One Paragraph of project description goes here
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
# see here for model: https://www.cggonzalez.com/blog/model.html | |
from sympy import symbols, init_printing, Matrix, sin, cos, tan | |
init_printing() | |
x_dot, y_dot, z_dot = symbols("x_dot y_dot z_dot") | |
phi, theta, psi = symbols("phi theta psi") | |
phi_dot, theta_dot, psi_dot = symbols("phi_dot theta_dot psi_dot") | |
p, q, r = symbols("p q r") |
your_model.author = 'your name' | |
your_model.short_description = 'Digit Recognition with MNIST' | |
your_model.input_description['image'] = 'Takes as input an image of a handwritten digit' | |
your_model.output_description['output'] = 'Prediction of Digit |
from keras.models import load_model | |
import coremltools | |
model.save('your_model.h5') | |
output_labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'] | |
your_model = coremltools.converters.keras.convert('your_model.h5', input_names=['image'], output_names=['output'], | |
class_labels=output_labels, image_input_names='image') | |
#your_model.author = 'your name' |
model.save('your_model.h5') |
output_labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'] | |
your_model = coremltools.converters.keras.convert('your_model.h5', input_names=['image'], output_names=['output'], | |
class_labels=output_labels, image_input_names='image') | |
your_model.save('your_model_name.mlmodel') |
output_labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'] | |
your_model = coremltools.converters.keras.convert('your_model.h5', input_names=['image'], output_names=['output'], | |
class_labels=output_labels, image_input_names='image') | |
model.save('your_model_name.mlmodel') | |
from keras.models import load_model | |
import coremltools |
from keras.models import Sequential | |
from keras.layers import Dense, Conv2D, Flatten, MaxPooling2D | |
from sklearn import datasets | |
from sklearn.model_selection import train_test_split | |
digits = datasets.load_digits() | |
X = digits["images"] | |
y = digits["target"] |
import anki_vector | |
import time | |
from PIL import Image | |
import cv2 | |
import numpy as np | |
import matplotlib.pyplot as plt | |
with anki_vector.Robot(enable_camera_feed=True) as robot: | |
robot.motors.set_head_motor(-5.0) # move head to look at ground | |
robot.motors.set_wheel_motors(10, 10) # set initial driving direction |