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    "# If you're really new to python this code might be\n",
    "# a bit unreadable, but I tried to make it as simple\n",
    "# as possible.\n",
    "\n",
    "# Setting up our imported libraries.\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "\n",
    "# We're going to use numpy for some easy\n",
    "# array functionality with keras, and keras\n",
    "# is our library for handling most of our neural network\n",
    "# stuff. That being said, you need to have some sort of\n",
    "# backend for keras to work with, such as the recommended\n",
    "# TensorFlow, which I'm using here.\n",
    "# Since keras uses those as a backend, you don't inherently\n",
    "# need to import it."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# Generating dummy data so I can understand how to input.\n",
    "data = np.random.random((1000,240))\n",
    "output = np.random.random((1000, 29))\n",
    "\n",
    "# Here's some printouts to see exactly what I'm doing here.\n",
    "print(data.size)\n",
    "print(data[0].size)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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",
    "model = Sequential()\n",
    "\n",
    "# Adding layers to the model.\n",
    "\n",
    "# Dense is the type of layer I think, don't need\n",
    "# to look into this more since this is all I should\n",
    "# need to use.\n",
    "\n",
    "# units = the number of neurons for this layer.\n",
    "\n",
    "# activation = the activation function for this layer.\n",
    "# our project doc says to use hyperbolic tangent, so\n",
    "# I set this to tanh. Except for the output layer,\n",
    "# which I set to sigmoid.\n",
    "\n",
    "# input_dim = the dimension of the input list,\n",
    "# should only be set for the  input layer.\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",
    "\n",
    "# optimizer = I'm just using \n",
    "# Stomchastic gradient descent for this,\n",
    "# remember that this uses essentially staggered\n",
    "# aggregation by step to calculate gradient\n",
    "# descent towards our target, which is faster\n",
    "# than doing all of the calculation together.\n",
    "\n",
    "# loss = the loss function, currently I'm using\n",
    "# mean squared error, but might change to\n",
    "# mean_absolute_percentage_error, considering\n",
    "# I think we're supposed to calculate cost\n",
    "# based on the percentage we are away from the\n",
    "# correct number of moves away from the solved state.\n",
    "\n",
    "# metrics = evaluation metrics...\n",
    "# I think all I care about is accuracy in this case,\n",
    "# if anything.\n",
    "\n",
    "model.compile(optimizer='sgd',\n",
    "             loss='mean_squared_error',\n",
    "             metrics=['accuracy'])\n",
    "\n",
    "# This is where we're configuring how we train the network:\n",
    "\n",
    "# data = the input sets of training data for this network,\n",
    "# in my case I'm unsure what exactly that will be.\n",
    "\n",
    "# output = the input sets of target data for the network,\n",
    "# I believe this should just be a set of the same size\n",
    "# as the training data all containing the number\n",
    "# of steps until being solved for each state... I think.\n",
    "\n",
    "# epochs = it seems like this is how many times this\n",
    "# training should be run...\n",
    "\n",
    "# batch_size = I'm pretty sure this directly correlates\n",
    "# to how many input sets we train per step.\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)\n",
    "\n",
    "# I've commented it out since I don't have any real\n",
    "# data to predict yet."
   ]
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   "execution_count": null,
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