In this project we demonstrated how to use the tensorflow library to create a neural network that can be used to classify 6 different animals.
LABELS
Cat 0
Dog 1
Monkey 2
Parrot 3
Elephant 4
Bear 5- Knowledge in Python
- Basic understanding of Django and the Web
- Python 3 : The Python Programming Language
- Django : The Web Framework for Python
- TensorFlow : The TensorFlow Machine Learning Library used to create the neural network
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Download and Install the latest Python 3 on your computer. Install Now
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Get the starter files here
# Download the start files or Clone the repository
git clone https://github.com/Academy-Omen/tensored-django.git
# switch to the starter branch
cd tensored-django
git checkout starter- Create a virtual environment where all packages will be installed.
# Windows
py -3 -m venv env
# Linux and Mac
python3 -m venv env- Activate the virtual environment.
# Windows
.\env\Scripts\activate
# Linux and Mac
source env/bin/activate- Install all the required packages.
pip install -r requirements.txtWe are building an Image classifier that can predict if the inputed image is a Cat, Dog, Monkey, Parrot, Elephant or Bear. We therefore need images of these animals. The images are going to be used to train our algorithm on how to recognize these animals. This is a supervised machine learning algorithm aka just like a teacher teaching a kid on how to recognize different animals.
We will used the Download All Images Chrome Extension which will help us download images automatically.
Just like a teacher will do, we will label each group of images so our kid (aka computer) can verify after performing a prediction.
# temporal storage for labels and images
data=[]
labels=[]
# Cat 0
# Get the animal directory
cats = os.listdir(os.getcwd() + "/CNN/data/cat")
for x in cats:
"""
Loop through all the images in the directory
1. Convert to arrays
2. Resize the images
3. Add image to dataset
4. Add the label
"""
imag=cv2.imread(os.getcwd() + "/CNN/data/cat/" + x)
img_from_ar = Image.fromarray(imag, 'RGB')
resized_image = img_from_ar.resize((50, 50))
data.append(np.array(resized_image))
labels.append(0)
# load in animals and labels
animals=np.array(data)
labels=np.array(labels)
# save
np.save("animals",animals)
np.save("labels",labels)We will use TensorFlow CNN Example to create our own CNN. Feel free to read more and to fine-tune the algorithm. We start by separating the dataset into train_set (90%) and test_set (10%) which are used for training and validation respectively.
# train through 100 times
history = model.fit(x_train, y_train, epochs=100,
validation_data=(x_test, y_test))
# perform validation and get accuracy
test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
print(test_acc)
# save the model or brain
model.save("model.h5")- Create new django project
# create new project in the current directory
django-admin startproject core .
# run web app
python manage.py runserver
# visit http://localhost:8000/ on your browser- We will go ahead and update our settings.py file and then create a view that will perform the prediction and pass it to the frontend.