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Commit 0f3c22a8 authored by kpp55's avatar kpp55
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Rearrange files and add MNIST data

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Arduino_Code/LCD_Test/LCD_Test.ino 0 → 100644
+ 68
− 0
View file @ 0f3c22a8
#include <LiquidCrystal.h>
// initialize the library by associating any needed LCD interface pin
// with the arduino pin number it is connected to
const int rs = 12, en = 11, d4 = 5, d5 = 4, d6 = 3, d7 = 2;
LiquidCrystal lcd(rs, en, d4, d5, d6, d7);
int incomingByte = 0;
void setup() {
Serial.begin(19200); //Setup the baud rate. The amount of times it samples per second.
// set up the LCD's number of columns and rows:
lcd.begin(16, 2);
// Print a message to the LCD.
lcd.print("Prediction:");
}
void loop() {
if (Serial.available() > 0)
{
// read the incoming byte:
incomingByte = Serial.read();
// say what you got:
Serial.print("I received: ");
Serial.println(incomingByte, DEC);
}
// set the cursor to column 0, line 1
// (note: line 1 is the second row, since counting begins with 0):
lcd.setCursor(0, 1);
if(incomingByte == 57)
{
lcd.print("9");
}
else if(incomingByte == 56)
{
lcd.print("8");
}
else if(incomingByte == 55)
{
lcd.print("7");
}
else if(incomingByte == 54)
{
lcd.print("6");
}
else if(incomingByte == 53)
{
lcd.print("5");
}
else if(incomingByte == 52)
{
lcd.print("4");
}
else if(incomingByte == 51)
{
lcd.print("3");
}
else if(incomingByte == 50)
{
lcd.print("2");
}
else if(incomingByte == 49)
{
lcd.print("1");
}
else if(incomingByte == 48)
{
lcd.print("0");
}
}
Arduino_Code/testing.ino 0 → 100644
+ 103
− 0
View file @ 0f3c22a8
int incomingByte = 0;
void setup() {
Serial.begin(19200);
pinMode(13,OUTPUT);
pinMode(12,OUTPUT);
pinMode(11,OUTPUT);
pinMode(10,OUTPUT);
pinMode(9,OUTPUT);
pinMode(8,OUTPUT);
pinMode(7,OUTPUT);
pinMode(6,OUTPUT);
pinMode(5,OUTPUT);
pinMode(4,OUTPUT);
}
void loop() {
// send data only when you receive data:
if (Serial.available() > 0) {
// read the incoming byte:
incomingByte = Serial.read();
// say what you got:
Serial.print("I received: ");
Serial.println(incomingByte, DEC);
}
if(incomingByte == 57)
{
digitalWrite(13,HIGH);
delay(1000);
digitalWrite(13,LOW);
delay(1000);
}
else if(incomingByte == 56)
{
digitalWrite(12,HIGH);
delay(1000);
digitalWrite(12,LOW);
delay(1000);
}
else if(incomingByte == 55)
{
digitalWrite(11,HIGH);
delay(1000);
digitalWrite(11,LOW);
delay(1000);
}
else if(incomingByte == 54)
{
digitalWrite(10,HIGH);
delay(1000);
digitalWrite(10,LOW);
delay(1000);
}
else if(incomingByte == 53)
{
digitalWrite(9,HIGH);
delay(1000);
digitalWrite(9,LOW);
delay(1000);
}
else if(incomingByte == 52)
{
digitalWrite(8,HIGH);
delay(1000);
digitalWrite(8,LOW);
delay(1000);
}
else if(incomingByte == 51)
{
digitalWrite(7,HIGH);
delay(1000);
digitalWrite(7,LOW);
delay(1000);
}
else if(incomingByte == 50)
{
digitalWrite(6,HIGH);
delay(1000);
digitalWrite(6,LOW);
delay(1000);
}
else if(incomingByte == 49)
{
digitalWrite(5,HIGH);
delay(1000);
digitalWrite(5,LOW);
delay(1000);
}
else if(incomingByte == 48)
{
digitalWrite(4,HIGH);
delay(1000);
digitalWrite(4,LOW);
delay(1000);
}
}
MNIST_Code/keras_training.py 0 → 100644
+ 104
− 0
View file @ 0f3c22a8
import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
import serial
import time
ser = serial.Serial(
port='/dev/ttyACM0',
baudrate=19200,
parity=serial.PARITY_ODD,
stopbits=serial.STOPBITS_TWO,
bytesize=serial.SEVENBITS
)
plt.rcParams['interactive'] == True
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
train_images = train_images / 255.0
test_images = test_images / 255.0
plt.figure(figsize=(10,10))
for i in range(25):
plt.subplot(5,5,i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(train_images[i], cmap=plt.cm.binary)
plt.xlabel(class_names[train_labels[i]])
plt.show()
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)), #Transforms the format of the images from a 2d-array (of 28 by 28 pixels), to a 1d-array of 28 * 28 = 784 pixels
keras.layers.Dense(128, activation=tf.nn.relu), #This is the fully connected layers
keras.layers.Dense(10, activation=tf.nn.softmax) #Output layer
])
model.compile(optimizer=tf.train.AdamOptimizer(),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5)
test_loss, test_acc = model.evaluate(test_images, test_labels)
print('Test accuracy:', test_acc)
predictions = model.predict(test_images)
np.argmax(predictions[0])
def plot_image(i, predictions_array, true_label, img):
predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
plt.imshow(img, cmap=plt.cm.binary)
predicted_label = np.argmax(predictions_array)
if predicted_label == true_label:
color = 'blue'
else:
color = 'red'
plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
100*np.max(predictions_array),
class_names[true_label]),
color=color)
def plot_value_array(i, predictions_array, true_label):
predictions_array, true_label = predictions_array[i], true_label[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
thisplot = plt.bar(range(10), predictions_array, color="#777777")
plt.ylim([0, 1])
predicted_label = np.argmax(predictions_array)
thisplot[predicted_label].set_color('red')
thisplot[true_label].set_color('blue')
i = 0
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions, test_labels)
plt.show()
num_rows = 5
num_cols = 3
num_images = num_rows*num_cols
plt.figure(figsize=(2*2*num_cols, 2*num_rows))
for i in range(num_images):
plt.subplot(num_rows, 2*num_cols, 2*i+1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(num_rows, 2*num_cols, 2*i+2)
plot_value_array(i, predictions, test_labels)
# Single image testing
img = test_images[0]
print(img.shape)
img = (np.expand_dims(img,0))
print(img.shape)
predictions_single = model.predict(img)
print(predictions_single)
print(np.argmax(predictions_single[0]))
for i in range(0,len(test_images)):
img = test_images[i]
img = (np.expand_dims(img,0))
predictions_single = model.predict(img)
predict = int(np.argmax(predictions_single[0]))
time.sleep(3)
ser.write(b'%d' % predict)
\ No newline at end of file
MNIST_Code/keras_training_mnist.py 0 → 100644
+ 103
− 0
View file @ 0f3c22a8
import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
import serial
import time
ser = serial.Serial(
port='/dev/ttyACM0',
baudrate=19200,
parity=serial.PARITY_ODD,
stopbits=serial.STOPBITS_TWO,
bytesize=serial.SEVENBITS
)
plt.rcParams['interactive'] == True
mnist = keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
class_names = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
train_images = train_images / 255.0
test_images = test_images / 255.0
plt.figure(figsize=(10,10))
for i in range(25):
plt.subplot(5,5,i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(train_images[i], cmap=plt.cm.binary)
plt.xlabel(class_names[train_labels[i]])
plt.show()
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)), #Transforms the format of the images from a 2d-array (of 28 by 28 pixels), to a 1d-array of 28 * 28 = 784 pixels
keras.layers.Dense(128, activation=tf.nn.relu), #This is the fully connected layers
keras.layers.Dense(10, activation=tf.nn.softmax) #Output layer
])
model.compile(optimizer=tf.train.AdamOptimizer(),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5)
test_loss, test_acc = model.evaluate(test_images, test_labels)
print('Test accuracy:', test_acc)
predictions = model.predict(test_images)
np.argmax(predictions[0])
def plot_image(i, predictions_array, true_label, img):
predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
plt.imshow(img, cmap=plt.cm.binary)
predicted_label = np.argmax(predictions_array)
if predicted_label == true_label:
color = 'blue'
else:
color = 'red'
plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
100*np.max(predictions_array),
class_names[true_label]),
color=color)
def plot_value_array(i, predictions_array, true_label):
predictions_array, true_label = predictions_array[i], true_label[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
thisplot = plt.bar(range(10), predictions_array, color="#777777")
plt.ylim([0, 1])
predicted_label = np.argmax(predictions_array)
thisplot[predicted_label].set_color('red')
thisplot[true_label].set_color('blue')
i = 0
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions, test_labels)
plt.show()
num_rows = 5
num_cols = 3
num_images = num_rows*num_cols
plt.figure(figsize=(2*2*num_cols, 2*num_rows))
for i in range(num_images):
plt.subplot(num_rows, 2*num_cols, 2*i+1)
plot_image(i, predictions, test_labels, test_images)
plt.subplot(num_rows, 2*num_cols, 2*i+2)
plot_value_array(i, predictions, test_labels)
# Single image testing
img = test_images[0]
print(img.shape)
img = (np.expand_dims(img,0))
print(img.shape)
predictions_single = model.predict(img)
print(predictions_single)
print(np.argmax(predictions_single[0]))
for i in range(0,len(test_images)):
img = test_images[i]
img = (np.expand_dims(img,0))
predictions_single = model.predict(img)
predict = int(np.argmax(predictions_single[0]))
time.sleep(2)
ser.write(b'%d' % predict)
README.md
+ 2
− 2
View file @ 0f3c22a8
test # Senior Design
This project's purpose is to explore machine learning algorithms for use of autonomous vehicles.
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