse()\n",
" pos_data = pos_data[1:]\n",
" \n",
" state_pos = []\n",
" \n",
" for p in pos_data:\n",
" state_pos.append(p[1])\n",
" \n",
" target_pos = reduce(generate_pos, pos_data, [])\n",
" \n",
" print(format_man_dist(man_dist_state(state_pos, target_pos)))\n",
" \n",
" data = f.read(8)\n",
" counter += 1"
]
},
{
"cell_type": "code",
Cleaning up code.
2017-12-02 14:31:27 -06:00
"execution_count": 15,
Should be feature complete. Onto debugging and extracting data.
2017-12-02 11:08:16 -06:00
"metadata": {},
Cleaning up code.
2017-12-02 14:31:27 -06:00
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"Epoch 1/5\n",
"1/1 [==============================] - 0s 2ms/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1/1 [==============================] - 0s 2ms/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1/1 [==============================] - 0s 2ms/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1/1 [==============================] - 0s 2ms/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1/1 [==============================] - 0s 2ms/step - loss: 3.4483 - acc: 0.0000e+00\n",
"1\n",
"Epoch 1/5\n",
"2/2 [==============================] - 0s 987us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"2/2 [==============================] - 0s 924us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"2/2 [==============================] - 0s 951us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"2/2 [==============================] - 0s 927us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"2/2 [==============================] - 0s 884us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"2\n",
"Epoch 1/5\n",
"4/4 [==============================] - 0s 538us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"4/4 [==============================] - 0s 639us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"4/4 [==============================] - 0s 776us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"4/4 [==============================] - 0s 635us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"4/4 [==============================] - 0s 812us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"3\n",
"Epoch 1/5\n",
"10/10 [==============================] - 0s 207us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"10/10 [==============================] - 0s 211us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"10/10 [==============================] - 0s 232us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"10/10 [==============================] - 0s 259us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"10/10 [==============================] - 0s 188us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"4\n",
"Epoch 1/5\n",
"24/24 [==============================] - 0s 100us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"24/24 [==============================] - 0s 90us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"24/24 [==============================] - 0s 111us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"24/24 [==============================] - 0s 106us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"24/24 [==============================] - 0s 97us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"5\n",
"Epoch 1/5\n",
"54/54 [==============================] - 0s 44us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"54/54 [==============================] - 0s 52us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"54/54 [==============================] - 0s 33us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"54/54 [==============================] - 0s 41us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"54/54 [==============================] - 0s 50us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"6\n",
"Epoch 1/5\n",
"107/107 [==============================] - 0s 53us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"107/107 [==============================] - 0s 45us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"107/107 [==============================] - 0s 42us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"107/107 [==============================] - 0s 39us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"107/107 [==============================] - 0s 42us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"7\n",
"Epoch 1/5\n",
"222/222 [==============================] - 0s 31us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"222/222 [==============================] - 0s 32us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"222/222 [==============================] - 0s 34us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"222/222 [==============================] - 0s 35us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"222/222 [==============================] - 0s 32us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"8\n",
"Epoch 1/5\n",
"496/496 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"496/496 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"496/496 [==============================] - 0s 23us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"496/496 [==============================] - 0s 22us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"496/496 [==============================] - 0s 23us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"9\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 21us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"10\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 15us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 20us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"11\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 15us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 15us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"12\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 15us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"13\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 24us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"14\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 14us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 14us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"15\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 21us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"16\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1000/1000 [==============================] - 0s 20us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"17\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 20us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 21us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"18\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 23us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 20us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 22us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 20us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"19\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 21us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"20\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 15us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 15us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"21\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 23us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"22\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"23\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 15us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 16us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 20us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"24\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 20us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 22us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 23us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 23us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"25\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 20us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 24us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 21us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 22us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"26\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 20us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 21us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 25us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 21us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 22us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"27\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 18us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 21us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 21us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - ETA: 0s - loss: 3.4483 - acc: 0.0000e+0 - 0s 19us/step - loss: 3.4483 - acc: 0.0000e+00\n",
"28\n",
"Epoch 1/5\n",
"1000/1000 [==============================] - 0s 17us/step - loss: 3.4483 - acc: 1.0000\n",
"Epoch 2/5\n",
"1000/1000 [==============================] - 0s 22us/step - loss: 3.4483 - acc: 1.0000\n",
"Epoch 3/5\n",
"1000/1000 [==============================] - 0s 19us/step - loss: 3.4483 - acc: 1.0000\n",
"Epoch 4/5\n",
"1000/1000 [==============================] - 0s 22us/step - loss: 3.4483 - acc: 1.0000\n",
"Epoch 5/5\n",
"1000/1000 [==============================] - 0s 20us/step - loss: 3.4483 - acc: 1.0000\n"
]
}
],
Should be feature complete. Onto debugging and extracting data.
2017-12-02 11:08:16 -06:00
"source": [
"for i in range(29):\n",
" filename = join('data', str(i) + '.bin')\n",
" \n",
" print(i)\n",
" \n",
" with open(filename, 'rb') as f:\n",
" data = f.read(8)\n",
" counter = 0\n",
Cleaning up code.
2017-12-02 14:31:27 -06:00
" \n",
" temp = []\n",
" target = []\n",
Should be feature complete. Onto debugging and extracting data.
2017-12-02 11:08:16 -06:00
"\n",
" while(data and counter < 1000):\n",
" bin_data = reduce(format_input, list(data), [])\n",
" bin_data.reverse()\n",
" bin_data = bin_data[16:]\n",
"\n",
" pos_data = reduce(format_pos, enumerate(list(data)), [])\n",
" pos_data.reverse()\n",
"\n",
" state_pos = []\n",
"\n",
" for p in pos_data:\n",
" state_pos.append(p[1])\n",
"\n",
" target_pos = reduce(generate_pos, pos_data, [])\n",
"\n",
" #for i in range(int(len(bin_data)/256)):\n",
" # for x in range((i*16) + 1,(i*16) + 16):\n",
" # temp = []\n",
" # for y in range(16):\n",
" # temp.append(bin_data[(x*16) + y])\n",
" # print(temp)\n",
"\n",
Cleaning up code.
2017-12-02 14:31:27 -06:00
" target.append(format_man_dist(i))\n",
Should be feature complete. Onto debugging and extracting data.
2017-12-02 11:08:16 -06:00
"\n",
" temp.append(bin_data)\n",
"\n",
Cleaning up code.
2017-12-02 14:31:27 -06:00
" data = f.read(8)\n",
" counter += 1\n",
Should be feature complete. Onto debugging and extracting data.
2017-12-02 11:08:16 -06:00
"\n",
Cleaning up code.
2017-12-02 14:31:27 -06:00
" # Train the network.\n",
Should be feature complete. Onto debugging and extracting data.
2017-12-02 11:08:16 -06:00
"\n",
Cleaning up code.
2017-12-02 14:31:27 -06:00
" model.fit(np.array(temp), np.array(target), epochs=5, batch_size=100)\n",
" #model.train_on_batch(np.array(temp), np.array(target))"
Should be feature complete. Onto debugging and extracting data.
2017-12-02 11:08:16 -06:00
]
},
{
"cell_type": "code",
Cleaning up code.
2017-12-02 14:31:27 -06:00
"execution_count": 16,
"metadata": {
"collapsed": true
},
Should be feature complete. Onto debugging and extracting data.
2017-12-02 11:08:16 -06:00
"outputs": [],
"source": [
Cleaning up code.
2017-12-02 14:31:27 -06:00
"# Used for testing data\n",
"\n",
"with open('data/18.bin', 'rb') as f:\n",
" \n",
" for i in range(300):\n",
" data = f.read(8)\n",
" \n",
Should be feature complete. Onto debugging and extracting data.
2017-12-02 11:08:16 -06:00
" data = f.read(8)\n",
Cleaning up code.
2017-12-02 14:31:27 -06:00
" \n",
Should be feature complete. Onto debugging and extracting data.
2017-12-02 11:08:16 -06:00
" counter = 0\n",
Cleaning up code.
2017-12-02 14:31:27 -06:00
" \n",
" temp = []\n",
" \n",
" target = []\n",
Should be feature complete. Onto debugging and extracting data.
2017-12-02 11:08:16 -06:00
" \n",
" while(data):\n",
" bin_data = reduce(format_input, list(data), [])\n",
" bin_data.reverse()\n",
" bin_data = bin_data[16:]\n",
Cleaning up code.
2017-12-02 14:31:27 -06:00
"\n",
" pos_data = reduce(format_pos, enumerate(list(data)), [])\n",
" pos_data.reverse()\n",
" pos_data = pos_data[16:]\n",
"\n",
" target_pos = reduce(generate_pos, pos_data, [])\n",
"\n",
" #for i in range(int(len(bin_data)/256)):\n",
" # for x in range((i*16) + 1,(i*16) + 16):\n",
" # temp = []\n",
" # for y in range(16):\n",
" # temp.append(bin_data[(x*16) + y])\n",
" # print(temp)\n",
"\n",
" target.append(format_man_dist(target_pos))\n",
"\n",
Should be feature complete. Onto debugging and extracting data.
2017-12-02 11:08:16 -06:00
" temp.append(bin_data)\n",
Cleaning up code.
2017-12-02 14:31:27 -06:00
" \n",
" counter += 1\n",
" data = f.read(8)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"447042/447042 [==============================] - 1s 2us/step\n"
]
}
],
"source": [
"# Evaluate accuracy\n",
" \n",
"loss_and_metrics = model.evaluate(np.array(temp),np.array(target), batch_size=1000)\n",
" \n",
"# Generating predictions:\n",
" \n",
"predictions = model.predict(np.array(temp), batch_size=1000)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0\n",
"[0.0, 1.0]\n",
"['loss', 'acc']\n"
]
}
],
"source": [
"output = []\n",
"\n",
"for p in predictions:\n",
" output.append(np.argmax(p))\n",
" \n",
"print(np.array(output).mean())\n",
Should be feature complete. Onto debugging and extracting data.
2017-12-02 11:08:16 -06:00
"\n",
Cleaning up code.
2017-12-02 14:31:27 -06:00
"print(loss_and_metrics)\n",
Initial commit.
2017-12-01 14:27:20 -06:00
"\n",
Cleaning up code.
2017-12-02 14:31:27 -06:00
"print(model.metrics_names)"
Initial commit.
2017-12-01 14:27:20 -06:00
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Reference in a new issue Copy permalink