Removed comment code, cleaned up notebook.
This commit is contained in:
Alex Huddleston
parent
f825ec3c9d
commit
2ba5366c4c
1 changed files with 26 additions and 70 deletions
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@ -14,23 +14,10 @@
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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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"from keras.layers import Dense"
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]
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},
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{
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@ -61,14 +48,12 @@
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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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"# Generating dummy data.\n",
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"\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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"# Replace this with parser code later."
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]
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},
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{
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@ -108,77 +93,48 @@
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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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"\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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"# Train the network.\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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"# predictions = model.predict(testing_data, batch_size=10)"
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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": 1,
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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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"0xf23456789abcdef0\n"
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]
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}
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],
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"source": [
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"with open('data/0.bin', 'rb') as f:\n",
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" data = f.read()\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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"print(hex(int.from_bytes(data, byteorder='little', signed=False)))"
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]
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},
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{
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