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simple-nnn/Simple NN from scratch.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31ec7db3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#https://www.youtube.com/watch?v=w8yWXqWQYmU\n",
+    "\n",
+    "data = np.array(data)\n",
+    "m, n = data.shape\n",
+    "np.random.shuffle(data) # shuffle before splitting into dev and training sets\n",
+    "\n",
+    "data_dev = data[0:1000].T\n",
+    "Y_dev = data_dev[0]\n",
+    "X_dev = data_dev[1:n]\n",
+    "X_dev = X_dev / 255.\n",
+    "\n",
+    "data_train = data[1000:m].T\n",
+    "Y_train = data_train[0]\n",
+    "X_train = data_train[1:n]\n",
+    "X_train = X_train / 255.\n",
+    "_,m_train = X_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4e2a280c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def init_params():\n",
+    "    W1 = np.random.rand(10, 784) - 0.5\n",
+    "    b1 = np.random.rand(10, 1) - 0.5\n",
+    "    W2 = np.random.rand(10, 10) - 0.5\n",
+    "    b2 = np.random.rand(10, 1) - 0.5\n",
+    "    return W1, b1, W2, b2\n",
+    "\n",
+    "def ReLU(Z):\n",
+    "    return np.maximum(Z, 0)\n",
+    "\n",
+    "def softmax(Z):\n",
+    "    A = np.exp(Z) / sum(np.exp(Z))\n",
+    "    return A\n",
+    "    \n",
+    "def forward_prop(W1, b1, W2, b2, X):\n",
+    "    \"\"\"W1,W2 are wights, b1,b2 are biasis X is the input\"\"\"\n",
+    "    Z1 = W1.dot(X) + b1  # value of layer 1 \n",
+    "    A1 = ReLU(Z1)        # value of layer 1 after applying activation\n",
+    "    Z2 = W2.dot(A1) + b2 # value of layer 2\n",
+    "    A2 = softmax(Z2)     # value of layer 2 after applying activation\n",
+    "    return Z1, A1, Z2, A2\n",
+    "\n",
+    "def ReLU_deriv(Z):\n",
+    "    return Z > 0\n",
+    "\n",
+    "def one_hot(Y):\n",
+    "    one_hot_Y = np.zeros((Y.size, Y.max() + 1))\n",
+    "    one_hot_Y[np.arange(Y.size), Y] = 1\n",
+    "    one_hot_Y = one_hot_Y.T\n",
+    "    return one_hot_Y\n",
+    "\n",
+    "def backward_prop(Z1, A1, Z2, A2, W1, W2, X, Y):\n",
+    "    one_hot_Y = one_hot(Y)\n",
+    "    dZ2 = A2 - one_hot_Y                 #by how much layer 2 is off\n",
+    "    dW2 = 1 / m * dZ2.dot(A1.T)          # how much the weight contributed for the error of layer 2\n",
+    "    db2 = 1 / m * np.sum(dZ2)            # how much the bias contributed for the error of layer 2\n",
+    "    dZ1 = W2.T.dot(dZ2) * ReLU_deriv(Z1) #by how much layer 1 is off\n",
+    "    dW1 = 1 / m * dZ1.dot(X.T)\n",
+    "    db1 = 1 / m * np.sum(dZ1)\n",
+    "    return dW1, db1, dW2, db2\n",
+    "\n",
+    "def update_params(W1, b1, W2, b2, dW1, db1, dW2, db2, alpha):\n",
+    "    \"\"\"alpha is the learning rate, by how much we want to change the weights and biasis each epoch\"\"\"\n",
+    "    W1 = W1 - alpha * dW1\n",
+    "    b1 = b1 - alpha * db1    \n",
+    "    W2 = W2 - alpha * dW2  \n",
+    "    b2 = b2 - alpha * db2    \n",
+    "    return W1, b1, W2, b2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "86bd1d48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_predictions(A2):\n",
+    "    return np.argmax(A2, 0)\n",
+    "\n",
+    "def get_accuracy(predictions, Y):\n",
+    "    print(predictions, Y)\n",
+    "    return np.sum(predictions == Y) / Y.size\n",
+    "\n",
+    "def gradient_descent(X, Y, alpha, iterations):\n",
+    "    W1, b1, W2, b2 = init_params()\n",
+    "    for i in range(iterations):\n",
+    "        Z1, A1, Z2, A2 = forward_prop(W1, b1, W2, b2, X)\n",
+    "        dW1, db1, dW2, db2 = backward_prop(Z1, A1, Z2, A2, W1, W2, X, Y)\n",
+    "        W1, b1, W2, b2 = update_params(W1, b1, W2, b2, dW1, db1, dW2, db2, alpha)\n",
+    "        if i % 10 == 0: # print info for every 10th epoch\n",
+    "            print(\"Iteration: \", i)\n",
+    "            predictions = get_predictions(A2)\n",
+    "            print(get_accuracy(predictions, Y))\n",
+    "    return W1, b1, W2, b2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1e3bf4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_predictions(X, W1, b1, W2, b2):\n",
+    "    _, _, _, A2 = forward_prop(W1, b1, W2, b2, X)\n",
+    "    predictions = get_predictions(A2)\n",
+    "    return predictions\n",
+    "\n",
+    "def test_prediction(index, W1, b1, W2, b2):\n",
+    "    current_image = X_train[:, index, None]\n",
+    "    prediction = make_predictions(X_train[:, index, None], W1, b1, W2, b2)\n",
+    "    label = Y_train[index]\n",
+    "    print(\"Prediction: \", prediction)\n",
+    "    print(\"Label: \", label)\n",
+    "    \n",
+    "    current_image = current_image.reshape((28, 28)) * 255\n",
+    "    plt.gray()\n",
+    "    plt.imshow(current_image, interpolation='nearest')\n",
+    "    plt.show()"
+   ]
+  }
+ ],
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+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
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+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}