Initial commit.
This commit is contained in:
Alex Huddleston
commit
f825ec3c9d
5 changed files with 227 additions and 0 deletions
1
.gitignore
vendored
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.gitignore
vendored
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data/
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BIN
420Project17fall.docx
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420Project17fall.docx
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420Project17fall.pdf
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420Project17fall.pdf
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README.md
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README.md
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See 420Project17fall.pdf
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Use this script to download the data files, because I'm not hosting them here.
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~~~~
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mkdir "data"
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for i in {0..28}
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do
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wget "http://courses.cse.tamu.edu/daugher/misc/PPP/homeworks/data/${i}states.bin" -O "data/${i}.bin"
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done
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~~~~
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215
neural_network.ipynb
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neural_network.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using TensorFlow backend.\n"
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]
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}
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],
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"source": [
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"# If you're really new to python this code might be\n",
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"# a bit unreadable, but I tried to make it as simple\n",
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"# as possible.\n",
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"\n",
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"# Setting up our imported libraries.\n",
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"import numpy as np\n",
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"from keras.models import Sequential\n",
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"from keras.layers import Dense\n",
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"\n",
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"# We're going to use numpy for some easy\n",
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"# array functionality with keras, and keras\n",
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"# is our library for handling most of our neural network\n",
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"# stuff. That being said, you need to have some sort of\n",
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"# backend for keras to work with, such as the recommended\n",
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"# TensorFlow, which I'm using here.\n",
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"# Since keras uses those as a backend, you don't inherently\n",
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"# need to import it."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"240000\n",
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"240\n",
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"[[ 0.28588903 0.05308564 0.99171479 ..., 0.92657084 0.09114427\n",
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" 0.76495161]\n",
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" [ 0.50998915 0.74032164 0.04898317 ..., 0.77742777 0.46720853\n",
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" 0.01731216]\n",
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" [ 0.31522802 0.11448062 0.40291163 ..., 0.87519373 0.31255597\n",
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" 0.7202333 ]\n",
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" ..., \n",
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" [ 0.13906598 0.99536312 0.36709839 ..., 0.68740262 0.9536678\n",
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" 0.53053495]\n",
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" [ 0.13696298 0.91392043 0.5846018 ..., 0.84365665 0.92837426\n",
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" 0.18738981]\n",
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" [ 0.05775272 0.7919279 0.51444914 ..., 0.53078037 0.67684536\n",
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" 0.25327729]]\n"
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]
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}
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],
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"source": [
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"# Generating dummy data so I can understand how to input.\n",
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"data = np.random.random((1000,240))\n",
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"output = np.random.random((1000, 29))\n",
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"\n",
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"# Here's some printouts to see exactly what I'm doing here.\n",
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"print(data.size)\n",
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"print(data[0].size)\n",
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"print(data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1/5\n",
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"1000/1000 [==============================] - 4s 4ms/step - loss: 0.0986 - acc: 0.0250\n",
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"Epoch 2/5\n",
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"1000/1000 [==============================] - 0s 155us/step - loss: 0.0936 - acc: 0.0350\n",
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"Epoch 3/5\n",
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"1000/1000 [==============================] - 0s 157us/step - loss: 0.0912 - acc: 0.0380\n",
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"Epoch 4/5\n",
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"1000/1000 [==============================] - 0s 154us/step - loss: 0.0901 - acc: 0.0370\n",
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"Epoch 5/5\n",
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"1000/1000 [==============================] - 0s 162us/step - loss: 0.0894 - acc: 0.0370\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<keras.callbacks.History at 0x7fe4fae027f0>"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Sets up a Sequential model, Sequential is all\n",
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"# that should need to be used for this project,\n",
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"# considering that it will only be dealing with\n",
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"# a linear stack of layers of neurons.\n",
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"model = Sequential()\n",
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"\n",
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"# Adding layers to the model.\n",
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"\n",
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"# Dense is the type of layer I think, don't need\n",
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"# to look into this more since this is all I should\n",
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"# need to use.\n",
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"\n",
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"# units = the number of neurons for this layer.\n",
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"\n",
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"# activation = the activation function for this layer.\n",
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"# our project doc says to use hyperbolic tangent, so\n",
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"# I set this to tanh. Except for the output layer,\n",
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"# which I set to sigmoid.\n",
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"\n",
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"# input_dim = the dimension of the input list,\n",
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"# should only be set for the input layer.\n",
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"\n",
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"model.add(Dense(units=240, activation='tanh', input_dim=240))\n",
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"model.add(Dense(units=120, activation='tanh'))\n",
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"model.add(Dense(units=29, activation='sigmoid'))\n",
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"\n",
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"# Configure the learning process.\n",
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"# \n",
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"\n",
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"# optimizer = I'm just using \n",
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"# Stomchastic gradient descent for this,\n",
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"# remember that this uses essentially staggered\n",
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"# aggregation by step to calculate gradient\n",
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"# descent towards our target, which is faster\n",
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"# than doing all of the calculation together.\n",
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"\n",
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"# loss = the loss function, currently I'm using\n",
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"# mean squared error, but might change to\n",
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"# mean_absolute_percentage_error, considering\n",
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"# I think we're supposed to calculate cost\n",
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"# based on the percentage we are away from the\n",
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"# correct number of moves away from the solved state.\n",
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"\n",
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"# metrics = evaluation metrics...\n",
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"# I think all I care about is accuracy in this case,\n",
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"# if anything.\n",
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"\n",
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"model.compile(optimizer='sgd',\n",
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" loss='mean_squared_error',\n",
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" metrics=['accuracy'])\n",
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"\n",
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"# This is where we're configuring how we train the network:\n",
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"\n",
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"# data = the input sets of training data for this network,\n",
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"# in my case I'm unsure what exactly that will be.\n",
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"\n",
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"# output = the input sets of target data for the network,\n",
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"# I believe this should just be a set of the same size\n",
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"# as the training data all containing the number\n",
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"# of steps until being solved for each state... I think.\n",
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"\n",
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"# epochs = it seems like this is how many times this\n",
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"# training should be run...\n",
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"\n",
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"# batch_size = I'm pretty sure this directly correlates\n",
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"# to how many input sets we train per step.\n",
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"\n",
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"model.fit(data, output, epochs=5, batch_size=10)\n",
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"\n",
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"# Generating predictions should look like this:\n",
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"\n",
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"# predictions = model.predict(testing_data, batch_size=10)\n",
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"\n",
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"# I've commented it out since I don't have any real\n",
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"# data to predict yet."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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