Deep_Learning/NN_FROM_SCRATCH/nn-from-scratch.ipynb

{"cells":[{"cell_type":"code","execution_count":106,"metadata":{"execution":{"iopub.execute_input":"2024-10-16T18:19:20.017871Z","iopub.status.busy":"2024-10-16T18:19:20.017438Z","iopub.status.idle":"2024-10-16T18:19:20.023386Z","shell.execute_reply":"2024-10-16T18:19:20.022092Z","shell.execute_reply.started":"2024-10-16T18:19:20.017828Z"},"trusted":true},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n"]},{"cell_type":"code","execution_count":107,"metadata":{"execution":{"iopub.execute_input":"2024-10-16T18:19:20.563528Z","iopub.status.busy":"2024-10-16T18:19:20.563064Z","iopub.status.idle":"2024-10-16T18:19:24.297011Z","shell.execute_reply":"2024-10-16T18:19:24.295965Z","shell.execute_reply.started":"2024-10-16T18:19:20.563482Z"},"trusted":true},"outputs":[],"source":["data = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')"]},{"cell_type":"code","execution_count":110,"metadata":{"execution":{"iopub.execute_input":"2024-10-16T18:19:26.770414Z","iopub.status.busy":"2024-10-16T18:19:26.769946Z","iopub.status.idle":"2024-10-16T18:19:26.777408Z","shell.execute_reply":"2024-10-16T18:19:26.776246Z","shell.execute_reply.started":"2024-10-16T18:19:26.770364Z"},"trusted":true},"outputs":[{"data":{"text/plain":["(42000, 785)"]},"execution_count":110,"metadata":{},"output_type":"execute_result"}],"source":["data.shape"]},{"cell_type":"code","execution_count":111,"metadata":{"execution":{"iopub.execute_input":"2024-10-16T18:19:31.131762Z","iopub.status.busy":"2024-10-16T18:19:31.131314Z","iopub.status.idle":"2024-10-16T18:19:32.006377Z","shell.execute_reply":"2024-10-16T18:19:32.005297Z","shell.execute_reply.started":"2024-10-16T18:19:31.131715Z"},"trusted":true},"outputs":[],"source":["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":112,"metadata":{"execution":{"iopub.execute_input":"2024-10-16T18:19:33.617470Z","iopub.status.busy":"2024-10-16T18:19:33.616973Z","iopub.status.idle":"2024-10-16T18:19:33.625228Z","shell.execute_reply":"2024-10-16T18:19:33.623820Z","shell.execute_reply.started":"2024-10-16T18:19:33.617426Z"},"trusted":true},"outputs":[{"data":{"text/plain":["(784,)"]},"execution_count":112,"metadata":{},"output_type":"execute_result"}],"source":["X_train[:, 0].shape"]},{"cell_type":"markdown","metadata":{},"source":["Our NN will have a simple two-layer architecture. Input layer $a^{[0]}$ will have 784 units corresponding to the 784 pixels in each 28x28 input image. A hidden layer $a^{[1]}$ will have 10 units with ReLU activation, and finally our output layer $a^{[2]}$ will have 10 units corresponding to the ten digit classes with softmax activation.\n","\n","**Forward propagation**\n","\n","$$Z^{[1]} = W^{[1]} X + b^{[1]}$$\n","$$A^{[1]} = g_{\\text{ReLU}}(Z^{[1]}))$$\n","$$Z^{[2]} = W^{[2]} A^{[1]} + b^{[2]}$$\n","$$A^{[2]} = g_{\\text{softmax}}(Z^{[2]})$$\n","\n","**Backward propagation**\n","\n","$$dZ^{[2]} = A^{[2]} - Y$$\n","$$dW^{[2]} = \\frac{1}{m} dZ^{[2]} A^{[1]T}$$\n","$$dB^{[2]} = \\frac{1}{m} \\Sigma {dZ^{[2]}}$$\n","$$dZ^{[1]} = W^{[2]T} dZ^{[2]} .* g^{[1]\\prime} (z^{[1]})$$\n","$$dW^{[1]} = \\frac{1}{m} dZ^{[1]} A^{[0]T}$$\n","$$dB^{[1]} = \\frac{1}{m} \\Sigma {dZ^{[1]}}$$\n","\n","**Parameter updates**\n","\n","$$W^{[2]} := W^{[2]} - \\alpha dW^{[2]}$$\n","$$b^{[2]} := b^{[2]} - \\alpha db^{[2]}$$\n","$$W^{[1]} := W^{[1]} - \\alpha dW^{[1]}$$\n","$$b^{[1]} := b^{[1]} - \\alpha db^{[1]}$$\n","\n","**Vars and shapes**\n","\n","Forward prop\n","\n","- $A^{[0]} = X$: 784 x m\n","- $Z^{[1]} \\sim A^{[1]}$: 10 x m\n","- $W^{[1]}$: 10 x 784 (as $W^{[1]} A^{[0]} \\sim Z^{[1]}$)\n","- $B^{[1]}$: 10 x 1\n","- $Z^{[2]} \\sim A^{[2]}$: 10 x m\n","- $W^{[1]}$: 10 x 10 (as $W^{[2]} A^{[1]} \\sim Z^{[2]}$)\n","- $B^{[2]}$: 10 x 1\n","\n","Backprop\n","\n","- $dZ^{[2]}$: 10 x m ($~A^{[2]}$)\n","- $dW^{[2]}$: 10 x 10\n","- $dB^{[2]}$: 10 x 1\n","- $dZ^{[1]}$: 10 x m ($~A^{[1]}$)\n","- $dW^{[1]}$: 10 x 10\n","- $dB^{[1]}$: 10 x 1"]},{"cell_type":"code","execution_count":113,"metadata":{"execution":{"iopub.execute_input":"2024-10-16T18:19:35.771662Z","iopub.status.busy":"2024-10-16T18:19:35.771174Z","iopub.status.idle":"2024-10-16T18:19:35.786551Z","shell.execute_reply":"2024-10-16T18:19:35.785547Z","shell.execute_reply.started":"2024-10-16T18:19:35.771617Z"},"trusted":true},"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","    Z1 = W1.dot(X) + b1\n","    A1 = ReLU(Z1)\n","    Z2 = W2.dot(A1) + b2\n","    A2 = softmax(Z2)\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\n","    dW2 = 1 / m * dZ2.dot(A1.T)\n","    db2 = 1 / m * np.sum(dZ2)\n","    dZ1 = W2.T.dot(dZ2) * ReLU_deriv(Z1)\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","    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":114,"metadata":{"execution":{"iopub.execute_input":"2024-10-16T18:19:37.265949Z","iopub.status.busy":"2024-10-16T18:19:37.265491Z","iopub.status.idle":"2024-10-16T18:19:37.275805Z","shell.execute_reply":"2024-10-16T18:19:37.274688Z","shell.execute_reply.started":"2024-10-16T18:19:37.265898Z"},"trusted":true},"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:\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":115,"metadata":{"execution":{"iopub.execute_input":"2024-10-16T18:19:38.833479Z","iopub.status.busy":"2024-10-16T18:19:38.832908Z","iopub.status.idle":"2024-10-16T18:20:38.284131Z","shell.execute_reply":"2024-10-16T18:20:38.282612Z","shell.execute_reply.started":"2024-10-16T18:19:38.833430Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Iteration:  0\n","[5 9 7 ... 0 7 2] [6 7 0 ... 0 9 5]\n","0.12773170731707317\n","Iteration:  10\n","[0 9 7 ... 0 1 2] [6 7 0 ... 0 9 5]\n","0.19665853658536586\n","Iteration:  20\n","[2 9 1 ... 0 1 2] [6 7 0 ... 0 9 5]\n","0.2515609756097561\n","Iteration: 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500)"]},{"cell_type":"code","execution_count":116,"metadata":{"execution":{"iopub.execute_input":"2024-10-16T18:22:18.575278Z","iopub.status.busy":"2024-10-16T18:22:18.574819Z","iopub.status.idle":"2024-10-16T18:22:18.584372Z","shell.execute_reply":"2024-10-16T18:22:18.582933Z","shell.execute_reply.started":"2024-10-16T18:22:18.575234Z"},"trusted":true},"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()"]},{"cell_type":"code","execution_count":117,"metadata":{"execution":{"iopub.execute_input":"2024-10-16T18:22:25.040770Z","iopub.status.busy":"2024-10-16T18:22:25.040336Z","iopub.status.idle":"2024-10-16T18:22:26.023032Z","shell.execute_reply":"2024-10-16T18:22:26.021785Z","shell.execute_reply.started":"2024-10-16T18:22:25.040724Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Prediction:  [6]\n","Label:  6\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Prediction:  [7]\n","Label:  7\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Prediction:  [0]\n","Label:  0\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Prediction:  [4]\n","Label:  6\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["test_prediction(0, W1, b1, W2, b2)\n","test_prediction(1, W1, b1, W2, b2)\n","test_prediction(2, W1, b1, W2, b2)\n","test_prediction(3, W1, b1, W2, b2)"]},{"cell_type":"code","execution_count":118,"metadata":{"execution":{"iopub.execute_input":"2024-10-16T18:22:41.325577Z","iopub.status.busy":"2024-10-16T18:22:41.325115Z","iopub.status.idle":"2024-10-16T18:22:41.360403Z","shell.execute_reply":"2024-10-16T18:22:41.359258Z","shell.execute_reply.started":"2024-10-16T18:22:41.325527Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["[3 8 4 1 3 7 9 4 5 2 8 9 2 7 4 6 7 1 3 9 1 2 0 3 0 7 7 4 0 1 5 6 0 8 3 2 7\n"," 6 0 0 8 8 7 9 8 1 9 0 6 7 1 4 0 7 5 8 9 7 2 7 4 8 6 1 8 3 2 7 0 3 1 1 7 1\n"," 5 6 2 1 0 0 0 2 7 7 1 8 3 0 2 6 4 0 6 4 8 5 7 5 6 9 7 2 3 3 4 1 3 5 1 6 5\n"," 5 1 1 6 0 0 6 6 7 7 7 9 9 4 4 3 9 9 5 2 8 2 4 9 9 8 0 2 9 9 9 1 0 4 7 3 0\n"," 6 4 1 1 2 0 6 1 7 5 0 4 0 6 7 6 1 4 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5]\n"]},{"data":{"text/plain":["0.833"]},"execution_count":118,"metadata":{},"output_type":"execute_result"}],"source":["dev_predictions = make_predictions(X_dev, W1, b1, W2, b2)\n","get_accuracy(dev_predictions, Y_dev)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]}],"metadata":{"kaggle":{"accelerator":"none","dataSources":[{"databundleVersionId":861823,"sourceId":3004,"sourceType":"competition"}],"dockerImageVersionId":30786,"isGpuEnabled":false,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.14"}},"nbformat":4,"nbformat_minor":4}