jzuern

#!/usr/bin/env python3
# coding=utf-8

import collections
import sys
import itertools
import matplotlib.pyplot as plt
import matplotlib as mpl
import time
import datetime

jzuern

from tensorflow.python.platform import gfile
import tensorflow as tf
from tensorflow.contrib import tensorrt as trt

graph_filename ='resnetV150_frozen.pb'

f = gfile.FastGFile(graph_filename, 'rb')

# define graph def object
frozen_graph_def = tf.GraphDef()

jzuern

import tensorflow as tf
import numpy as np

def int_shape(x):

  list = x.get_shape().as_list()
  return list

def _activation_summary(x):

jzuern

class Landmark {
public:
    Landmark(float x, float y, SDL_Color id);
    ~Landmark();
    Position pos;
    SDL_Color id;
    void render(SDL_Renderer * ren);
};

jzuern

class Robot {
public:
    Robot(int x_start, int y_start, float orientation, int radius, SDL_Color col);
    ~Robot();
    
    void render(SDL_Renderer * ren);
    void move(const Uint8 * , Eigen::VectorXf & control);
    void moveForward(Eigen::VectorXf & control);
    void moveBackward(Eigen::VectorXf & control);
    void rotateLeft(Eigen::VectorXf & control);

jzuern

class KalmanFilter {
public:


    /**
    * Create a Kalman filter with the specified matrices.
    *   A - System dynamics matrix
    *   C - Output matrix
    *   Q - Process noise covariance
    *   R - Measurement noise covariance

jzuern

import numpy as np
import matplotlib.pyplot as plt
import time

L = 1.0 # length of 1-D heat-conducting object
Nx = 100 # number of spatial grid points
T = 10.0 # maximum time
Nt = 1000 # number of time steps
a = 0.005 # material proberty alpha

jzuern

def resnet_layer(inputs,
                 num_filters=16,
                 kernel_size=3,
                 strides=1,
                 activation='relu',
                 batch_normalization=True,
                 conv_first=True):
    """2D Convolution-Batch Normalization-Activation stack builder
    # Arguments
        inputs (tensor): input tensor from input image or previous layer

jzuern

import os
import pprint
import tensorflow as tf

if 'COLAB_TPU_ADDR' not in os.environ:
  print('ERROR: Not connected to a TPU runtime; please see the first cell in this notebook for instructions!')
else:
  tpu_address = 'grpc://' + os.environ['COLAB_TPU_ADDR']
  print ('TPU address is', tpu_address)

jzuern

# This address identifies the TPU we'll use when configuring TensorFlow.
TPU_WORKER = 'grpc://' + os.environ['COLAB_TPU_ADDR']
tf.logging.set_verbosity(tf.logging.INFO)

resnet_model = tf.contrib.tpu.keras_to_tpu_model(
    resnet_model,
    strategy=tf.contrib.tpu.TPUDistributionStrategy(
        tf.contrib.cluster_resolver.TPUClusterResolver(TPU_WORKER)))