From 4974aafc7e1f211c57da9dc66b6c9e4b93667b66 Mon Sep 17 00:00:00 2001
From: Sathvik Duddela <147434853+r4iden-IITR@users.noreply.github.com>
Date: Fri, 10 May 2024 22:44:02 +0530
Subject: [PATCH 1/6] Add files via upload
---
Assignment1.ipynb | 394 ++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 394 insertions(+)
create mode 100644 Assignment1.ipynb
diff --git a/Assignment1.ipynb b/Assignment1.ipynb
new file mode 100644
index 0000000..0f402ee
--- /dev/null
+++ b/Assignment1.ipynb
@@ -0,0 +1,394 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "68c4bfd2-cb36-436e-b12b-f718b597db6b",
+ "metadata": {},
+ "source": [
+ "**Question 1a:**"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import numpy as np \n",
+ "import matplotlib.pyplot as plt\n",
+ "import math\n",
+ "\n",
+ "mu1 = 6 \n",
+ "mu2 = 4 \n",
+ "mu3 = 2 \n",
+ "\n",
+ "def poisson_pmf(k, mu): \n",
+ " return np.exp(-mu) * (mu ** k) / math.factorial(k) \n",
+ "def binomial_pmf(k,n,p):\n",
+ " return (p**k)*(math.factorial(n)/(math.factorial(n-k)*math.factorial(k)))*((1-p)**(n-k))\n",
+ "\n",
+ "\n",
+ "x = np.arange(0, 10) \n",
+ "pmf1 = np.array([poisson_pmf(k, mu1) for k in x]) \n",
+ "\n",
+ "pmf2 = np.array([poisson_pmf(k, mu2) for k in x]) \n",
+ "\n",
+ "pmf3 = np.array([poisson_pmf(k, mu3) for k in x]) \n",
+ "\n",
+ "plt.plot(x, pmf1, label='Poisson Distribution with $\\lambda$=6', alpha=0.5) \n",
+ "plt.plot(x, pmf2, label='Poisson Distribution with $\\lambda$=4', alpha=0.5) \n",
+ "plt.plot(x, pmf3, label='Poisson Distribution with $\\lambda$=2', alpha=0.5) \n",
+ "plt.xlabel('x') \n",
+ "plt.ylabel('P(X=x)') \n",
+ "plt.title('Poisson Distribution') \n",
+ "plt.legend() \n",
+ "plt.grid(True) \n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1572d801-27ce-4e06-8fd9-1348c6db2e77",
+ "metadata": {},
+ "source": [
+ "**Question 1b:**"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "n = int(20)\n",
+ "p = 0.23\n",
+ "mu = n*p\n",
+ "\n",
+ "x = np.arange(0, 10)\n",
+ "\n",
+ "pmf_pois = np.array([poisson_pmf(k, mu) for k in x])\n",
+ "pmf_bin = np.array([binomial_pmf(k,n,p) for k in x])\n",
+ "\n",
+ "plt.bar(x, pmf_pois, label='poisson', alpha=0.5)\n",
+ "plt.bar(x, pmf_bin, label='binomial', alpha=0.5)\n",
+ "plt.xlabel('x')\n",
+ "plt.ylabel('P(X=x)')\n",
+ "plt.title('Poisson vs Binomial')\n",
+ "plt.legend()\n",
+ "plt.grid(True)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0fbbdae1-01a7-4ad1-a099-31fc9436527c",
+ "metadata": {},
+ "source": [
+ "ii) $n=3.10^5$, $p=10^{-6}$"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "n = int(3e5)\n",
+ "p = int(1e-6)\n",
+ "mu = 0.3\n",
+ "\n",
+ "x = np.arange(0, 10)\n",
+ "\n",
+ "pmf_pois = np.array([poisson_pmf(k, mu) for k in x])\n",
+ "pmf_bin = np.array([binomial_pmf(k,n,p) for k in x])\n",
+ "\n",
+ "plt.bar(x, pmf_pois, label='poisson', alpha=0.5)\n",
+ "plt.bar(x, pmf_bin, label='binomial', alpha=0.5)\n",
+ "plt.xlabel('x')\n",
+ "plt.ylabel('P(X=x)')\n",
+ "plt.title('Poisson vs Binomial')\n",
+ "plt.legend()\n",
+ "plt.grid(True)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "32ea2406-6b1e-48b1-bc93-eca11119ba64",
+ "metadata": {},
+ "source": [
+ "**Question 2:**"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "X = np.array([1, 2, 3, 4, 5, 6, 7, 8])\n",
+ "P = np.array([2/24, 3/24, 1/24, 1/24, 5/24, 1/24, 2/24, 9/24])\n",
+ "\n",
+ "means = []\n",
+ "for _ in range(10000):\n",
+ " samples = np.random.choice(X, size=10000, p=P)\n",
+ " mean = np.mean(samples)\n",
+ " means.append(mean)\n",
+ "\n",
+ "# Plot histogram\n",
+ "plt.hist(means, bins=30, edgecolor='black')\n",
+ "plt.title('Histogram of Means')\n",
+ "plt.xlabel('Mean')\n",
+ "plt.ylabel('Frequency')\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "98af8aba-893d-4822-8d40-0a47bba58c5b",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "mean = 5.5\n",
+ "std_dev = 0.02483\n",
+ "\n",
+ "x = np.linspace(mean - 3 * std_dev, mean + 3 * std_dev, 1000)\n",
+ "pdf = 1/(std_dev * np.sqrt(2 * np.pi)) * np.exp(-(x - mean)**2 / (2 * std_dev**2))\n",
+ "\n",
+ "plt.plot(x, pdf)\n",
+ "plt.title(\"Normal Distribution with Mean = {} and Standard Deviation = {}\".format(mean, std_dev))\n",
+ "plt.xlabel('Value')\n",
+ "plt.ylabel('Density')\n",
+ "plt.grid(True)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c9305103-5f57-4459-aa01-b4b60dd53e29",
+ "metadata": {},
+ "source": [
+ "the experimentally obatained graph is nearly similar to the CLT obtained graph,the mean and variance are \n",
+ "nearly the same for both the graphs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1ab391b5-fa99-4bc4-8593-c7d392a850b1",
+ "metadata": {},
+ "source": [
+ "**Question 5:**"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Function and its gradient\n",
+ "def f(x1, x2):\n",
+ " return 9*x1**2 + 4*x2**2 + 36*x1*x2 + 9*x1 + 4*x2 + 10\n",
+ "\n",
+ "def grad_f(x1, x2):\n",
+ " dfdx1 = 18*x1 + 36*x2 + 9\n",
+ " dfdx2 = 8*x2 + 36*x1 + 4\n",
+ " return np.array([dfdx1, dfdx2])\n",
+ "\n",
+ "# Gradient Descent\n",
+ "def gradient_descent(starting_point, learning_rate, num_iterations):\n",
+ " x = starting_point\n",
+ " points = [x]\n",
+ " for i in range(num_iterations):\n",
+ " grad = grad_f(x[0], x[1])\n",
+ " x = x - learning_rate * grad\n",
+ " points.append(x)\n",
+ " return np.array(points)\n",
+ "\n",
+ "# Parameters\n",
+ "starting_point = np.array([5.0, 5.0])\n",
+ "num_iterations = 60\n",
+ "\n",
+ "# Learning rates\n",
+ "critical = 0.04 # Adjust this value based on eigenvalue calculation if necessary\n",
+ "zero = 0.00001\n",
+ "outside = 3.0\n",
+ "\n",
+ "# Perform gradient descent for each learning rate\n",
+ "crit_gd = gradient_descent(starting_point, critical, num_iterations)\n",
+ "zero_gd = gradient_descent(starting_point, zero, num_iterations)\n",
+ "max_gd = gradient_descent(starting_point, outside, num_iterations)\n",
+ "\n",
+ "# Plotting the results\n",
+ "plt.figure(figsize=(12, 8))\n",
+ "\n",
+ "if len(crit_gd) > 0:\n",
+ " plt.plot(crit_gd[:, 0], crit_gd[:, 1], label='Critical')\n",
+ " plt.scatter(crit_gd[:, 0], crit_gd[:, 1])\n",
+ "\n",
+ "# Adjust the axis limits\n",
+ "plt.xlabel('$x_1$')\n",
+ "plt.ylabel('$x_2$')\n",
+ "plt.title('Gradient Descent Trajectories')\n",
+ "plt.legend()\n",
+ "plt.grid()\n",
+ "plt.show()\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "76b0502a-02ff-48ec-9239-a493481fc37e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(12, 8))\n",
+ "if len(zero_gd) > 0:\n",
+ " plt.plot(zero_gd[:, 0], zero_gd[:, 1], label='Approaching Zero',color='green')\n",
+ " plt.scatter(zero_gd[:, 0], zero_gd[:, 1],color='green')\n",
+ " \n",
+ "# Adjust the axis limits\n",
+ "plt.xlabel('$x_1$')\n",
+ "plt.ylabel('$x_2$')\n",
+ "plt.title('Gradient Descent Trajectories')\n",
+ "plt.legend()\n",
+ "plt.grid()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "id": "16a55cf1-22ad-4854-84a2-6ef436db4dcd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(12, 8))\n",
+ "if len(max_gd) > 0:\n",
+ " plt.plot(max_gd[:, 0], max_gd[:, 1], label='Beyond Stability',color='red')\n",
+ " plt.scatter(max_gd[:, 0], max_gd[:, 1],color='red')\n",
+ "\n",
+ "# Adjust the axis limits\n",
+ "plt.xlabel('$x_1$')\n",
+ "plt.ylabel('$x_2$')\n",
+ "plt.title('Gradient Descent Trajectories')\n",
+ "plt.legend()\n",
+ "plt.grid()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "58525232-1527-468e-addf-682ba0c9ec0f",
+ "metadata": {},
+ "source": [
+ "**Question 2:**"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "from scipy.optimize import minimize\n",
+ "import matplotlib.pyplot as plt\n",
+ "from scipy.stats import norm\n",
+ "\n",
+ "true_mu = 7\n",
+ "true_sigma = 3\n",
+ "data = np.random.normal(true_mu, true_sigma, 1000)\n",
+ "\n",
+ "def neg_log_likelihood(params, data):\n",
+ " mu, sigma = params[0], params[1]\n",
+ " if sigma <= 0: \n",
+ " return np.inf\n",
+ " n = len(data)\n",
+ " ll = -0.5 * n * np.log(2 * np.pi) - n * np.log(sigma) - (1 / (2 * sigma ** 2)) * np.sum((data - mu) ** 2)\n",
+ " return -ll \n",
+ "\n",
+ "guess = [0, 1]\n",
+ "result = minimize(neg_log_likelihood, guess, args=(data,), bounds=[(None, None), (1e-6, None)])\n",
+ "mle_mu, mle_sigma = result.x\n",
+ "\n",
+ "print(f\"MLE Estimates:\\nmu = {mle_mu}\\nsigma = {mle_sigma}\\n\")\n",
+ "\n",
+ "x = np.linspace(min(data),max(data),1000)\n",
+ "original_pmf = norm.pdf(x,true_mu,true_sigma)\n",
+ "estimated_pmf = norm.pdf(x,mle_mu,mle_sigma)\n",
+ "plt.plot(x,original_pmf,label='Original Distribution',alpha=0.5)\n",
+ "plt.plot(x,estimated_pmf,label='ML Estimated distribution',alpha=0.5)\n",
+ "plt.xlabel('x') \n",
+ "plt.ylabel('P(X=x)') \n",
+ "plt.title('Maximum Likelihood Estimate') \n",
+ "plt.legend() \n",
+ "plt.grid(True) \n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "656c8f12-bd54-4dc5-b2e6-16d49d5b685c",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " Unnamed: 0 \n",
+ " data \n",
+ " para \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 324 \n",
+ " 324 \n",
+ " 4.265624 \n",
+ " -8.283114e-12 \n",
+ " \n",
+ " \n",
+ " 7763 \n",
+ " 7763 \n",
+ " 4.265821 \n",
+ " -8.278843e-12 \n",
+ " \n",
+ " \n",
+ " 2153 \n",
+ " 2153 \n",
+ " 4.266086 \n",
+ " -8.269104e-12 \n",
+ " \n",
+ " \n",
+ " 1148 \n",
+ " 1148 \n",
+ " 4.264565 \n",
+ " -8.266170e-12 \n",
+ " \n",
+ " \n",
+ " 6781 \n",
+ " 6781 \n",
+ " 4.264327 \n",
+ " -8.253822e-12 \n",
+ " \n",
+ " \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " ... \n",
+ " \n",
+ " \n",
+ " 6555 \n",
+ " 6555 \n",
+ " 4.688447 \n",
+ " 1.098229e-02 \n",
+ " \n",
+ " \n",
+ " 2652 \n",
+ " 2652 \n",
+ " 4.692392 \n",
+ " 1.098726e-02 \n",
+ " \n",
+ " \n",
+ " 4927 \n",
+ " 4927 \n",
+ " 4.691943 \n",
+ " 1.098852e-02 \n",
+ " \n",
+ " \n",
+ " 6579 \n",
+ " 6579 \n",
+ " 4.690387 \n",
+ " 1.098927e-02 \n",
+ " \n",
+ " \n",
+ " 7552 \n",
+ " 7552 \n",
+ " 4.691109 \n",
+ " 1.098962e-02 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
8000 rows × 3 columns
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "class LinearRegression:\n",
+ " def __init__(self):\n",
+ " self.weights = None\n",
+ " \n",
+ " def fit(self, X, y):\n",
+ " X_b = np.c_[np.ones((X.shape[0], 1)), X]\n",
+ " self.weights = np.linalg.inv(X_b.T @ X_b) @ X_b.T @ y\n",
+ " \n",
+ " def predict(self, X):\n",
+ " X_b = np.c_[np.ones((X.shape[0], 1)), X]\n",
+ " return X_b @ self.weights\n",
+ "\n",
+ "np.random.seed(42)\n",
+ "X = 2 * np.random.rand(100, 1)\n",
+ "y = 4 + 3 * X + np.random.randn(100, 1)\n",
+ "\n",
+ "model = LinearRegression()\n",
+ "model.fit(X, y)\n",
+ "\n",
+ "a=np.arange(0,2.5)\n",
+ "plt.scatter(X,y,color='red')\n",
+ "plt.plot(a,model.predict(a),color='blue')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "29492ef2-83ac-41a9-a517-7606e2bbfed1",
+ "metadata": {},
+ "source": [
+ "parameterizing $\\sigma$ can be useful when errors are more widely distributed, it provides more acuurate uncertainity estimates but the cons of parameterizing $\\sigma$ is that the normal equation for calculating weights cannot be used and a recursive optimization method should be used which increases complexity, it may also lead to overfitting."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f06beafd-ee4a-408e-bf18-f427a77f2035",
+ "metadata": {},
+ "source": [
+ "**Question 2:**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "51c33724-092e-4b24-9985-65f44b13c523",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "A_train=pd.read_csv(\"train_set_A.csv\")\n",
+ "A_test=pd.read_csv(\"test_set_A.csv\")\n",
+ "B_train=pd.read_csv(\"train_set_B.csv\")\n",
+ "B_test=pd.read_csv(\"test_set_B.csv\")\n",
+ "C_train=pd.read_csv(\"train_set_C.csv\")\n",
+ "C_test=pd.read_csv(\"test_set_C.csv\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "1afa84d3-6f94-4470-b240-e7896003262f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "LinearRegression() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. LinearRegression() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. LinearRegression() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. "
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig,axs=plt.subplots(3,3,figsize=(15,15))\n",
+ "datasets=[[aa_err,ab_err,ac_err],[ba_err,bb_err,bc_err],[ca_err,cb_err,cc_err]]\n",
+ "dict={0:\"A\",1:\"B\",2:\"C\"}\n",
+ "for i in {0,1,2}:\n",
+ " for j in {0,1,2}:\n",
+ " ax=axs[i,j]\n",
+ " ax.hist(datasets[i][j],bins=30,alpha=0.75,color=\"blue\",edgecolor=\"black\")\n",
+ " ax.set_title(f\"model {dict[i]}, test {dict[j]}\")\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f675030b-2056-40db-ad54-0e376b660cfc",
+ "metadata": {},
+ "source": [
+ "**Question 3:**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "be8da1f3-b503-4c67-8bfc-5e6ad36d29a3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "n_samples=100\n",
+ "Xclass0=np.random.randn(n_samples,2)+np.array([-2,-2])\n",
+ "Xclass1=np.random.randn(n_samples,2)+np.array([2,2])\n",
+ "X=np.vstack((Xclass0,Xclass1))\n",
+ "Y=np.hstack((np.zeros(n_samples),np.ones(n_samples)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "id": "92158016-86d5-496b-b9fb-e303b19a8a7f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def g(z):\n",
+ " return 1/(1+np.exp(-z))\n",
+ "def loglikelihood(x,y,theta):\n",
+ " z = np.dot(x, theta)\n",
+ " z = np.clip(g(z), 1e-15, 1-1e-15)\n",
+ " logl = np.mean(y*np.log(z)+(1-y)*np.log(1-z))\n",
+ " return logl\n",
+ "def pred(x,theta):\n",
+ " return g(np.dot(x,theta))\n",
+ "def pred_bin(x,theta):\n",
+ " z=g(np.dot(x,theta))\n",
+ " return (z>=0.5).astype(int)\n",
+ "def batchgd(x,y,theta,n_iter=100):\n",
+ " alpha=0.1\n",
+ " errors=[]\n",
+ " for i in range(0,n_iter):\n",
+ " theta=(theta+alpha*np.dot((y-pred(x,theta)),x))\n",
+ " error=loglikelihood(x,y,theta)\n",
+ " errors.append(error)\n",
+ " return theta,errors\n",
+ "def newton_method(x, y,theta, n_iter=50):\n",
+ " errors = []\n",
+ " for i in range(0,n_iter):\n",
+ " predictions = pred(x,theta)\n",
+ " error = loglikelihood(x, y, theta)\n",
+ " errors.append(error)\n",
+ " gradient = -np.dot((y-pred(x,theta)),x)\n",
+ " w = np.diag(predictions*(1-predictions))\n",
+ " hessian = np.dot(np.dot(x.T, w), x)\n",
+ " theta = theta-np.dot(np.linalg.inv(hessian), gradient)\n",
+ " return theta, errors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "9e9184a7-da67-4c3a-85a1-77467543d5c5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "accuracy using gradient descent: 0.995 with run-time: 0.012635231018066406\n",
+ "accuracy using newton method: 0.995 with run-time: 0.02735447883605957\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.metrics import accuracy_score\n",
+ "import time\n",
+ "theta=np.array([1,1])\n",
+ "start_time_gd = time.time() \n",
+ "weights_gd, errors_gd = batchgd(X, Y,theta)\n",
+ "end_time_gd = time.time()\n",
+ "run_time_gd = end_time_gd-start_time_gd\n",
+ "theta=np.array([1,1])\n",
+ "start_time_nm = time.time()\n",
+ "weights_nm, errors_nm = newton_method(X, Y,theta)\n",
+ "end_time_nm = time.time()\n",
+ "run_time_nm = end_time_nm-start_time_nm\n",
+ "pred_gd = pred_bin(X, weights_gd)\n",
+ "pred_nm = pred_bin(X, weights_nm)\n",
+ "accuracy_gd = accuracy_score(Y, pred_gd)\n",
+ "accuracy_nm = accuracy_score(Y, pred_nm)\n",
+ "print(f'accuracy using gradient descent: {accuracy_gd} with run-time: {run_time_gd}')\n",
+ "print(f'accuracy using newton method: {accuracy_nm} with run-time: {run_time_nm}')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "id": "dcdc5e96-a7e9-4597-bac2-08ff6ff847d9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "x1 = np.arange(0, 100)\n",
+ "x2 = np.arange(0, 50)\n",
+ "plt.plot(x1, errors_gd, color='red', label='loglikelihood function using gd')\n",
+ "plt.plot(x2, errors_nm, color='blue', label='loglikelihood function using nm')\n",
+ "plt.xlabel('iterations')\n",
+ "plt.ylabel('cost function')\n",
+ "plt.legend()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "58be64e0-48ac-4212-bae0-11928d73566b",
+ "metadata": {},
+ "source": [
+ "as we can see from the plot that newtons method reaches a higher value much quicker but consumes more time and as features increase the time taken will go higher because the complexity of calculating the inverse of the hessian gets more and more complex but for only 2 features newtons method is much better than batch gradient descent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5053a2ac-bdce-46ec-9436-47099bde55a1",
+ "metadata": {},
+ "source": [
+ "**Question 4:**"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mean Squared Error: 67.36738622079729\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "from sklearn.linear_model import PoissonRegressor\n",
+ "from sklearn.metrics import mean_squared_error\n",
+ "\n",
+ "np.random.seed(42)\n",
+ "n = 100\n",
+ "x = np.random.rand(n) * 10 # predictor\n",
+ "beta_0 = 1\n",
+ "beta_1 = 0.5\n",
+ "log_lambda = beta_0 + beta_1 * x\n",
+ "lambda_ = np.exp(log_lambda)\n",
+ "y = np.random.poisson(lambda_)\n",
+ "\n",
+ "data = pd.DataFrame({'x': x, 'y': y})\n",
+ "\n",
+ "plt.scatter(x, y, alpha=0.5)\n",
+ "plt.xlabel('x')\n",
+ "plt.ylabel('y')\n",
+ "plt.title('Generated Poisson Data')\n",
+ "plt.show()\n",
+ "\n",
+ "model = PoissonRegressor()\n",
+ "model.fit(x.reshape(-1, 1), y)\n",
+ "\n",
+ "y_pred = model.predict(x.reshape(-1, 1))\n",
+ "\n",
+ "mse = mean_squared_error(y, y_pred)\n",
+ "print(f'Mean Squared Error: {mse}')\n",
+ "\n",
+ "plt.scatter(x, y, alpha=0.5, label='Observed')\n",
+ "plt.scatter(x, y_pred, color='red', alpha=0.5, label='Predicted')\n",
+ "plt.xlabel('x')\n",
+ "plt.ylabel('y')\n",
+ "plt.title('Poisson Regression Fit')\n",
+ "plt.legend()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bcb97615-c87c-4cff-bc67-353d8b55dcae",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.12.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
From a07fc757c6d03777a6d4f953ee36bc4c08f71213 Mon Sep 17 00:00:00 2001
From: Sathvik Duddela <147434853+r4iden-IITR@users.noreply.github.com>
Date: Fri, 26 Jul 2024 19:29:52 +0530
Subject: [PATCH 6/6] Asn5(3,4,5)
---
Assignment_5_(Q3,4,5).ipynb | 702 ++++++++++++++++++++++++++++++++++++
1 file changed, 702 insertions(+)
create mode 100644 Assignment_5_(Q3,4,5).ipynb
diff --git a/Assignment_5_(Q3,4,5).ipynb b/Assignment_5_(Q3,4,5).ipynb
new file mode 100644
index 0000000..2feee01
--- /dev/null
+++ b/Assignment_5_(Q3,4,5).ipynb
@@ -0,0 +1,702 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "**Question 3:**"
+ ],
+ "metadata": {
+ "id": "T755viVhUrPu"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "8nlCMzEGEtTh"
+ },
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "from torchvision import datasets, transforms\n",
+ "from torch.utils.data import Dataset,DataLoader\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "transform = transforms.Compose([\n",
+ " transforms.ToTensor(),\n",
+ " transforms.Normalize((0.1307,), (0.3081,))\n",
+ "])"
+ ],
+ "metadata": {
+ "id": "F9ABnIbTKSoi"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "train_dataset = datasets.MNIST(root='./data', train=True, transform=transform, download=True)\n",
+ "test_dataset = datasets.MNIST(root='./data', train=False, transform=transform, download=True)"
+ ],
+ "metadata": {
+ "id": "tEkTLRv3KVNL"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
+ "test_loader = DataLoader(test_dataset, batch_size=1000, shuffle=False)"
+ ],
+ "metadata": {
+ "id": "10OwTCfiKya1"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import random\n",
+ "random_index=random.randint(0,len(train_dataset)-1)\n",
+ "image,label=train_dataset[random_index]\n",
+ "print(image.shape)"
+ ],
+ "metadata": {
+ "id": "aWXiUq5qLJVA",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "a71012cf-5a7e-4552-ea07-1b6f6582eb92"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "torch.Size([1, 28, 28])\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import torch.nn as nn\n",
+ "class neural_net(nn.Module):\n",
+ " def __init__(self):\n",
+ " super(neural_net,self).__init__()\n",
+ " self.fc1=nn.Linear(784,128)\n",
+ " self.fc2=nn.Linear(128,64)\n",
+ " self.fc3=nn.Linear(64,10)\n",
+ "\n",
+ " def forward(self,x):\n",
+ " x=x.view(-1,784)\n",
+ " x=self.fc1(x)\n",
+ " x=torch.relu(x)\n",
+ " x=torch.relu(self.fc2(x))\n",
+ " x=self.fc3(x)\n",
+ " return x\n",
+ " def backward(self,loss):\n",
+ " loss.backward()\n",
+ "\n",
+ "import torch.optim as optim\n",
+ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+ "model = neural_net().to(device)\n",
+ "criterion = nn.CrossEntropyLoss()\n",
+ "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)\n",
+ "\n",
+ "def train(epochs):\n",
+ " for i in range(epochs):\n",
+ " model.train()\n",
+ " for idx,(data,target) in enumerate(train_loader):\n",
+ " data,target=data.to(device),target.to(device)\n",
+ " optimizer.zero_grad()\n",
+ " output=model(data)\n",
+ " loss=criterion(output,target)\n",
+ " model.backward(loss)\n",
+ " optimizer.step()\n",
+ " if idx % 500 == 0:\n",
+ " print(f\"Epoch {i+1}/{epochs}, Batch {idx+1}/{len(train_loader)}, Loss: {loss.item()}\")\n",
+ " return model\n",
+ "\n",
+ "def test_random_sample():\n",
+ " model.eval()\n",
+ "\n",
+ " idx = random.randint(0, len(test_dataset) - 1)\n",
+ " image, true_label = test_dataset[idx]\n",
+ "\n",
+ " plt.imshow(image.squeeze(), cmap='gray')\n",
+ " plt.title(f\"True Label: {true_label}\")\n",
+ " plt.axis('off')\n",
+ " plt.show()\n",
+ "\n",
+ " image = image.unsqueeze(0).to(device)\n",
+ " with torch.no_grad():\n",
+ " output = model(image)\n",
+ "\n",
+ " _, predicted_class = torch.max(output, 1)\n",
+ "\n",
+ " probabilities = torch.nn.functional.softmax(output, dim=1)\n",
+ "\n",
+ " print(f\"Predicted Label: {predicted_class.item()}\")\n",
+ " print(\"Class Probabilities:\")\n",
+ " for i, prob in enumerate(probabilities[0]):\n",
+ " print(f\"Class {i}: {prob.item()*100:.2f}%\")\n"
+ ],
+ "metadata": {
+ "id": "xATYxnXAF-bg"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "train(epochs=5)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "H2Upo9ubBV0c",
+ "outputId": "d52a1a25-0e8d-4f6e-df59-30a75c921804"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Epoch 1/5, Batch 1/938, Loss: 2.299408197402954\n",
+ "Epoch 1/5, Batch 501/938, Loss: 0.09671914577484131\n",
+ "Epoch 2/5, Batch 1/938, Loss: 0.21627262234687805\n",
+ "Epoch 2/5, Batch 501/938, Loss: 0.1175546944141388\n",
+ "Epoch 3/5, Batch 1/938, Loss: 0.11789850145578384\n",
+ "Epoch 3/5, Batch 501/938, Loss: 0.0226764939725399\n",
+ "Epoch 4/5, Batch 1/938, Loss: 0.022747565060853958\n",
+ "Epoch 4/5, Batch 501/938, Loss: 0.021300004795193672\n",
+ "Epoch 5/5, Batch 1/938, Loss: 0.020878547802567482\n",
+ "Epoch 5/5, Batch 501/938, Loss: 0.045971985906362534\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "neural_net(\n",
+ " (fc1): Linear(in_features=784, out_features=128, bias=True)\n",
+ " (fc2): Linear(in_features=128, out_features=64, bias=True)\n",
+ " (fc3): Linear(in_features=64, out_features=10, bias=True)\n",
+ ")"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 14
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "test_random_sample()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 636
+ },
+ "id": "2j9l14kuBedk",
+ "outputId": "5359a11b-536c-4d61-a2d8-a3636fe557bf"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": "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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Predicted Label: 3\n",
+ "Class Probabilities:\n",
+ "Class 0: 0.00%\n",
+ "Class 1: 0.00%\n",
+ "Class 2: 0.00%\n",
+ "Class 3: 100.00%\n",
+ "Class 4: 0.00%\n",
+ "Class 5: 0.00%\n",
+ "Class 6: 0.00%\n",
+ "Class 7: 0.00%\n",
+ "Class 8: 0.00%\n",
+ "Class 9: 0.00%\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "**Question 4:**"
+ ],
+ "metadata": {
+ "id": "W-f5WTmxdnuS"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "x=np.arange(0,2*np.pi,0.1)\n",
+ "y=np.sin(x)\n",
+ "\n",
+ "x_val=np.concatenate((np.linspace(-1*np.pi,0,1000),np.linspace(2*np.pi,3*np.pi,1000)))\n",
+ "y_val=np.sin(x_val)"
+ ],
+ "metadata": {
+ "id": "l3MZVNHiREJJ"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.optim as optim\n",
+ "class SimpleNN(nn.Module):\n",
+ " def __init__(self,activation_function):\n",
+ " super(SimpleNN,self).__init__()\n",
+ " self.activation=activation_function\n",
+ " self.fc1=nn.Linear(1,64)\n",
+ " self.fc2=nn.Linear(64,1)\n",
+ "\n",
+ " def forward(self,x):\n",
+ " x=self.fc1(x)\n",
+ " x=self.activation(x)\n",
+ " x=self.fc2(x)\n",
+ " return x\n",
+ "\n",
+ "activation_functions={\"ReLU\":nn.ReLU(),\"GeLU\":nn.GELU(),\"SiLU\":nn.SiLU(),\"LeakyReLU\":nn.LeakyReLU(),\"ELU\":nn.ELU()}\n",
+ "networks = {name: SimpleNN(act_func) for name, act_func in activation_functions.items()}\n",
+ "\n",
+ "def train(network,x_train,y_train,x_val,y_val,epochs=1000,lr=0.01):\n",
+ " criterion=nn.MSELoss()\n",
+ " optimizer=optim.Adam(network.parameters(),lr=lr)\n",
+ " training_loss = []\n",
+ " validation_loss = []\n",
+ "\n",
+ " for i in range(epochs):\n",
+ " network.train()\n",
+ " optimizer.zero_grad()\n",
+ " output=network(x_train)\n",
+ " loss=criterion(output,y_train)\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ " training_loss.append(loss.item())\n",
+ " network.eval()\n",
+ " with torch.no_grad():\n",
+ " output=network(x_val)\n",
+ " val_loss=criterion(output,y_val)\n",
+ " validation_loss.append(loss.item())\n",
+ "\n",
+ " return training_loss,validation_loss"
+ ],
+ "metadata": {
+ "id": "1dD0ff_pXnxA"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "results={}\n",
+ "x_train=torch.Tensor(x).unsqueeze(1)\n",
+ "y_train=torch.Tensor(y).unsqueeze(1)\n",
+ "x_val=torch.Tensor(x_val).unsqueeze(1)\n",
+ "y_val=torch.Tensor(y_val).unsqueeze(1)\n",
+ "for name,network in networks.items():\n",
+ " training_loss,validation_loss=train(network,x_train,y_train,x_val,y_val,epochs=1000,lr=0.01)\n",
+ " results[name]={\"Network\":network,\"train_loss\":training_loss,\"val_loss\":validation_loss}\n",
+ "\n",
+ "sorted_results = sorted(results.items(), key=lambda x: min(x[1]['val_loss']))\n",
+ "\n",
+ "print(\"Order of activation functions by minimum validation loss:\")\n",
+ "for name, result in sorted_results:\n",
+ " print(f\"{name}: {min(result['val_loss'])}\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "mPisC36qOjvc",
+ "outputId": "3513c547-4d5b-477b-aa76-c69b1213d0f3"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Order of activation functions by minimum validation loss:\n",
+ "GeLU: 1.1101047675765585e-05\n",
+ "SiLU: 4.428025567904115e-05\n",
+ "LeakyReLU: 0.0001383751950925216\n",
+ "ELU: 0.0001823971833800897\n",
+ "ReLU: 0.00030360042001120746\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "fig, axs = plt.subplots(3, 2, figsize=(10, 10))\n",
+ "axs = axs.ravel()\n",
+ "\n",
+ "x_plot = np.linspace(-np.pi, 3 * np.pi, 1000)\n",
+ "y_true = np.sin(x_plot)\n",
+ "\n",
+ "x_train=x_train.reshape(-1)\n",
+ "y_train=y_train.reshape(-1)\n",
+ "x_val=x_val.reshape(-1)\n",
+ "y_val=y_val.reshape(-1)\n",
+ "for i, (name, result) in enumerate(results.items()):\n",
+ " network = result['Network']\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " y_pred = network(torch.Tensor(x_plot).unsqueeze(1)).numpy().flatten()\n",
+ "\n",
+ " axs[i].plot(x_train, y_train, 'b', label='Training data')\n",
+ " axs[i].plot(x_val, y_val, 'g', label='Validation data')\n",
+ " axs[i].plot(x_plot, y_true, 'k--', label='True function',color='yellow')\n",
+ " axs[i].plot(x_plot, y_pred, 'r', label='Prediction')\n",
+ " axs[i].set_title(f'{name} Activation')\n",
+ " axs[i].legend()\n",
+ " axs[i].set_xlabel('x')\n",
+ " axs[i].set_ylabel('f(x)')\n",
+ "\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "jgcFswHRWOD-",
+ "outputId": "dfe003b9-5fd6-45f7-d7f2-32455adb9c11"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ ":19: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"k--\" (-> color='k'). The keyword argument will take precedence.\n",
+ " axs[i].plot(x_plot, y_true, 'k--', label='True function',color='yellow')\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": "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\n"
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "smoother activation functions like GELU or SiLU can perform better in approximating continuous functions like sine due to their non-linearity therefore, they perform better than ReLU or LeakyReLU and ELU"
+ ],
+ "metadata": {
+ "id": "DpSQIlUkcwHT"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "**Question 5:**"
+ ],
+ "metadata": {
+ "id": "VEBSDj2vfVY8"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.optim as optim\n",
+ "import torchvision\n",
+ "import torchvision.transforms as transforms\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "class Cifar10CNN(nn.Module):\n",
+ " def __init__(self, use_batchnorm=False):\n",
+ " super(Cifar10CNN, self).__init__()\n",
+ " self.use_batchnorm = use_batchnorm\n",
+ " self.conv1 = nn.Conv2d(3,16,3,padding=1)\n",
+ " self.conv2 = nn.Conv2d(16,32,3,padding=1)\n",
+ "\n",
+ " if use_batchnorm:\n",
+ " self.bn1 = nn.BatchNorm2d(16)\n",
+ " self.bn2 = nn.BatchNorm2d(32)\n",
+ "\n",
+ " self.fc = nn.Linear(32*8*8,10)\n",
+ "\n",
+ " def forward(self,x):\n",
+ " x = self.conv1(x)\n",
+ " if self.use_batchnorm:\n",
+ " x = self.bn1(x)\n",
+ " x = nn.functional.relu(x)\n",
+ " x = nn.functional.max_pool2d(x,2)\n",
+ " x = self.conv2(x)\n",
+ " if self.use_batchnorm:\n",
+ " x = self.bn2(x)\n",
+ " x = nn.functional.relu(x)\n",
+ " x = nn.functional.max_pool2d(x, 2)\n",
+ " x = x.view(-1,32*8*8)\n",
+ " x = self.fc(x)\n",
+ " return x\n",
+ "\n",
+ "device=torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+ "\n",
+ "transform=transforms.Compose([\n",
+ " transforms.ToTensor(),\n",
+ " transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))\n",
+ "])\n",
+ "\n",
+ "trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)\n",
+ "trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)\n",
+ "\n",
+ "def train_model(model, epochs=5, lr=0.001, momentum=0.9):\n",
+ " criterion = nn.CrossEntropyLoss()\n",
+ " optimizer = optim.SGD(model.parameters(), lr=lr, momentum=momentum)\n",
+ "\n",
+ " model.to(device)\n",
+ "\n",
+ " for epoch in range(epochs):\n",
+ " running_loss = 0.0\n",
+ " for i, data in enumerate(trainloader, 0):\n",
+ " inputs, labels = data\n",
+ " inputs, labels = inputs.to(device), labels.to(device)\n",
+ "\n",
+ " optimizer.zero_grad()\n",
+ " outputs = model(inputs)\n",
+ " loss = criterion(outputs, labels)\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ "\n",
+ " running_loss += loss.item()\n",
+ " if i % 200 == 199:\n",
+ " print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 200:.3f}')\n",
+ " running_loss = 0.0\n",
+ "\n",
+ " print('Finished Training')\n",
+ "\n",
+ "model_without_bn = Cifar10CNN(use_batchnorm=False)\n",
+ "train_model(model_without_bn)\n",
+ "model_with_bn = Cifar10CNN(use_batchnorm=True)\n",
+ "train_model(model_with_bn)"
+ ],
+ "metadata": {
+ "id": "gIC8Q-TMcJQ-",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "7e789eff-7c60-4981-d10b-59fc3cca244c"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 170498071/170498071 [00:01<00:00, 94852669.76it/s] \n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
+ "[1, 200] loss: 2.243\n",
+ "[1, 400] loss: 2.037\n",
+ "[1, 600] loss: 1.873\n",
+ "[2, 200] loss: 1.731\n",
+ "[2, 400] loss: 1.685\n",
+ "[2, 600] loss: 1.601\n",
+ "[3, 200] loss: 1.524\n",
+ "[3, 400] loss: 1.490\n",
+ "[3, 600] loss: 1.468\n",
+ "[4, 200] loss: 1.405\n",
+ "[4, 400] loss: 1.365\n",
+ "[4, 600] loss: 1.355\n",
+ "[5, 200] loss: 1.309\n",
+ "[5, 400] loss: 1.293\n",
+ "[5, 600] loss: 1.267\n",
+ "Finished Training\n",
+ "[1, 200] loss: 1.731\n",
+ "[1, 400] loss: 1.440\n",
+ "[1, 600] loss: 1.323\n",
+ "[2, 200] loss: 1.198\n",
+ "[2, 400] loss: 1.149\n",
+ "[2, 600] loss: 1.134\n",
+ "[3, 200] loss: 1.053\n",
+ "[3, 400] loss: 1.041\n",
+ "[3, 600] loss: 1.024\n",
+ "[4, 200] loss: 0.970\n",
+ "[4, 400] loss: 0.958\n",
+ "[4, 600] loss: 0.983\n",
+ "[5, 200] loss: 0.923\n",
+ "[5, 400] loss: 0.920\n",
+ "[5, 600] loss: 0.918\n",
+ "Finished Training\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "def get_activation_maps(model, input_image):\n",
+ " model.eval()\n",
+ " x = input_image.unsqueeze(0).to(device)\n",
+ "\n",
+ " conv1_out = model.conv1(x)\n",
+ " if model.use_batchnorm:\n",
+ " conv1_out = model.bn1(conv1_out)\n",
+ " conv1_act = nn.functional.relu(conv1_out)\n",
+ " conv2_out = model.conv2(nn.functional.max_pool2d(conv1_act, 2))\n",
+ " if model.use_batchnorm:\n",
+ " conv2_out = model.bn2(conv2_out)\n",
+ " conv2_act = nn.functional.relu(conv2_out)\n",
+ "\n",
+ " return conv1_out, conv1_act, conv2_out, conv2_act\n",
+ "\n",
+ "def plot_activations(conv1_out, conv1_act, conv2_out, conv2_act, use_batchnorm=False):\n",
+ " fig, axs = plt.subplots(2, 4, figsize=(20, 10))\n",
+ "\n",
+ " axs[0, 0].imshow(conv1_out[0, 0].cpu().detach().numpy())\n",
+ " axs[0, 0].set_title('Conv1 Output')\n",
+ " axs[0, 1].hist(conv1_out.cpu().detach().numpy().flatten(), bins=50)\n",
+ " axs[0, 1].set_title('Conv1 Output Distribution')\n",
+ "\n",
+ " axs[0, 2].imshow(conv1_act[0, 0].cpu().detach().numpy())\n",
+ " axs[0, 2].set_title('Conv1 Activation')\n",
+ " axs[0, 3].hist(conv1_act.cpu().detach().numpy().flatten(), bins=50)\n",
+ " axs[0, 3].set_title('Conv1 Activation Distribution')\n",
+ "\n",
+ " axs[1, 0].imshow(conv2_out[0, 0].cpu().detach().numpy())\n",
+ " axs[1, 0].set_title('Conv2 Output')\n",
+ " axs[1, 1].hist(conv2_out.cpu().detach().numpy().flatten(), bins=50)\n",
+ " axs[1, 1].set_title('Conv2 Output Distribution')\n",
+ "\n",
+ " axs[1, 2].imshow(conv2_act[0, 0].cpu().detach().numpy())\n",
+ " axs[1, 2].set_title('Conv2 Activation')\n",
+ " axs[1, 3].hist(conv2_act.cpu().detach().numpy().flatten(), bins=50)\n",
+ " axs[1, 3].set_title('Conv2 Activation Distribution')\n",
+ "\n",
+ " plt.tight_layout()\n",
+ " plt.suptitle(f'Activations {\"with\" if use_batchnorm else \"without\"} BatchNorm', fontsize=16)\n",
+ " plt.show()\n",
+ "\n",
+ "dataiter = iter(trainloader)\n",
+ "images, _ = next(dataiter)\n",
+ "input_image = images[2]\n",
+ "\n",
+ "conv1_out, conv1_act, conv2_out, conv2_act = get_activation_maps(model_without_bn, input_image)\n",
+ "plot_activations(conv1_out, conv1_act, conv2_out, conv2_act, use_batchnorm=False)\n",
+ "\n",
+ "conv1_out, conv1_act, conv2_out, conv2_act = get_activation_maps(model_with_bn, input_image)\n",
+ "plot_activations(conv1_out, conv1_act, conv2_out, conv2_act, use_batchnorm=True)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "bzt74rHU3qmt",
+ "outputId": "bb7ee2cc-fb5c-4595-d01b-297b6b6441b6"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": "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\n"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "image/png": "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\n"
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "BatchNorm works beacuse it make the network less sensitive to the size of the weights of the previous layer, and since it can hande larger weights the learning rate can be increased too, hence speeding up the training. Also the activation patterns are more pronounced as observable."
+ ],
+ "metadata": {
+ "id": "NVdEa-Pa6mN6"
+ }
+ }
+ ]
+}
\ No newline at end of file