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# Building a Flask app on Image Classification of Dog/Cat Dataset implemented by Convolutional Neural Network (CNN) ### Phuong T.M. Chu & Minh H. Nguyen This is the project that we finished after the 6th week of studying **Machine Learning**.

## INTRODUCTION ### 1. The Dog vs. Cat Dataset **Dogs vs. Cats** [dataset](https://www.kaggle.com/c/dogs-vs-cats/data) provided by Microsoft Research contains 25,000 images of dogs and cats with the labels * 1 = dog * 0 = cat ### 2. Project goals - Building a **deep neural network** using **TensorFlow** to classify dogs and cats images. - Making a **Flask application** so user can upload their photos and receive the prediction. ### 3. Project plan During this project, we need to answer these following questions: **A. Build the model** - How to import the data - How to preprocess the images - How to create a model - How to train the model with the data - How to export the model - How to import the model **B. Build the Flask app** **Front end** - HTML - How to connect frontend to backend - How to draw a number on HTML - How to make UI looks good **Back end** - Flask - How to set up Flask - How to handle backend error - How to make real-time prediction - Combine the model with the app ## SETUP ENVIRONMENT * In order to run our model on a Flask application locally, you need to clone this repository and then set up the environment by these following commands: ```shell python3 -m pip install --user pipx python3 -m pipx ensurepath pipx install pipenv # Install dependencies pipenv install --dev # Setup pre-commit and pre-push hooks pipenv run pre-commit install -t pre-commit pipenv run pre-commit install -t pre-push ``` * On the Terminal, use these commands: ``` # enter the environment pipevn shell pipenv graph set FLASK_APP=app.py set FLASK_ENV=development export FLASK_DEBUG=1 flask run ``` * If you have error `ModuleNotFoundError: No module named 'tensorflow'` then use ``` pipenv install tensorflow==2.0.0beta-1 ``` * If `* Debug mode: off` then use ``` export FLASK_DEBUG=1 ``` * Run the model by ```shell pipenv run flask run ``` * If you want to exit `pipenv shell`, use `exit` ## HOW IT WORK: CONVOLUTIONAL NEURAL NETWORK (CNN) > In deep learning, a [convolutional neural network](https://en.wikipedia.org/wiki/Convolutional_neural_network) (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery. (Wiki) For this project, we used **pre-trained model [MobileNetV2](https://keras.io/applications/#mobilenetv2)** from keras. MobileNetV2 is a model that was trained on a large dataset to solve a **similar problem to this project**, so it will help us to save lots of time on buiding low-level layers and focus on the application. ***Note:** You can learn more about CNN architecture [here](https://medium.com/@RaghavPrabhu/understanding-of-convolutional-neural-network-cnn-deep-learning-99760835f148)*  ### 1. Load and preprocess images: - Import **path, listdir** from **os library**. - Find and save all the image's path to **all_image_paths**. (note that our images is in folder `train`). ```python all_image_path = [path.join('train', p) for p in listdir('train') if isfile(path.join('train', p))] ``` - Define a function to load and preprocess image from path: ```python def load_and_preprocess_image(path): file = tf.io.read_file(path) image = tf.image.decode_jpeg(file , channels=3) image = tf.image.resize(image, [192, 192]) # resize all images to the same size. image /= 255.0 # normalize to [0,1] range image = 2*image-1 # normalize to [-1,1] range return image ``` - Load and preprocess all images which path is in **all_image_path**: ```python all_images = [load_and_preprocess_image(path) for path in all_image_path] ``` - Save all image labels in **all_image_labels**: ```python dict = {'cat': 0, 'dog': 1} # path.split('.')[0][-3:] return the name of the image ('dog' or 'cat') labels = [path.split('.')[0][-3:] for path in all_image_path] # Transfer name-labels to number-labels: all_image_labels = [dict[label] for label in labels] ``` - To implement batch training, we put the images and labels into Tensorflow dataset: ```python ds = tf.data.Datasets.from_tensor_slices((all_images, all_image_labels)) ``` ### 2. Building CNN model: The CNN model contain **MobileNetV2, Pooling, fully-connected hidden layer and Output layer**. - First we create **mobile_net** as an instance of **MobileNetV2**: ```python mobile_net = tf.keras.applications.MobileNetV2(input_shape=(192, 192, 3), include_top=False) mobile_net.trainable=False # this told the model not to train the mobile_net. ``` - Then we build CNN model: ```python cnn_model = keras.models.Sequential([ mobile_net, # mobile_net is low-level layers keras.layers.GlobalAveragePooling2D(), keras.layers.Flatten(), keras.layers.Dense(***, activation="relu"), # fully-connected hidden layer keras.layers.Dense(2, activation="softmax") # output layer ]) ``` ### 3. Training model: We almost there! But before training our **cnn_model**, we need to implement batch to the training data so that the model will train faster. ```python BATCH_SIZE = 32 AUTOTUNE = tf.data.experimental.AUTOTUNE train_ds = ds.shuffle(buffer_size = len(all_image_labels)) train_ds = train_ds.repeat() train_ds = train_ds.batch(BATCH_SIZE) train_ds = train_ds.prefetch(buffer_size=AUTOTUNE) ``` Now we train the model. ```python cnn_model.compile(optimizer=tf.keras.optimizers.Adam(), loss='sparse_categorical_crossentropy', metrics=["accuracy"]) ``` ```python steps_per_epoch=tf.math.ceil(len(all_image_dirs)/BATCH_SIZE).numpy() cnn_model.fit(train_ds, epochs=2, steps_per_epoch=steps_per_epoch) ``` After training, save the model for later use. ```python cnn_model.save('my_model.h5') ``` ## MODEL PERFOMANCE SUMARY Our model has the accuracy of **97.79 %** for the train dataset and **97.32 %** for the test dataset. ## FLASH APPLICATION ### Homepage  ### Example of results   ## CONCLUSION We successfully **built a deep neural network model** by implementing **Convolutional Neural Network (CNN)** to classify dog and cat images with very high accuracy **97.32 %**. In addition, we also **built a Flask application** so user can upload their images and classify easily.

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