.

Author: mapattacker

docker.rst

@@ -128,10 +128,11 @@ Note that Dockerhub can only allow a single image to be made private for the fre
 Docker Compose
 ----------------
 
-In a production environment, a docker compose file can be used to run all separate docker containers (which interact with each other) 
+In a production environment, a docker compose file can be used to run all separate docker containers 
 together. It consists of all necessary configurations that a ``docker run`` command provides in a yaml file.
 
 So, instead of entering multiple ``docker run imageX``, we can just run one docker compose file to start all images.
+We also input all the commands like ports, volumes, depends_on
 
 .. figure:: images/docker_compose1.png
     :width: 650px
@@ -141,25 +142,33 @@ So, instead of entering multiple ``docker run imageX``, we can just run one dock
 
 Below is an example using wordpress blog, where both the wordpress and mysql database are needed to get it working.
 
+Run ``docker-compose up`` command to launch. 
+If there are some images not built yet, we can add another specification in the docker compose file 
+e.g., ``build: /directory_name``. 
+
 .. code:: python
 
-    # in ymal file, ":" represents dictionary
-                    # "-" represents list
-                    # note that spaces matter
-    version: '3'
+    # in ymal file, 
+        ":" represents dictionary
+        # "-" represents list
+        $ the 2nd level definition, e.g. "web:" is just a name, can give it anything
+        # note that spaces matter
+    # must specify version
+
+    version: 3
     services:
-    mysql:
-        image: "mysql"
-        environment:
-            - MYSQL_ROOT_PASSWORD=password 
-        volumes:
-            - "/data/mysql:/var/lib/mysql"
-    web:
-        image: "wordpress"
-        ports:
-            - "8080:80"
-        environment:
-            - WORDPRESS_DB_PASSWORD=password
+        mysql:
+            image: "mysql"
+            environment:
+                - MYSQL_ROOT_PASSWORD=password 
+            volumes:
+                - "/data/mysql:/var/lib/mysql"
+        web:
+            image: "wordpress"
+            ports:
+                - "8080:80"
+            environment:
+                - WORDPRESS_DB_PASSWORD=password
 
 
 Docker Swarm
@@ -311,4 +320,10 @@ Commands
 
 +--------------------------------------+------------------------------------+
 | ``docker exec container_nm COMMAND`` | execute a command within container |
-+--------------------------------------+------------------------------------+
++--------------------------------------+------------------------------------+
+
+
+Tips
+*****
+
+https://pythonspeed.com/articles/multi-stage-docker-python/

flask.rst

@@ -0,0 +1,40 @@
+FLASK
+======
+
+Flask is a micro web framework written in Python. 
+It is easy and fast to implement with the knowledge of basic web development and REST APIs.
+
+
+Basics
+------
+
+.. code:: Python
+
+    from flask import Flask, render_template
+
+    app = Flask(__name__)
+
+
+    @app.route('/')
+    def index():
+        return render_template('index.html')
+
+    if __name__ == '__main__':
+        app.run(debug = True)
+
+
+Folder Structure
+-----------------
+
+There are some default directory structure to adhere to. 
+The first is that HTML files are placed under /templates, 
+second is for css or other static files like images, will be placed under /static
+
+```
+├── app.py
+├── static
+│   └── img
+│       └── image.png
+└── templates
+    └── index.html
+```

index.rst

@@ -29,6 +29,7 @@ A lot of the content are compiled from various resources, so please cite them ap
    evaluation
    explainability
    utils
+   flask
    docker
    ethics
    resources

sl-deeplearning.rst

@@ -497,7 +497,6 @@ architecture includes **LeNet-5** (handwriting recognition), **AlexNet** (deeper
 
 .. _article1: https://medium.com/@RaghavPrabhu/cnn-architectures-lenet-alexnet-vgg-googlenet-and-resnet-7c81c017b848
 
-
 Keras Model
 ***************
 
@@ -509,24 +508,10 @@ Keras Model
     from tensorflow.keras.layers import Dense, Dropout, Conv2D, MaxPooling2D, Flatten
     from tensorflow.keras.optimizers import RMSprop
 
-    (X_train, y_train), (X_test, y_test) = mnist.load_data()
-
-
-    # need to reshape image dataset
-    total_rows_train = X_train.shape[0]
-    total_rows_test = X_test.shape[0]
-    sample_rows = X_train.shape[1]
-    sample_columns = X_train.shape[2]
-    num_channels = 1
-
-    # i.e. X_train = X_train.reshape(60000,28,28,1), where 1 means images are grayscale
-    X_train = X_train.reshape(total_rows_train,sample_rows,sample_columns,num_channels)
-    X_test = X_test.reshape(total_rows_test,sample_rows,sample_columns,num_channels)
-
-
     model = Sequential()
-    model.add(Conv2D(32, kernel_size=(3, 3), activation='relu',
-                     input_shape=(sample_rows, sample_columns, num_channels)))
+    model.add(Conv2D(32, kernel_size=(3, 3),
+                    activation='relu',
+                    input_shape=input_shape))
 
     # 64 3x3 kernels
     model.add(Conv2D(64, (3, 3), activation='relu'))
@@ -543,7 +528,6 @@ Keras Model
     # Final categorization from 0-9 with softmax
     model.add(Dense(10, activation='softmax'))
 
-
     model.summary()
 
     # _________________________________________________________________
@@ -570,7 +554,7 @@ Keras Model
     # Non-trainable params: 0
     # _________________________________________________________________
 
-    model.compile(loss='sparse_categorical_crossentropy',
+    model.compile(loss='categorical_crossentropy',
               optimizer='adam',
               metrics=['accuracy'])