1000 lines
27 KiB
Text
1000 lines
27 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "c4460aee-ec58-454e-9304-82deb89942b4",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd \n",
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"from matplotlib import pyplot as plt \n",
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"\n",
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"data = pd.read_csv('data/bel_data_test.csv')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "f1ec44a2-7bb0-44a1-a417-69ab70e1b6f8",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"data = np.array(data)\n",
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"\n",
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"m,n = data.shape\n",
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"data_train = data[1000:m].T\n",
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"\n",
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"Y_train = data_train[0].astype(int)\n",
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"\n",
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"X_train = data_train[1:n]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "9223239b-35b9-43bc-acf9-dac6a19587c5",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"def init_params():\n",
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" W1 = np.random.rand(10,1024) - 0.5\n",
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" b1 = np.random.rand(10,1) - 0.5\n",
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" W2 = np.random.rand(61,10) - 0.5\n",
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" b2 = np.random.rand(61,1) - 0.5\n",
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" return W1, b1 , W2, b2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "610cd614-b3c9-445e-8f3d-491596bc773c",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"def ReLU(Z):\n",
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" return np.maximum(Z,0)\n",
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"\n",
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"def softmax(Z):\n",
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" A = np.exp(Z) / sum(np.exp(Z))\n",
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" return A\n",
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"\n",
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"def forward_prop(W1, b1, W2, b2, X):\n",
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" Z1 = W1.dot(X) + b1\n",
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" A1 = ReLU(Z1)\n",
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" Z2 = W2.dot(A1) + b2\n",
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" A2 = softmax(Z2)\n",
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" return Z1, A1, Z2, A2\n",
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"\n",
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"def ReLU_deriv(Z):\n",
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" return Z > 0\n",
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"\n",
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"def one_hot(Y):\n",
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" one_hot_Y = np.zeros((Y.size, Y.max() + 1))\n",
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" one_hot_Y[np.arange(Y.size), Y] = 1\n",
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" one_hot_Y = one_hot_Y.T\n",
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" return one_hot_Y\n",
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"\n",
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"def backward_prop(Z1, A1, Z2, A2, W1, W2, X, Y):\n",
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" one_hot_Y = one_hot(Y)\n",
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" dZ2 = A2 - one_hot_Y\n",
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" dW2 = 1 / m * dZ2.dot(A1.T)\n",
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" db2 = 1 / m * np.sum(dZ2)\n",
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" dZ1 = W2.T.dot(dZ2) * ReLU_deriv(Z1)\n",
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" dW1 = 1 / m * dZ1.dot(X.T)\n",
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" db1 = 1 / m * np.sum(dZ1)\n",
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" return dW1, db1, dW2, db2\n",
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"\n",
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"def update_params(W1, b1, W2, b2, dW1, db1, dW2, db2, alpha):\n",
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" W1 = W1 - alpha * dW1\n",
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" b1 = b1 - alpha * db1 \n",
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" W2 = W2 - alpha * dW2 \n",
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" b2 = b2 - alpha * db2 \n",
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" return W1, b1, W2, b2\n",
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"\n",
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"def get_predictions(A2):\n",
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" return np.argmax(A2, 0)\n",
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"\n",
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"def get_accuracy(predictions, Y):\n",
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" #print(predictions, Y)\n",
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" return np.sum(predictions == Y) / Y.size"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "287c3525-e1a0-41c4-a1ae-9c24c805c7c5",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"acc_store = [] "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "539820c9-dc35-4f24-bbff-7860f7442c19",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"def gradient_descent(X, Y, alpha, iterations):\n",
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" W1, b1, W2, b2 = init_params()\n",
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" for i in range(iterations):\n",
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" Z1, A1, Z2, A2 = forward_prop(W1, b1, W2, b2, X)\n",
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" dW1, db1, dW2, db2 = backward_prop(Z1, A1, Z2, A2, W1, W2, X, Y)\n",
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" W1, b1, W2, b2 = update_params(W1, b1, W2, b2, dW1, db1, dW2, db2, alpha)\n",
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" if i % 10 == 0:\n",
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" print(\"Iteration: \", i)\n",
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" predictions = get_predictions(A2)\n",
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" pred = get_accuracy(predictions, Y)\n",
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" print(pred)\n",
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" acc_store.append(pred)\n",
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" return W1, b1, W2, b2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "06a5f975-472b-4b98-99e2-c1fff036ddc7",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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}
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],
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"source": [
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"W1, b1, W2, b2 = gradient_descent(X_train, Y_train, 0.10, 4000)\n",
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"df = pd.DataFrame(acc_store)\n",
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"df.to_csv('bt_acc.csv', index=False)\n",
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"np.savez(\"bt_weights\", W1, b1, W2, b2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d3370366-9827-44db-85b9-512e39574ee7",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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