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Jensen-holm commited on May 2, 2023

Commit

24f2542

1 Parent(s): 31806c5

splitting up some backprop functions that may have been too long

Browse files

Files changed (5) hide show

app.py +6 -1
neural_network/backprop.py +41 -11
neural_network/main.py +4 -1
templates/index.html +1 -1
templates/opts.html +32 -0

app.py CHANGED Viewed

@@ -25,6 +25,11 @@ def index():
     return render_template("index.html")
 @app.route("/process_algorithm", methods=["GET", "POST"])
 def process_algorithm():
     alg = request.form.get('model-select')
@@ -33,7 +38,7 @@ def process_algorithm():
     # have a form for options based on the algorithm the user chose
     # and set it as the args variable, make a 'go' button for this funcitonality
     # to start the algorithm
-    args = 0
     if args:
         # create random numpy array dataset
         X, y = random_dataset(100, 3)

     return render_template("index.html")
+@app.route("/select_algorithm", methods=["GET", "POST"])
+def select_algorithm():
+    return
 @app.route("/process_algorithm", methods=["GET", "POST"])
 def process_algorithm():
     alg = request.form.get('model-select')
     # have a form for options based on the algorithm the user chose
     # and set it as the args variable, make a 'go' button for this funcitonality
     # to start the algorithm
+    args = request.form.get("params")
     if args:
         # create random numpy array dataset
         X, y = random_dataset(100, 3)

neural_network/backprop.py CHANGED Viewed

@@ -4,21 +4,50 @@ from tqdm import tqdm
 from neural_network.opts import activation
-def bp(X_train: np.array, y_train: np.array, wb: dict, args: dict) -> (dict, np.array):
-    epochs = args["epochs"]
-    func = activation[args["activation_func"]]["main"]
-    func_prime = activation[args["activation_func"]]["prime"]
-    w1, w2 = wb["W1"], wb["W2"]
-    b1, b2 = wb["b1"], wb["b2"]
-    lr = args["learning_rate"]
     r = {}
     loss_history = []
     for e in tqdm(range(epochs)):
         # forward prop
-        node1 = compute_node(arr=X_train, w=w1, b=b1, func=func)
-        y_hat = compute_node(arr=node1, w=w2, b=b2, func=func)
-        error = y_hat - y_train
         mean_squared_error = mse(y_train, y_hat)
         loss_history.append(mean_squared_error)
@@ -32,7 +61,8 @@ def bp(X_train: np.array, y_train: np.array, wb: dict, args: dict) -> (dict, np.
             error * func_prime(y_hat),
         )
         db2 = np.sum(error * func_prime(y_hat), axis=0)
-        db1 = np.sum(np.dot(error * func_prime(y_hat), w2.T) * func_prime(node1), axis=0)
         # update weights & biases using gradient descent.
         # this is -= and not += because if the gradient descent

 from neural_network.opts import activation
+def get_args(args: dict, wb: dict):
+    return (
+        args["epochs"],
+        args["activation_func"],
+        args["func_prime"],
+        args["learning_rate"],
+        wb["W1"],
+        wb["W2"],
+        wb["b1"],
+        wb["b2"],
+    )
+def fp(
+    X_train: np.array,
+    y_train: np.array,
+    actiavtion: callable,
+    w1: np.array,
+    w2: np.array,
+    b1: np.array,
+    b2: np.array,
+):
+    n1 = compute_node(arr=X_train, w=w1, b=b1, func=activation)
+    y_hat = compute_node(arr=n1, w=w2, b=b2, func=activation)
+    return y_hat, n1, (y_hat-y_train)
+def bp(
+    X_train: np.array,
+    y_train: np.array,
+    wb: dict,
+    args: dict
+):
+    epochs, func, func_prime, lr, w1, w2, b1, b2 = get_args(args, wb)
     r = {}
     loss_history = []
     for e in tqdm(range(epochs)):
         # forward prop
+        y_hat, node1, error = fp(
+            X_train=X_train,
+            y_train=y_train,
+            actiavtion=func,
+            w1=w1, w2=w2, b1=b1, b2=b2,
+        )
         mean_squared_error = mse(y_train, y_hat)
         loss_history.append(mean_squared_error)
             error * func_prime(y_hat),
         )
         db2 = np.sum(error * func_prime(y_hat), axis=0)
+        db1 = np.sum(np.dot(error * func_prime(y_hat), w2.T)
+                     * func_prime(node1), axis=0)
         # update weights & biases using gradient descent.
         # this is -= and not += because if the gradient descent

neural_network/main.py CHANGED Viewed

@@ -7,7 +7,10 @@ from neural_network.model import Network
 from neural_network.plot import loss_history_plt, save_plt
-def init(X: np.array, hidden_size: int) -> dict:
     """
     returns a dictionary containing randomly initialized
     weights and biases to start off the neural_network

 from neural_network.plot import loss_history_plt, save_plt
+def init(
+    X: np.array,
+    hidden_size: int
+) -> dict:
     """
     returns a dictionary containing randomly initialized
     weights and biases to start off the neural_network

templates/index.html CHANGED Viewed

@@ -14,7 +14,7 @@
         <p class="text-gray-500">By: Jensen Holm</p>
     </div>
     <div class="w-full md:w-1/3">
-        <form method="POST" action="{{ url_for('process_algorithm') }}">
             <label class="block font-medium text-gray-700 mb-2" for="model-select">
                 Select Algorithm
             </label>

         <p class="text-gray-500">By: Jensen Holm</p>
     </div>
     <div class="w-full md:w-1/3">
+        <form method="POST" action="{{ url_for('select_algorithm') }}">
             <label class="block font-medium text-gray-700 mb-2" for="model-select">
                 Select Algorithm
             </label>

templates/opts.html ADDED Viewed

	@@ -0,0 +1,32 @@

+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Data Mining From Scratch</title>
+    <link rel="stylesheet" href="{{ url_for('static', filename='dist/css/output.css') }}">
+</head>
+<body class="bg-gray-100">
+<div class="container mx-auto flex flex-col items-center justify-center h-screen">
+    <div class="text-center mb-8">
+        <h1 class="text-4xl font-bold">Data Mining From Scratch</h1>
+        <p class="text-gray-500">By: Jensen Holm</p>
+    </div>
+    <div class="w-full md:w-1/3">
+        <form method="POST" action="{{ url_for('process_algorithm') }}">
+            <label class="block font-medium text-gray-700 mb-2" for="model-select">
+                Tune {{ alg }} Parameters
+            </label>
+            <select id="model-select" name="model-select"
+                    class="form-select block w-full mt-1 rounded-md shadow-sm border-gray-300 focus:border-indigo-300 focus:ring focus:ring-indigo-200 focus:ring-opacity-50">
+                {{% for i in opts %}}
+                    <option value="{{i}}">{{ i }}</option>
+                {{% endfor %}}
+            </select>
+            <button type="submit" class="bg-blue-500 hover:bg-blue-700 text-white font-bold py-2 px-4 rounded mt-4">
+                Go
+            </button>
+        </form>
+    </div>
+</div>
+</body>
+</html>