Daniel Haehn PRO
Hi, I am a biomedical imaging and visualization researcher who investigates how computational methods can accelerate biological and medical research.
Please stay healthy and well!
Remote Lectures
All Meetings
Schedule here:
Functions, Classes
Arrays, Vectors
Templates
GIBBS Cluster
Cython
Run our C++ code in Python using Cython
and compare timing against NumPy
Analyze a bunch of numbers and calculate min, max, mean, stddev.
What do we have?
stats.cc
What do we need?
setup.py
statistics.pyx
#include <iostream>
#include <vector>
#include <algorithm>
#include <cassert>
#include <cmath>
template <typename T>
class Stats {
public:
T get_min(std::vector<T> v);
T get_max(std::vector<T> v);
float get_mean(std::vector<T> v);
float get_stddev(std::vector<T> v);
};
template <typename T>
T Stats<T>::get_min(std::vector<T> v) {
T minvalue = v[0];
for(int i=1; i<v.size(); i++) {
minvalue = std::min(minvalue, v[i]);
}
return minvalue;
}
template <typename T>
T Stats<T>::get_max(std::vector<T> v) {
T minvalue = v[0];
for(int i=1; i<v.size(); i++) {
minvalue = std::max(minvalue, v[i]);
}
return minvalue;
}
template <typename T>
float Stats<T>::get_mean(std::vector<T> v) {
float sum = v[0];
for(int i=1; i<v.size(); i++) {
sum += v[i];
}
sum /= v.size();
return sum;
}
template <typename T>
float Stats<T>::get_stddev(std::vector<T> v) {
float stddev = 0;
float mean = Stats<T>::get_mean(v);
for(int i=1; i<v.size(); i++) {
stddev += std::pow(v[i] - mean, 2);
}
return std::sqrt(stddev / v.size());
}
void test_get_min() {
std::vector<float> somevalues;
somevalues.push_back(1.3);
somevalues.push_back(2);
somevalues.push_back(3);
somevalues.push_back(-241);
Stats<float> stats;
assert(stats.get_min(somevalues)==-241);
std::cout << "Test OK!" << std::endl;
}
void test_get_max() {
std::vector<float> somevalues;
somevalues.push_back(1.3);
somevalues.push_back(2);
somevalues.push_back(3);
somevalues.push_back(-241);
Stats<float> stats;
assert(stats.get_max(somevalues)==3);
std::cout << "Test OK!" << std::endl;
}
void test_get_mean() {
std::vector<float> somevalues;
somevalues.push_back(1.3);
somevalues.push_back(2);
somevalues.push_back(3);
somevalues.push_back(-241);
Stats<float> stats;
float diff = std::abs(stats.get_mean(somevalues)) - std::abs(-58.675);
assert(diff < 0.0005);
std::cout << "Test OK!" << std::endl;
}
void test_get_stddev() {
std::vector<float> somevalues;
somevalues.push_back(1.3);
somevalues.push_back(2);
somevalues.push_back(3);
somevalues.push_back(-241);
Stats<float> stats;
float diff = std::abs(stats.get_stddev(somevalues)) - std::abs(105.26712152899404);
assert(diff < 0.0005);
std::cout << "Test OK!" << std::endl;
}
int main()
{
test_get_min();
test_get_max();
test_get_mean();
test_get_stddev();
}
from setuptools import setup
from Cython.Build import cythonize
setup(ext_modules=cythonize("statistics.pyx"))# distutils: language = c++
from libcpp.vector cimport vector
#
# Connection to C++
#
cdef extern from "stats.cc":
cdef cppclass Stats[T]:
T get_min(vector[T])
T get_max(vector[T])
float get_mean(vector[T])
float get_stddev(vector[T])
#
# Python Interface
#
cdef class PyStats:
cdef Stats[float] stats
def get_min(self, vector[float] v):
return self.stats.get_min(v)
def get_max(self, vector[float] v):
return self.stats.get_max(v)
def get_mean(self, vector[float] v):
return self.stats.get_mean(v)
def get_stddev(self, vector[float] v):
return self.stats.get_stddev(v)
stats.cc
statistics.pyx
setup.py
import statistics
s = statistics.PyStats()
somevalues = [1.3, 2, 3, -241]
print( s.get_min( somevalues ) )More soon...
but not today...
Lex Fridman
Artificial General Intelligence
MNIST
Lex Fridman
Supervised Learning
9
Convolutional Neural Network
cat
Convolutional Neural Network
Keras
Easier!
GIBBS Cluster
conda install keras-gpu
conda install pytorch
conda install pillow
1. Load Data
2. Setup Network
3. Train Network
4. Predict!
4 Steps
Data
Training
Testing
2
Label
?
Label
But we know the answer!
X_train
y_train
X_test
y_test
Setup Network
NUMBER_OF_CLASSES = 10
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=first_image.shape))
model.add(keras.layers.Conv2D(64, (3, 3), activation='relu'))
model.add(keras.layers.MaxPooling2D(pool_size=(2, 2)))
model.add(keras.layers.Dropout(0.25))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(128, activation='relu'))
model.add(keras.layers.Dropout(0.5))
model.add(keras.layers.Dense(NUMBER_OF_CLASSES, activation='softmax'))NUMBER_OF_CLASSES = 10
MNIST
NUMBER_OF_CLASSES = 2Cats vs. Dogs
Setup Network
NUMBER_OF_CLASSES = 10
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=first_image.shape))
model.add(keras.layers.Conv2D(64, (3, 3), activation='relu'))
model.add(keras.layers.MaxPooling2D(pool_size=(2, 2)))
model.add(keras.layers.Dropout(0.25))
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(128, activation='relu'))
model.add(keras.layers.Dropout(0.5))
model.add(keras.layers.Dense(NUMBER_OF_CLASSES, activation='softmax'))model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])Train Network
9
Training Data
Then we check how well the network predicts the testing data!
?
Loss
should go down!
Repeated.. (1 run is called an epoch)
Predict!
Testing Data
0 0 0
1 1 1
2 2 2
3 3 3
4 4 4
5 5 5
6 6 6
7 7 7
8 8 8
9 9 9
Measure how well the CNN does...
CS410 Lecture 27
By Daniel Haehn
CS410 Lecture 27
Slides for CS410 Software Engineering at UMass Boston. See https://cs410.net!
