13126 lines
590 KiB
Plaintext
13126 lines
590 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Uczenie maszynowe 2019/2020 – laboratoria\n",
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"### 18/19 maja 2020\n",
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"# 10. Sieci neuronowe - wprowadzenie"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Poniżej znajduje się implementacja prostej sieci neuronowej dla problemu klasyfikacji binarnej na przykładzie losowo wygenerowanego zestawu danych.\n",
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"\n",
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"W sieciach jednokierunkowych (ang. _feedforward_) wartości neuronów w $i$-tej warstwie są obliczane na podstawie wartości neuronów warstwy $i-1$. Mając daną $n$-warstwową sieć neuronową oraz jej parametry $\\Theta^{(1)}, \\ldots, \\Theta^{(n)} $ oraz $\\beta^{(1)}, \\ldots, \\beta^{(n)}$ liczymy: \n",
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"$$a^{(i)} = g^{(i)}\\left( a^{(i-1)} \\Theta^{(i)} + \\beta^{(i)} \\right) \\; , $$\n",
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"gdzie $g^{(i)}$ to tzw. **funkcje aktywacji**"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Zadanie 10"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Część podstawowa (4 punkty)\n",
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"\n",
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" * Zaimplementuj funkcję `accuracy()` liczącą skuteczność klasyfikacji.\n",
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" * Za jej pomocą oblicz i wypisz końcową skuteczność klasyfikatora.\n",
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" * Wypisuj również wartość `accuracy` podczas trenowania (przy okazji wypisywania wartości funkcji kosztu).\n",
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" * Zbuduj sieci neuronowe dla różnych wielkości warstwy ukrytej (`dim_hid` = 1, 2, 5, 10, 25). Porównaj skuteczność tych modeli."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Część zaawansowana (2 punkty)\n",
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"\n",
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"Zastosuj poniższą implementację sieci neuronowej do klasyfikacji binarnej zbioru wygenerowanego za pomocą wybranej funkcji [sklearn.datasets](http://scikit-learn.org/stable/modules/classes.html#samples-generator). Ustal rozmiary warstw wejściowej ($n \\gt 2$) i ukrytej, dobierz odpowiednie parametry sieci (parametr $\\alpha$, liczba epok, wielkość warstwy ukrytej). Podaj skuteczność klasyfikacji."
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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": 1,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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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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"from sklearn import datasets\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"\n",
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"def generate_data():\n",
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" # Keep results deterministic\n",
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" np.random.seed(1234)\n",
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" X, y = datasets.make_moons(200, noise=0.25)\n",
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" # X, y = datasets.make_classification(200, 2, 2, 0)\n",
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" return X, y\n",
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"\n",
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"def visualize(X, y, model=None):\n",
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" x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
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" y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
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" h = 0.01\n",
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" xx, yy = np.meshgrid(\n",
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" np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
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" if model:\n",
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" Z = predict(model, np.c_[xx.ravel(), yy.ravel()])\n",
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" Z = Z.reshape(xx.shape)\n",
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" plt.contourf(xx, yy, Z, cmap=plt.cm.viridis)\n",
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" plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.viridis)\n",
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" plt.show()\n",
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"\n",
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"def initialize_model(dim_in=2, dim_hid=3, dim_out=2):\n",
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" # Keep results deterministic\n",
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" np.random.seed(1234)\n",
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" W1 = np.random.randn(dim_in, dim_hid) / np.sqrt(dim_in)\n",
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" b1 = np.zeros((1, dim_hid))\n",
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" W2 = np.random.randn(dim_hid, dim_out) / np.sqrt(dim_hid)\n",
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" b2 = np.zeros((1, dim_out))\n",
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" return W1, b1, W2, b2\n",
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"\n",
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"def softmax(X):\n",
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" e = np.exp(X)\n",
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" return e / np.sum(e, axis=1, keepdims=True)\n",
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"\n",
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"def predict(model, X):\n",
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" W1, b1, W2, b2 = model\n",
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" z1 = X.dot(W1) + b1\n",
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" a1 = np.tanh(z1)\n",
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" z2 = a1.dot(W2) + b2\n",
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" probs = softmax(z2)\n",
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" return np.argmax(probs, axis=1)\n",
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"\n",
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"def calculate_cost(model, X, y):\n",
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" W1, b1, W2, b2 = model\n",
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" z1 = X.dot(W1) + b1\n",
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" a1 = np.tanh(z1)\n",
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" z2 = a1.dot(W2) + b2\n",
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" probs = softmax(z2)\n",
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" preds = probs[:, 1]\n",
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" return -1. / len(y) * np.sum(\n",
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" np.multiply(y, np.log(preds)) + np.multiply(1 - y, np.log(1 - preds)),\n",
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" axis=0)\n",
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"\n",
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"def accuracy(model, X, y):\n",
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" # TODO: Napisz funkcję obliczającą skuteczność.\n",
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" pass\n",
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"\n",
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"def train(model, X, y, alpha=0.01, epochs=10000, debug=False):\n",
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" W1, b1, W2, b2 = model\n",
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" m = len(X)\n",
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"\n",
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" for i in range(epochs):\n",
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" # Forward propagation\n",
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" z1 = X.dot(W1) + b1\n",
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" a1 = np.tanh(z1)\n",
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" z2 = a1.dot(W2) + b2\n",
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" probs = softmax(z2)\n",
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"\n",
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" # Backpropagation\n",
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" delta3 = probs\n",
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" delta3[range(m), y] -= 1\n",
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" dW2 = (a1.T).dot(delta3)\n",
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" db2 = np.sum(delta3, axis=0, keepdims=True)\n",
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" delta2 = delta3.dot(W2.T) * (1 - np.power(a1, 2))\n",
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" dW1 = np.dot(X.T, delta2)\n",
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" db1 = np.sum(delta2, axis=0)\n",
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"\n",
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" # Parameter update\n",
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" W1 -= alpha * dW1\n",
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" b1 -= alpha * db1\n",
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" W2 -= alpha * dW2\n",
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" b2 -= alpha * db2\n",
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"\n",
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" # Print loss\n",
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" if debug and i % 1000 == 0:\n",
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" model = (W1, b1, W2, b2)\n",
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" print(\"Cost after iteration {}: {:.4f}\".format(i, calculate_cost(\n",
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" model, X, y)))\n",
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" # TODO: Wypisz skuteczność (accuracy) klasyfikacji w tym miejscu\n",
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"\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": 2,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [
|
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{
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"data": {
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"text": [
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"Cost after iteration 0: 0.4692\n",
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"Cost after iteration 1000: 0.1527\n",
|
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"Cost after iteration 2000: 0.1494\n",
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"Cost after iteration 3000: 0.1478\n",
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"Cost after iteration 4000: 0.1458\n",
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"Cost after iteration 6000: 0.1433\n",
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"Cost after iteration 7000: 0.1423\n",
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"Cost after iteration 8000: 0.1415\n",
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"Cost after iteration 9000: 0.1407\n"
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{
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"data": {
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