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‎Run_Simulation.py

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# coding: utf-8
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# In[4]:
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import os
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os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
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import base64
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from datetime import datetime
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import os
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import shutil
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import numpy as np
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import socketio
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import eventlet
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import eventlet.wsgi
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from PIL import Image
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from flask import Flask
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from io import BytesIO
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from keras.models import load_model
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import utils
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# In[5]:
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model = load_model('./model.h5')
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# In[6]:
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sio = socketio.Server()
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app = Flask(__name__)
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MAX_SPEED = 25
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MIN_SPEED = 10
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speed_limit = MAX_SPEED
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# In[7]:
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def send_control(steering_angle, throttle):
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sio.emit(
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"steer",
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data={
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'steering_angle': steering_angle.__str__(),
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'throttle': throttle.__str__()
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},
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skip_sid=True)
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# In[8]:
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@sio.on('connect')
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def connect(sid, environ):
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print("connect ", sid)
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send_control(0, 0)
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@sio.on('telemetry')
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def telemetry(sid, data):
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if data:
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steering_angle = float(data["steering_angle"])
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throttle = float(data["throttle"])
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speed = float(data["speed"])
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#print (steering_angle, throttle, speed)
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image = Image.open(BytesIO(base64.b64decode(data["image"])))
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try:
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image = np.asarray(image)
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image = utils.process(image)
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image = image/255.0
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image = np.array([image])
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steering_angle = float(model.predict(image, batch_size=1))
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global speed_limit
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if speed > speed_limit:
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speed_limit = MIN_SPEED # slow down
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else:
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speed_limit = MAX_SPEED
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throttle = 1.0 - ( (steering_angle)**2 ) - ( (speed/speed_limit)**2 )
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#throttle = 1.0
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print('{} {} {}'.format(steering_angle, throttle, speed))
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send_control(steering_angle, throttle)
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except Exception as e:
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print(e)
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else:
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sio.emit('manual', data={}, skip_sid=True)
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# In[9]:
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app = socketio.Middleware(sio, app)
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eventlet.wsgi.server(eventlet.listen(('0.0.0.0', 4567)), app)
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‎model.h5

2.93 MB
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‎model.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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"c:\\users\\suran\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
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" from ._conv import register_converters as _register_converters\n"
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]
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}
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],
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"source": [
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"import os\n",
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"os.environ['CUDA_VISIBLE_DEVICES'] = ''\n",
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"import numpy as np\n",
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"from sklearn.model_selection import train_test_split\n",
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"from keras.models import Sequential, save_model\n",
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"from keras.optimizers import Adam\n",
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"from keras.callbacks import ModelCheckpoint\n",
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"from keras.layers import Conv2D, MaxPooling2D, Dropout, Dense, Flatten\n",
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"from utils import INPUT_SHAPE, generate_dataset"
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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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"source": [
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"data_X, data_Y = generate_dataset()"
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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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"source": [
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"X_train, X_test, Y_train, Y_test = train_test_split(data_X, data_Y, test_size = 0.2 )"
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_train = X_train/255.0\n",
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"X_test = X_test/255.0"
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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": 5,
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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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"(6251, 66, 200, 3) (1563, 66, 200, 3)\n",
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"(6251,) (1563,)\n"
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]
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}
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],
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"source": [
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"print (X_train.shape, X_test.shape)\n",
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"print (Y_train.shape, Y_test.shape)"
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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": 6,
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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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"WARNING:tensorflow:From c:\\users\\suran\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:968: calling reduce_prod (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"keep_dims is deprecated, use keepdims instead\n",
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"____________________________________________________________________________________________________\n",
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"Layer (type) Output Shape Param # Connected to \n",
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"====================================================================================================\n",
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"convolution2d_1 (Convolution2D) (None, 31, 98, 24) 1824 convolution2d_input_1[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"convolution2d_2 (Convolution2D) (None, 14, 47, 36) 21636 convolution2d_1[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"convolution2d_3 (Convolution2D) (None, 5, 22, 48) 43248 convolution2d_2[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"convolution2d_4 (Convolution2D) (None, 3, 20, 64) 27712 convolution2d_3[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"convolution2d_5 (Convolution2D) (None, 1, 18, 64) 36928 convolution2d_4[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"dropout_1 (Dropout) (None, 1, 18, 64) 0 convolution2d_5[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"flatten_1 (Flatten) (None, 1152) 0 dropout_1[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"dense_1 (Dense) (None, 100) 115300 flatten_1[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"dense_2 (Dense) (None, 50) 5050 dense_1[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"dense_3 (Dense) (None, 10) 510 dense_2[0][0] \n",
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"____________________________________________________________________________________________________\n",
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"dense_4 (Dense) (None, 1) 11 dense_3[0][0] \n",
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"====================================================================================================\n",
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"Total params: 252,219\n",
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"Trainable params: 252,219\n",
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"Non-trainable params: 0\n",
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"____________________________________________________________________________________________________\n"
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]
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}
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],
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"source": [
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"model = Sequential()\n",
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"model.add(Conv2D(24, 5, 5, activation='relu',input_shape = INPUT_SHAPE, subsample=(2, 2)))\n",
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"model.add(Conv2D(36, 5, 5, activation='relu', subsample=(2, 2)))\n",
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"model.add(Conv2D(48, 5, 5, activation='relu', subsample=(2, 2)))\n",
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"model.add(Conv2D(64, 3, 3, activation='relu'))\n",
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"model.add(Conv2D(64, 3, 3, activation='relu'))\n",
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"model.add(Dropout(0.5))\n",
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"model.add(Flatten())\n",
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"model.add(Dense(100, activation='relu'))\n",
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"model.add(Dense(50, activation='relu'))\n",
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"model.add(Dense(10, activation='relu'))\n",
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"model.add(Dense(1))\n",
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"model.summary()"
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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": 7,
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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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"WARNING:tensorflow:From c:\\users\\suran\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:996: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"keep_dims is deprecated, use keepdims instead\n"
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]
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}
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],
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"source": [
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"checkpoint = ModelCheckpoint('model-{val_loss:.4f}.h5',\n",
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" monitor='val_loss',\n",
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" verbose=0,\n",
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" save_best_only=True,\n",
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" mode='auto')\n",
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"model.compile(loss='mean_squared_error', optimizer=Adam(lr=1.0e-4))"
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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": 9,
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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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"Train on 6251 samples, validate on 1563 samples\n",
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"Epoch 1/30\n",
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"6251/6251 [==============================] - 22s - loss: 0.0240 - val_loss: 0.0270\n",
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"Epoch 2/30\n",
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"6251/6251 [==============================] - 21s - loss: 0.0235 - val_loss: 0.0259\n",
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"Epoch 3/30\n",
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"6251/6251 [==============================] - 21s - loss: 0.0221 - val_loss: 0.0252\n",
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"Epoch 4/30\n",
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"6251/6251 [==============================] - 22s - loss: 0.0213 - val_loss: 0.0229\n",
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"Epoch 5/30\n",
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"6251/6251 [==============================] - 22s - loss: 0.0206 - val_loss: 0.0223\n",
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"Epoch 6/30\n",
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"6251/6251 [==============================] - 21s - loss: 0.0201 - val_loss: 0.0236\n",
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"Epoch 7/30\n",
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"6251/6251 [==============================] - 21s - loss: 0.0200 - val_loss: 0.0230\n",
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"Epoch 8/30\n",
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"6251/6251 [==============================] - 22s - loss: 0.0197 - val_loss: 0.0226\n",
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"Epoch 9/30\n",
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"6251/6251 [==============================] - 23s - loss: 0.0196 - val_loss: 0.0224\n",
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"Epoch 10/30\n",
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"6251/6251 [==============================] - 23s - loss: 0.0194 - val_loss: 0.0224\n",
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"Epoch 11/30\n",
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"6251/6251 [==============================] - 21s - loss: 0.0193 - val_loss: 0.0220\n",
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"Epoch 12/30\n",
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"6251/6251 [==============================] - 25s - loss: 0.0191 - val_loss: 0.0216\n",
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"Epoch 13/30\n",
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"6251/6251 [==============================] - 21s - loss: 0.0191 - val_loss: 0.0219\n",
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"Epoch 14/30\n",
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"6251/6251 [==============================] - 25s - loss: 0.0189 - val_loss: 0.0217\n",
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"Epoch 15/30\n",
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"6251/6251 [==============================] - 21s - loss: 0.0187 - val_loss: 0.0212\n",
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"Epoch 16/30\n",
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"6251/6251 [==============================] - 24s - loss: 0.0186 - val_loss: 0.0217\n",
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"Epoch 17/30\n",
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"6251/6251 [==============================] - 24s - loss: 0.0184 - val_loss: 0.0215\n",
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"Epoch 18/30\n",
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"6251/6251 [==============================] - 25s - loss: 0.0183 - val_loss: 0.0215\n",
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"Epoch 19/30\n",
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"6251/6251 [==============================] - 21s - loss: 0.0182 - val_loss: 0.0211\n",
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"Epoch 20/30\n",
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"6251/6251 [==============================] - 23s - loss: 0.0178 - val_loss: 0.0208\n",
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"Epoch 21/30\n",
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"6251/6251 [==============================] - 24s - loss: 0.0175 - val_loss: 0.0210\n",
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"Epoch 22/30\n",
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"6251/6251 [==============================] - 22s - loss: 0.0177 - val_loss: 0.0208\n",
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"Epoch 23/30\n",
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"6251/6251 [==============================] - 22s - loss: 0.0175 - val_loss: 0.0211\n",
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"Epoch 24/30\n",
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"6251/6251 [==============================] - 26s - loss: 0.0173 - val_loss: 0.0213\n",
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"Epoch 25/30\n",
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"6251/6251 [==============================] - 24s - loss: 0.0173 - val_loss: 0.0209\n",
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"Epoch 26/30\n",
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"6251/6251 [==============================] - 23s - loss: 0.0170 - val_loss: 0.0206\n",
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"Epoch 27/30\n",
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"6251/6251 [==============================] - 22s - loss: 0.0168 - val_loss: 0.0209\n",
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"Epoch 28/30\n",
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"6251/6251 [==============================] - 23s - loss: 0.0166 - val_loss: 0.0219\n",
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"Epoch 29/30\n",
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"6251/6251 [==============================] - 24s - loss: 0.0166 - val_loss: 0.0207\n",
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"Epoch 30/30\n",
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"6251/6251 [==============================] - 22s - loss: 0.0163 - val_loss: 0.0207\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 0x18455984748>"
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]
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},
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"execution_count": 9,
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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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"model.fit( X_train, Y_train, batch_size=32, nb_epoch=30, validation_data=(X_test, Y_test),callbacks=[checkpoint], shuffle=True )"
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]
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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": "3.6.2"
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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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