171 lines
4.3 KiB
Text
171 lines
4.3 KiB
Text
{
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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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"# 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"
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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.\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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"# Replace this with parser code later."
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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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"\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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"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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"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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"# 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)"
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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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"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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"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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