How to Build an AI Model with Tensorflow & Deploy with Django - Step by Step Guide

# Preparing Dataset
# 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)

# Train Model
# 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")