From 6211892f0954bf10c4321764e43aee4f2f821cfa Mon Sep 17 00:00:00 2001 From: Priyanka <82670475+Priyankam20@users.noreply.github.com> Date: Mon, 6 Jun 2022 03:51:58 +0530 Subject: [PATCH 1/6] Add files via upload --- Assignment 1/A1_200731.ipynb | 533 +++++++++++++++++++++++++++++++++++ 1 file changed, 533 insertions(+) create mode 100644 Assignment 1/A1_200731.ipynb diff --git a/Assignment 1/A1_200731.ipynb b/Assignment 1/A1_200731.ipynb new file mode 100644 index 0000000..b6ccf0f --- /dev/null +++ b/Assignment 1/A1_200731.ipynb @@ -0,0 +1,533 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "A1_200731.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "### **Aim** \n", + "The motive of this assignment is to make predictions using **Linear Regression**. To make sure you truly understand how the underlying algorithm works, you are to implement it from scratch." + ], + "metadata": { + "id": "RB2d1J1f1CF7" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Generating the dataset \n", + "Run the cell below to create the dataset. It further splits the available data into training and testing. Please do not edit this cell.\n" + ], + "metadata": { + "id": "a_S80lf6H4Xv" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn import datasets\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Generate the data\n", + "X, y = datasets.make_regression(n_samples=100, n_features=5, noise=20, random_state=4)\n", + "\n", + "# Split the data\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234)" + ], + "metadata": { + "id": "yX0zqXcHIQHP" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Visualizing the data \n", + "Use `matplotlib` to visualize the given data." + ], + "metadata": { + "id": "Zj4rrRXGJBXy" + } + }, + { + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "\n", + "plt.plot(X_train,y_train,'*','MarkerSize',10)\n", + "\n", + "# Your code here" + ], + "metadata": { + "id": "zxfi8dkBJOUi", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 369 + }, + "outputId": "d057b03e-b5bc-461c-d21e-7be1f5991f00" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "metadata": {}, + "execution_count": 10 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "You should be able to see the linear relations between `y` and the features in vector `X`." + ], + "metadata": { + "id": "r7vndSBAJceF" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Gradient Descent Review \n", + "1. #### Cost function\n", + "Define the `cost function` to measure the difference between predictions and target outputs. Here, we are working with first degree polynomial, so derivatives are easy to calculate. ( Linear function `y = wx +b` ) \n", + "\n", + "$$Error = \\frac{1}{N}\\sum_{i=1}^N (y_i - \\overline{y}_i)^2 = \\frac{1}{N}\\sum_{i=1}^N (y_i - (x_iw+b))^2 $$ \n", + "\n", + " where `N` is the number of samples \n", + " \n", + "\n", + "\n", + "2. #### Compute the derivative\n", + "$$\\frac{\\delta Error}{\\delta w} = \\frac{2}{N}\\sum_{i=1}^N -x_i(y_i -(m x_i +b )) $$\n", + "$$\\frac{\\delta Error}{\\delta b} = \\frac{2}{N}\\sum_{i=1}^N -(y_i -(m x_i +b )) $$\n", + "3.

Update current parameters

\n", + "$$ w:= w- learning\\_rate \\cdot \\frac{\\delta Error}{\\delta w} $$ \n", + "$$ b:= b- learning\\_rate \\cdot \\frac{\\delta Error}{\\delta b} $$ \n", + "4.

Repeat until it fits good enough

\n" + ], + "metadata": { + "id": "b4I9Z3epNvBM" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Model definition\n", + "\n", + "Complete the functions in the class below. Hints provided at appropriate places." + ], + "metadata": { + "id": "kBtUcOVnJu-I" + } + }, + { + "cell_type": "code", + "source": [ + "X" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7VwBQu6Su8Iu", + "outputId": "d70bfdb9-7da4-4d6e-b5eb-3273bc140d5b" + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[ 1.58928579e+00, -2.43864781e-01, -9.53657453e-01,\n", + " -6.10570973e-01, 1.59709397e+00],\n", + " [ 1.91728485e+00, -5.80595172e-01, -1.94573993e-01,\n", + " -6.80116241e-01, -1.09591600e+00],\n", + " [ 4.86030067e-02, -3.74444917e-01, 2.09313488e-01,\n", + " 1.88646203e-01, 9.54698597e-01],\n", + " [ 2.02240507e+00, 8.70841779e-01, -1.54292905e+00,\n", + " -5.64875297e-01, -1.75210527e-01],\n", + " [ 1.01355006e+00, 7.97023134e-01, -1.85293455e+00,\n", + " 4.59581779e-01, -9.17741045e-01],\n", + " [ 2.96304983e-01, -7.23081483e-01, -2.43670919e+00,\n", + " -1.18048659e-01, -9.61613938e-01],\n", + " [-2.73972085e-01, -1.19688147e+00, 1.02608401e+00,\n", + " -9.33043173e-01, 8.58741057e-01],\n", + " [-2.55604366e-01, 4.24876682e-01, -1.56073345e-01,\n", + " 1.59447516e-01, -1.69217314e+00],\n", + " [-3.16299786e-01, 5.33567293e-01, 2.82206663e-01,\n", + " -2.29291305e+00, 5.74612568e-01],\n", + " [-1.50226745e+00, 1.10246309e+00, -3.32110906e-01,\n", + " -3.37095149e-01, -2.35725003e-01],\n", + " [-6.51225833e-01, -2.35807363e+00, 4.80062472e-02,\n", + " 5.42451308e-01, -1.10558404e+00],\n", + " [ 7.82975562e-01, -7.80654326e-01, 1.54768490e+00,\n", + " 6.93483618e-01, -1.20372540e-01],\n", + " [ 3.50997153e-01, 7.23341609e-01, 1.54697933e+00,\n", + " -6.06887283e-01, 4.61355672e-02],\n", + " [-4.01176818e-01, -6.95396909e-01, -1.49585236e+00,\n", + " 8.95104377e-01, -1.19411441e-01],\n", + " [ 1.80833313e-01, -6.76900945e-01, 8.50386871e-01,\n", + " 3.02902035e-01, -1.12511459e+00],\n", + " [-6.51840227e-01, 6.22221839e-01, 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1.91525157e+00, -1.17895602e+00, 8.40333927e-02,\n", + " 2.18584815e+00, 1.15605606e+00],\n", + " [ 4.22848467e-01, -2.76809158e-01, 3.27939113e-01,\n", + " -3.34539354e-01, 1.13384332e+00],\n", + " [-1.45246737e-01, 1.07763433e+00, 2.20356402e+00,\n", + " 5.08073300e-01, 1.61724121e+00],\n", + " [-1.90001834e-01, 1.67290643e+00, 1.96871939e+00,\n", + " 1.74544451e+00, -3.95259451e-01],\n", + " [ 5.05617071e-02, 6.93598508e-01, -9.95908931e-01,\n", + " 4.99951333e-01, -4.18301520e-01],\n", + " [ 7.99755537e-01, 9.81710663e-01, -1.87373844e+00,\n", + " 1.06569977e+00, -2.35967116e-01]])" + ] + }, + "metadata": {}, + "execution_count": 20 + } + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "class LinearRegression:\n", + "\n", + " # The __init__ is called when we make any object of our class. Here, you are to specify the default values for \n", + " # Learning Rate, Number of Iterations, Weights and Biases. It doesn't return anything.\n", + " # Hint: Google what a `self pointer` is and figure out how it can be used here.\n", + " def __init__(self, learning_rate=0.001, n_iters=1000):\n", + " # Your code here \n", + " self.rate = learning_rate\n", + " self.iters = n_iters\n", + " self.weights = None\n", + " self.bias = None\n", + "\n", + "\n", + " # pass # Uncomment this when you're done with this function\n", + "\n", + "\n", + " # The following function would be the heart of the model. This is where the training would happen. \n", + " # You're supposed to iterate and keep on updating the weights and biases according to the steps of Gradient Descent.\n", + " def fit(self, X, y):\n", + " n_samples = 100\n", + " n_features = 5\n", + " \n", + "\n", + "\n", + " self.weights = np.zeros(n_features)\n", + " self.bias = 0\n", + "\n", + " for _ in range(self.iters):\n", + " # Gradient Descent code goes here\n", + " y_predicted = np.dot(X, self.weights) + self.bias\n", + " # compute gradients\n", + " dw = (1/n_samples)* np.dot(X.T, (y_predicted - y))\n", + " db = (1/n_samples)* np.sum(y_predicted - y)\n", + "\n", + " # update parameters\n", + " self.weights -= self.rate * dw\n", + " self.bias -= self.rate * db\n", + "\n", + " # pass # Uncomment this when you're done with this function\n", + " \n", + " \n", + " # This function will be called after our model has been trained and we are predicting on unseen data\n", + " # What is our prediction? Just return that\n", + " def predict(self, X):\n", + " # Code goes here\n", + " y_approximated = np.dot(X, self.weights) + self.bias\n", + " return y_approximated\n", + "\n", + " # pass # Uncomment this when you're done with this function" + ], + "metadata": { + "id": "dGnFNPJx3I28" + }, + "execution_count": 23, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Initializing, Training & Predictions" + ], + "metadata": { + "id": "EvyInkTKPn7W" + } + }, + { + "cell_type": "code", + "source": [ + "# Now, we make an object of our custom class.\n", + "regressor = LinearRegression(learning_rate=0.01, n_iters=1000) # You may pass the custom parameters or let the default values take it ahead\n", + "\n", + "regressor. fit(X_train, y_train) # Call the fit method on the object to train (pass appropriate part of dataset)\n", + "\n", + "\n", + "predictions = regressor.predict(X_test) # pass appropriate part of dataset" + ], + "metadata": { + "id": "nvItUpAkHTiv" + }, + "execution_count": 24, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Evaluate the model \n", + "\n", + "Return [Mean Squared Error](https://en.wikipedia.org/wiki/Mean_squared_error) & [R2 Score](https://www.ncl.ac.uk/webtemplate/ask-assets/external/maths-resources/statistics/regression-and-correlation/coefficient-of-determination-r-squared.html#:~:text=%C2%AFy) from the functions below." + ], + "metadata": { + "id": "tzK6cq8eRD4Q" + } + }, + { + "cell_type": "code", + "source": [ + "def mean_squared_error(y_true, y_pred):\n", + " return np.mean((y_true - y_pred)**2)\n", + " # return the mean squared error\n", + " # pass # Uncomment this when you're done with this function\n", + "\n", + "\n", + "def r2_score(y_true, y_pred):\n", + " d1 = np.sum((y_true - y_pred)**2)\n", + " m = np.mean(y_true)\n", + " d2 = np.sum((y_true - m)**2)\n", + " return 1 - (d1/d2)\n", + "\n", + " \n", + "\n", + " \n", + " # return the r2 score\n", + " # pass # Uncomment this when you're done with this function\n", + " \n", + "\n", + "mse = mean_squared_error(y_test, predictions) # Pass appropriate parts of dataset\n", + "print(\"MSE:\", mse)\n", + "\n", + "accu = r2_score(y_test, predictions) # Pass appropriate parts of dataset\n", + "print(\"Accuracy:\", accu)" + ], + "metadata": { + "id": "WqkrvDzcRF5m", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d25115ac-87c0-48c5-d4fd-25eee6c33d29" + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "MSE: 389.5184798939019\n", + "Accuracy: 0.9631753931003519\n" + ] + } + ] + } + ] +} \ No newline at end of file From fa559c8dac69b9ab34ad418b317941c199d6d97c Mon Sep 17 00:00:00 2001 From: Priyanka <82670475+Priyankam20@users.noreply.github.com> Date: Thu, 16 Jun 2022 23:57:53 +0530 Subject: [PATCH 2/6] Create Assignment 2 --- Assignment 2 | 1 + 1 file changed, 1 insertion(+) create mode 100644 Assignment 2 diff --git a/Assignment 2 b/Assignment 2 new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/Assignment 2 @@ -0,0 +1 @@ + From 2bbeb882f5dd50a1c4ba4ee6395c6043d5403d3c Mon Sep 17 00:00:00 2001 From: Priyanka <82670475+Priyankam20@users.noreply.github.com> Date: Fri, 17 Jun 2022 00:07:20 +0530 Subject: [PATCH 3/6] Delete Assignment 2 --- Assignment 2 | 1 - 1 file changed, 1 deletion(-) delete mode 100644 Assignment 2 diff --git a/Assignment 2 b/Assignment 2 deleted file mode 100644 index 8b13789..0000000 --- a/Assignment 2 +++ /dev/null @@ -1 +0,0 @@ - From 833c10554ce24716fb34d7e8f171afa80055dc1d Mon Sep 17 00:00:00 2001 From: Priyanka <82670475+Priyankam20@users.noreply.github.com> Date: Fri, 17 Jun 2022 00:08:23 +0530 Subject: [PATCH 4/6] Add files via upload --- Assignment 2/A2_200731.ipynb | 1218 ++++++++++++++++++++++++++++++++++ 1 file changed, 1218 insertions(+) create mode 100644 Assignment 2/A2_200731.ipynb diff --git a/Assignment 2/A2_200731.ipynb b/Assignment 2/A2_200731.ipynb new file mode 100644 index 0000000..1124f3a --- /dev/null +++ b/Assignment 2/A2_200731.ipynb @@ -0,0 +1,1218 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Copy of Copy of CVusingTF_Assgn2.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Assigment 2: Deep Learning" + ], + "metadata": { + "id": "UxcaEbrCy1g_" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Generate Dataset\n", + "\n", + "This is the same code from Assignment 1" + ], + "metadata": { + "id": "h2JON-_Oy79w" + } + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "hgpG3WDuypfa" + }, + "outputs": [], + "source": [ + "from sklearn import datasets\n", + "from sklearn.model_selection import train_test_split\n", + "import pandas as pd\n", + "\n", + "# Generate the data\n", + "X, y = datasets.make_regression(n_samples=100, n_features=5, noise=5, random_state=4)\n", + "\n", + "# Split the data\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234)\n", + "# X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=1234)\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Visualize Dataset\n", + "This is the same code from Assignment 1" + ], + "metadata": { + "id": "r6it-Rm7zD1Y" + } + }, + { + "cell_type": "code", + "source": [ + "X,y" + ], + "metadata": { + "id": "23KFS5FODVcM", + "outputId": "f7e4623b-cdb8-439a-ba58-730beb2f99f7", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(array([[ 1.58928579e+00, -2.43864781e-01, -9.53657453e-01,\n", + " -6.10570973e-01, 1.59709397e+00],\n", + " [ 1.91728485e+00, -5.80595172e-01, -1.94573993e-01,\n", + " -6.80116241e-01, -1.09591600e+00],\n", + " [ 4.86030067e-02, -3.74444917e-01, 2.09313488e-01,\n", + " 1.88646203e-01, 9.54698597e-01],\n", + " [ 2.02240507e+00, 8.70841779e-01, -1.54292905e+00,\n", + " -5.64875297e-01, -1.75210527e-01],\n", + " [ 1.01355006e+00, 7.97023134e-01, -1.85293455e+00,\n", + " 4.59581779e-01, -9.17741045e-01],\n", + " [ 2.96304983e-01, -7.23081483e-01, -2.43670919e+00,\n", + " -1.18048659e-01, -9.61613938e-01],\n", + " [-2.73972085e-01, -1.19688147e+00, 1.02608401e+00,\n", + " -9.33043173e-01, 8.58741057e-01],\n", + " [-2.55604366e-01, 4.24876682e-01, -1.56073345e-01,\n", + " 1.59447516e-01, -1.69217314e+00],\n", + " [-3.16299786e-01, 5.33567293e-01, 2.82206663e-01,\n", + " -2.29291305e+00, 5.74612568e-01],\n", + " [-1.50226745e+00, 1.10246309e+00, -3.32110906e-01,\n", + " -3.37095149e-01, -2.35725003e-01],\n", + " [-6.51225833e-01, -2.35807363e+00, 4.80062472e-02,\n", + " 5.42451308e-01, -1.10558404e+00],\n", + " [ 7.82975562e-01, 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-1.87373844e+00,\n", + " 1.06569977e+00, -2.35967116e-01]]),\n", + " array([ 49.3327374 , 59.77779093, -3.97866834, 110.7712023 ,\n", + " 18.15793487, -165.58082993, -55.38495192, 6.7212803 ,\n", + " 0.63666213, -48.44622303, -201.32245986, 87.80628427,\n", + " 147.44792225, -151.71703589, 10.67978579, 61.58053338,\n", + " -22.13186259, 45.65271688, -151.95715438, -67.12608522,\n", + " 8.3158662 , -60.71131568, -138.64468958, 185.19651124,\n", + " 104.52220628, 50.5725045 , 69.49849865, 24.22069865,\n", + " -261.69829439, -100.80831078, -56.43670239, -55.96612341,\n", + " 230.857693 , 68.95790292, 189.44519815, -131.1390209 ,\n", + " 154.13555914, 109.32390875, 115.18382672, -139.33799971,\n", + " 215.47124758, 86.93433383, 153.57904985, 63.74218869,\n", + " -13.06099427, 94.31332848, -140.27898471, 50.82164171,\n", + " 83.90986194, 96.97832597, -148.76680488, 131.80583036,\n", + " 77.62589294, -76.25566607, 125.28851133, -5.41637619,\n", + " 57.63151087, 50.98632157, 10.27959057, 130.17425384,\n", + " -251.32606443, -140.76311462, 125.82914403, 29.65951354,\n", + " 181.34630306, -72.62741436, 83.47168195, -33.47350747,\n", + " -59.29081497, 82.33372406, 23.74122976, 86.26391892,\n", + " 34.6548773 , 9.63994037, -24.47910094, -198.67180146,\n", + " -17.15015968, -6.33117392, -40.76481194, -34.55413166,\n", + " 158.87671249, 79.87403933, -66.93162171, -43.88613954,\n", + " 143.37824889, 34.92501227, -17.55746273, 80.93091525,\n", + " 81.3623049 , -117.42276195, -8.03459148, 85.7751066 ,\n", + " -83.17756515, -41.13668036, 80.53740813, 34.89637518,\n", + " 198.17702362, 231.78809445, -7.91882959, 37.18411463]))" + ] + }, + "metadata": {}, + "execution_count": 3 + } + ] + }, + { + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.scatter(X[:,0],y)\n", + "\n", + "# Your code here" + ], + "metadata": { + "id": "UautPVj1yzaQ", + "outputId": "92b4f9a2-3903-417d-d24f-9cb4a5636725", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 282 + } + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 4 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" 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\n" 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\n" 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miZPTsM/vE+9+g+5fuITAj8aQ9slcl3LBqVe0NHFyyvXz69LYTF8R/DugK7nK1Mvlmjo5tfX5hQrYOYzNYDzSPkhG6uVyOafYQo5XpD42g8kQ/JGM1FMJqZ+cRgkZsFMfm8FkSPsgCeNSCamcGHJNsYUM2KmPzWAyBH8kYVzPlBxzPdMG7LKTbupjM5gMaR8kYVTPtGrKIrebarRhmvGKYeMEkrJNf+E0ev5IwqieaZWUBZUo5aYpKR110mUOQv4I/kjCqFTCsBUvy1IWXVvrKKSq4xUM7HYbwR9TCT0AO65nOmmOmYAVDgO73UbwR2VNpVaG9UyrpCxSCFipVCaVqdI2Bna7jeCPymKkViZNWcQOWCmPOVRtWxNLT8Q6MaZ8Qo6F4I/KUk6txF4rJ+Uxh2naFnJeQ6wTY8on5JgI/qgshdTKKDEnYqV8YozdtlgnxpRPyDFR54/KQq9x06W6/JTvr1CnbSE+o1gnn9gnvVQR/FFZyDVuunYP4pQXf5u2baE+o1gnxpRPyDGR9sFUQqVW6l6SpzaQF3vMYZRp2xYqbRJrMD52EUCqCP6Iqs4leaoDeSkv/jZN20KlTWKdGFM+IcdE8EdUdQaPUxvIS+0qJJSQA/yxTowpn5BjIeePqOrkyFMayOva2MWglMcxMD2CP6KqM3ic0kDesKuQD935/eyrmHK+iQ2GI+2D6Ka9JE9pIG/Y1cZJd0npjEdMi7RJ99DzR7ZS6pFOcrXBfW6REnr+yFoqPdKyq5AyfZ9YhHQQ/IEAVpcTnmV2KuUzqO8Ti5AOgj8QyOBVyOo5CBIVMkgLwR9oABOLkLpawd/M3iXpY5J+R9JF7r448NiNkq6RdFLSX7n7vmL7dkn/KGlG0r+7+646bUBcXZ3YFEIq4xFAmbo9/0clXSnpXwc3mtkWSVdJeq2k8yTdY2avKR6+RdJbJR2W9KCZ7XH3x2q2AxGkurwCgPFqlXq6++PuXla7tkPSHe7+a3d/StJBSRcVXwfd/Ul3f17SHcVzkaFRyysASFtTdf5zkp4e+PlwsW3Y9hcxs2vNbNHMFo8cOdJQM1FHSssrAKhmbPA3s3vM7NGSr0Z77O5+q7vPu/v8unXrmnwrTCml5RUAVDM25+/ul07xukuSNgz8vL7YphHbkZmUllcAUE1TaZ89kq4ys5ea2SZJmyV9V9KDkjab2SYzO1vLg8J7GmoDGpbS8goAqqlb6vlOSf8kaZ2kvWb2sLtf5u4HzOxOSY9JOiHpOnc/Wfyf6yXt03Kp523ufqDWHiAqyhkxDUqE4zMvmYKemvn5eV9cXBz/RADJGzb7mavG8MzsIXefL3uMVT0BtIoS4TQQ/AG0ihLhNBD8AbSKEuE0EPwBtIp7AqeBVT2BKVCtMj1WPE0DwR+oiAXt6qNEOD7SPkBFVKugC+j545QYqYwc0yexqlXKjpVE+gTTIfhDUpxURq7pk/Nm12qpJNA3Wa1Sdqxu+I/vSyYdP+mntuVw/JAG0j6QFCeVkWv6JEa1StmxOv6Cnwr8K3I4fkgDPX9IipPKyHWyT4xqlSrHJPXjhzQQ/CEpTiojxnuGErpaZdzYx7BjVSaH44f4SPtAUpxUBpN9lq3k85eOHpPrdO5+9/7Tt7ooO1ZrzjKtmbEzttU9frv3L2nbrvu0aWGvtu2674w2oF1Nfxb0/CEpTiqDyT7LRo19rByLYceqbNu0xy/XAfguauOzYElnILJNC3tV9ldokp7adUVr7di2677S1NLc7Frdv3BJa+1AuM+CJZ2BhKWy0FmuA/Bd1MZnQfAHIktl7COVkxDa+SwI/kAh1mBnKvdCTuUkhHY+CwZ8AcUf7ExhoTMG4NPRxmfBgC8gBjvRTQz4AmMw2Im+IfgDYrAT/UPwB8RgJ/qHAV9ADHaifwj+QCGFihugLaR9AKCHCP4A0EMEfwDoIYI/APQQwR8AeojgDwA9VCv4m9nNZvZDM3vEzL5kZrMDj91oZgfN7Akzu2xg+/Zi20EzW6jz/kAOuDUiUlS353+3pNe5++sl/UjSjZJkZlskXSXptZK2S/pnM5sxsxlJt0i6XNIWSVcXzwU6aZL7807zmpxMUFet4O/uX3f3E8WPD0haX3y/Q9Id7v5rd39K0kFJFxVfB939SXd/XtIdxXOBThp1f95pNHEyQT+FzPm/X9LXiu/nJD098NjhYtuw7S9iZtea2aKZLR45ciRgM4H2hF4tNPTJBP01Nvib2T1m9mjJ146B53xU0glJt4dqmLvf6u7z7j6/bt26UC8LtCr0aqEsPY1Qxq7t4+6XjnrczN4n6e2S3uKn7wyzJGnDwNPWF9s0YjvQOTdcdsEZdwiT6q0Wet7s2tKbzrD0NKqqW+2zXdKHJb3D3Z8beGiPpKvM7KVmtknSZknflfSgpM1mtsnMztbyoPCeOm0AUhb6/rwsPY1Q6q7q+SlJL5V0t5lJ0gPu/hfufsDM7pT0mJbTQde5+0lJMrPrJe2TNCPpNnc/ULMNQNJCrhbK0tMIhXv4AkBHcQ9fAMAZCP4A0EMEfwDoIYI/APQQwR8AeojgDwA9RPAHgB4i+ANADxH8AaCHCP4A0EMEfwDoIYI/APRQ3VU9gUbt3r/ECpZAAwj+SNbK/WpXboSycr9aSZwAgJpI+yBZ3K8WaA7BH8nifrVAc0j7IFncr7Y6xkgwKXr+SBb3q61mZYxk6egxuU6PkezevxS7aUgQwR/JCn3z865jjARVkPZB0kLe/LzrGCNBFfT8gY4YNhbCGAnKEPyBjmCMBFWQ9gE6YiU9RrUPJkHwBzqEMRJMirQPAPQQwR8AeojgDwA9RPAHgB4i+ANAD5m7x27DWGZ2RNKh2O0onCvpZ7EbERH73+/9lzgGOe3/b7n7urIHsgj+KTGzRXefj92OWNj/fu+/xDHoyv6T9gGAHiL4A0APEfyruzV2AyJj/9H3Y9CJ/SfnDwA9RM8fAHqI4A8APUTwr8jMbjazH5rZI2b2JTObjd2mtpnZu8zsgJm9YGbZl7xNysy2m9kTZnbQzBZit6dtZnabmT1rZo/GbksMZrbBzL5hZo8Vv/8fiN2mOgj+1d0t6XXu/npJP5J0Y+T2xPCopCslfTN2Q9piZjOSbpF0uaQtkq42sy1xW9W6z0raHrsREZ2Q9CF33yLpzZKuy/l3gOBfkbt/3d1PFD8+IGl9zPbE4O6Pu3vf7gp+kaSD7v6kuz8v6Q5JOyK3qVXu/k1J/xu7HbG4+zPu/r3i+19JelxStjdPIPjX835JX4vdCLRiTtLTAz8fVsZ/+KjHzDZK2irpO3FbMj3u5FXCzO6R9JslD33U3b9cPOejWr4MvL3NtrVlkmMA9JGZvUzSFyV90N1/Gbs90yL4l3D3S0c9bmbvk/R2SW/xjk6UGHcMemhJ0oaBn9cX29AjZrZGy4H/dne/K3Z76iDtU5GZbZf0YUnvcPfnYrcHrXlQ0mYz22RmZ0u6StKeyG1Ci8zMJH1G0uPu/vHY7amL4F/dpyS9XNLdZvawmf1L7Aa1zczeaWaHJf2upL1mti92m5pWDPJfL2mflgf67nT3A3Fb1S4z+4Kkb0u6wMwOm9k1sdvUsm2S3iPpkuJv/2Eze1vsRk2L5R0AoIfo+QNADxH8AaCHCP4A0EMEfwDoIYI/APQQwR8AeojgDwA99P+zvKdUEutrugAAAABJRU5ErkJggg==\n" 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Model Definition\n", + "\n", + "Using TensorFlow, build a model with the following definition:\n", + "> Input of shape 5 \\\\\n", + "> Dense of shape 5 \\\\\n", + "> Dense of shape 5 \\\\\n", + "> Dense of shape 1 \\\\\n", + "\n", + "Use Mean Square Error Loss and Stochaistic Gradient Descent (SGD) Optimizer\n", + "\n", + "Use Gradient Decay with appropriate parameters" + ], + "metadata": { + "id": "XMXb9lTyzGHE" + } + }, + { + "cell_type": "code", + "source": [ + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense\n", + "from tensorflow.keras.optimizers import SGD\n", + "\n", + "model = Sequential()\n", + "model.add(Dense(5,input_dim = 5,activation ='relu'))\n", + "model.add(Dense(5,input_dim = 5,activation ='relu'))\n", + "model.add(Dense(1,activation ='linear'))\n", + "opt = SGD(lr =0.03,momentum =0.9)\n", + "model.compile(loss = 'mean_squared_error', optimizer =opt)\n", + "\n", + "\n", + "\n", + "history = model.fit(X_train,y_train,validation_data = (X_test,y_test),epochs = 50, verbose =0)\n", + "train_loss =model.evaluate(X_train,y_train,verbose =0)\n", + "test_loss = model.evaluate(X_test,y_test,verbose =0)\n", + "print('Train: %.3f, Test: %.3f' % (train_loss, test_loss))\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "# Your code here" + ], + "metadata": { + "id": "r32N1xK2ziOs", + "outputId": "c5477e5a-6fcb-4e53-e19b-c748c45f7a5d", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n", + " super(SGD, self).__init__(name, **kwargs)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train: 11345.927, Test: 10813.325\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Plot Loss\n", + "\n", + "Using matplotlib visualise how the loss (both validation and training) is changing, use this information to retrain the model with appropriate parameters.
We ideally want the loss to be constant over the last few iterations." + ], + "metadata": { + "id": "jmeP6vt3z0oA" + } + }, + { + "cell_type": "code", + "source": [ + "plt.title('Loss / Mean Squared Error')\n", + "plt.plot(history.history['loss'], label='training')\n", + "plt.plot(history.history['val_loss'], label='validation')\n", + "\n", + "plt.legend()\n", + "plt.show()\n" + ], + "metadata": { + "id": "e-pqXPy7JYHx", + "outputId": "95af1761-e47c-4524-c3b4-46f7ba9b1d1f", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 281 + } + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "prediction = model.predict(X_test)\n" + ], + "metadata": { + "id": "3sUoNbh0W9Rk", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "2209aadf-90f7-48b2-b7e4-a1864d064d88" + }, + "execution_count": 71, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[-14.916588],\n", + " [-14.916588],\n", + " [-14.916588],\n", + " ...,\n", + " [-14.916588],\n", + " [-14.916588],\n", + " [-14.916588]], dtype=float32)" + ] + }, + "metadata": {}, + "execution_count": 71 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Evaluation Metrics\n", + "Use the R2 Score function implemented in the first assignment to evaluate the performance of the model." + ], + "metadata": { + "id": "IVrR_vXA7kOt" + } + }, + { + "cell_type": "code", + "source": [ + "# Insert the function for R2 Score\n", + "import numpy as np\n", + "def r2_score(y_true, y_pred):\n", + " d1 = np.sum((y_true - y_pred)**2)\n", + " m = np.mean(y_true)\n", + " d2 = np.sum((y_true - m)**2)\n", + " return 1 - (d1/d2)\n", + " accuracy = r2_score(y_test,prediction)\n", + " print('accuracy')" + ], + "metadata": { + "id": "-lOHpD8-7ggm" + }, + "execution_count": 72, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Your own custom model\n", + "Build a custom model of your own choice.
\n", + "Describe it in detail in Markdown/Latex in the cell below.
\n", + "Visualise the loss, as before." + ], + "metadata": { + "id": "CHqzF1OU0pBg" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn import datasets\n", + "from sklearn.model_selection import train_test_split\n", + "import pandas as pd\n", + "\n", + "# Generate the data\n", + "X, y = datasets.make_regression(n_samples=100, n_features=5, noise=5, random_state=4)\n", + "\n", + "# Split the data\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234)" + ], + "metadata": { + "id": "sqIQRrXit3nF" + }, + "execution_count": 43, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Your text here" + ], + "metadata": { + "id": "jF8oTUqq0y0g" + } + }, + { + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.scatter(X[:,0],y)" + ], + "metadata": { + "id": "aS0ROZa402Lo", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 282 + }, + "outputId": "8ccaae48-9de5-46fc-c783-2c5d65b9e4d4" + }, + "execution_count": 45, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 45 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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\n" 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\n" 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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense\n" + ], + "metadata": { + "id": "xVG8HDtWtpe3" + }, + "execution_count": 56, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model = Sequential()\n", + "model.add(Dense(10,input_dim = 5,activation ='relu'))\n", + "model.add(Dense(10,input_dim = 5,activation ='relu'))\n", + "model.add(Dense(1,activation ='linear'))\n", + "opt = SGD(lr =0.03,momentum =0.9)\n", + "model.compile(loss = 'mean_squared_error', optimizer =opt)\n", + "\n", + "\n", + "\n", + "history = model.fit(X_train,y_train,validation_data = (X_test,y_test),epochs = 20, verbose =0)\n", + "train_loss =model.evaluate(X_train,y_train,verbose =0)\n", + "test_loss = model.evaluate(X_test,y_test,verbose =0)\n", + "print('Train: %.3f, Test: %.3f' % (train_loss, test_loss))\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3PThAdv1th4w", + "outputId": "0f7be175-7527-4aeb-cbaf-54a04aa1eada" + }, + "execution_count": 64, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/gradient_descent.py:102: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n", + " super(SGD, self).__init__(name, **kwargs)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Train: 14002.473, Test: 14144.952\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "plt.title('Loss / Mean Squared Error')\n", + "plt.plot(history.history['loss'], label='training')\n", + "plt.plot(history.history['val_loss'], label='validation')\n", + "\n", + "plt.legend()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 281 + }, + "id": "58z-4EHstmVW", + "outputId": "0ab827ec-9b8c-4c62-9b30-db469ab0522d" + }, + "execution_count": 65, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "prediction = model.predict(X_test)\n" + ], + "metadata": { + "id": "9Gi7rERTtYSQ" + }, + "execution_count": 74, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "def r2_score(y_true, y_pred):\n", + " d1 = np.sum((y_true - y_pred)**2)\n", + " m = np.mean(y_true)\n", + " d2 = np.sum((y_true - m)**2)\n", + " return 1 - (d1/d2)\n", + " accuracy = r2_score(y_test,prediction)\n", + " \n", + " " + ], + "metadata": { + "id": "KGGIHo5CtcQC" + }, + "execution_count": 73, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "\n", + "\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mJJCpqDYpPzE", + "outputId": "c83d1222-3988-4e08-e8aa-1b9284bbb7fc" + }, + "execution_count": 69, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "accuracy\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "" + ], + "metadata": { + "id": "rx1N1DgNpRvT" + }, + "execution_count": 37, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "" + ], + "metadata": { + "id": "4i83-SeWpRzf" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "" + ], + "metadata": { + "id": "lfPc_7mHpR3i" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "" + ], + "metadata": { + "id": "ygxevXC_pR69" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "" + ], + "metadata": { + "id": "QsEou2c5pR-A" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "" + ], + "metadata": { + "id": "ank-OQIfpSAv" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "" + ], + "metadata": { + "id": "uhzLNj6lpSDz" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "" + ], + "metadata": { + "id": "7oyJZTs7pSHD" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "" + ], + "metadata": { + "id": "qBUhHQYXpSLm" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "" + ], + "metadata": { + "id": "2dXBtKsBpSPN" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "" + ], + "metadata": { + "id": "aHLu_LcLpSSK" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "" + ], + "metadata": { + "id": "LDveVe2gpSUs" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 6ade6923e7be2d3299acc4208df6d19f15d6ceb4 Mon Sep 17 00:00:00 2001 From: Priyanka <82670475+Priyankam20@users.noreply.github.com> Date: Thu, 23 Jun 2022 02:58:34 +0530 Subject: [PATCH 5/6] Add files via upload --- Assignment 3/A3_200731.ipynb | 1076 ++++++++++++++++++++++++++++++++++ 1 file changed, 1076 insertions(+) create mode 100644 Assignment 3/A3_200731.ipynb diff --git a/Assignment 3/A3_200731.ipynb b/Assignment 3/A3_200731.ipynb new file mode 100644 index 0000000..22fe97c --- /dev/null +++ b/Assignment 3/A3_200731.ipynb @@ -0,0 +1,1076 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Copy of CV_with_TF-Assignment-3.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU", + "gpuClass": "standard" + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Assignment 3\n", + "\n", + " In this Assignment, we will use CNN to classify digits. \n", + "The `MNIST` database is a large database of handwritten digits that is commonly used for training various image processing systems.\n", + "\n" + ], + "metadata": { + "id": "VGHh_5UYzKpV" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Importing TensorFlow" + ], + "metadata": { + "id": "JnsMbCPNzPAr" + } + }, + { + "cell_type": "code", + "execution_count": 240, + "metadata": { + "id": "HRLTw3cMwvi7" + }, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "import keras\n", + "from tensorflow.keras import Sequential\n", + "from tensorflow.keras.layers import Conv2D\n", + "from tensorflow.keras.layers import MaxPool2D\n", + "from tensorflow.keras.layers import Dense\n", + "from tensorflow.keras.layers import Flatten\n", + "from tensorflow.keras.layers import Dropout\n", + "from tensorflow.keras.utils import to_categorical\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Get the dataset" + ], + "metadata": { + "id": "6Ji7HGpgzSPi" + } + }, + { + "cell_type": "code", + "source": [ + "# Import the dataset\n", + "\n", + "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()" + ], + "metadata": { + "id": "oEW3KDEvzIHL" + }, + "execution_count": 241, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Split the dataset\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "X_train,X_test,Y_train,Y_test=train_test_split(x_test,y_test,test_size=0.2)" + ], + "metadata": { + "id": "F_sRU9dx_mYQ" + }, + "execution_count": 242, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "\n", + "# Pre processing \n", + "\n", + "X_train = X_train/255\n", + "X_test = X_test/255\n", + "X_train.shape\n", + "\n" + ], + "metadata": { + "id": "rbt0WbW6sDVs", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a4102b6a-9ec2-41ba-f504-ea685c8b790c" + }, + "execution_count": 243, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(8000, 28, 28)" + ] + }, + "metadata": {}, + "execution_count": 243 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Visualize the dataset\n", + "Print some images with labels." + ], + "metadata": { + "id": "EVpQheoVqoEG" + } + }, + { + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "for i in range(10):\n", + " plt.imshow(X_train[i],cmap = 'gray')\n", + " plt.title(Y_train[i])\n", + " plt.show()\n", + " # Your code\n", + " \n", + "\n", + " \n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n" + ], + "metadata": { + "id": "yF1Nj63Bz9m7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "1415ed68-0d9b-4e80-f165-fc7ffce77bf1" + }, + "execution_count": 252, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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wbK3K965OX11536oI+xZJR455/pViWU+IiC3F7xFJD6v2saOXvLd7Bt3i90jF/XwuIt6LiE8j4jNJP1aF710xzfhDkn4eEauKxZW/d/X66tb7VkXY10iaZvsY2wdJmifpsQr62IPtScWBE9meJOmb6r2pqB+TtKB4vEDSoxX28gW9Mo132TTjqvi9q3z684jo+o+kuaodkf8fSddV0UNJX38r6eXi59Wqe5O0QrXdur+odmzjO5KmSHpa0puSfiPp8B7q7WeqTe29XrVg9VfU26mq7aKvl/RS8TO36vcu0VdX3jculwUywQE6IBOEHcgEYQcyQdiBTBB2IBOEHcgEYQcy8f++eKtVSoS56AAAAABJRU5ErkJggg==\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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SJklPSJo6Qr39hzpTe7+oTrBmtNTbaeocor8oaW11O7vt967Q11DeN74uCyTBB3RAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kMT/AY0PpEqfTnA+AAAAAElFTkSuQmCC\n" 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G1fB31/jw5xEx8B9JF6q1R36rpH9sooc2fZ0i6b+qn01N9ybpAbVW6z5Ta9/GDyX9maRnJb0t6TeSZg1Rb/+m1tDer6sVrLkN9bZIrVX01yVtqH4ubPq7K/Q1kO+N02WBJNhBByRB2IEkCDuQBGEHkiDsQBKEHUiCsANJ/B/ecrQGXaG2FAAAAABJRU5ErkJggg==\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Plot statistics of the training and testing dataset \n", + "(`x axis`: digits, `y axis`: number of samples corresponding to the digits)" + ], + "metadata": { + "id": "Rx8muKSIrKhe" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "unique , total_count = np.unique(Y_train, return_counts=True)\n", + "plt.bar(unique,total_count)\n", + "plt.title(\"statistics of the training dataset\")\n", + "plt.show()\n", + "\n", + "\n", + "# Your code" + ], + "metadata": { + "id": "37kehTG_6Pi4", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 281 + }, + "outputId": "1b917ecf-520c-4f9d-a3ce-743556ed3990" + }, + "execution_count": 253, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "unique , total_count = np.unique(Y_test, return_counts=True)\n", + "plt.bar(unique,total_count)\n", + "plt.title(\"statistics of the testing dataset\")\n", + "plt.show()\n" + ], + "metadata": { + "id": "uwssasE1NDIt", + "outputId": "6fce66f7-3f4e-4e89-99bd-b041276e3a3d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 281 + } + }, + "execution_count": 254, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "\n", + "\n", + "print(X_train.shape)\n", + "X_test.shape\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Zq9DJoXQ1ZQr", + "outputId": "1b6979cc-460c-41e2-b106-a454ccc60dbb" + }, + "execution_count": 255, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(8000, 28, 28)\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(2000, 28, 28)" + ] + }, + "metadata": {}, + "execution_count": 255 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Model" + ], + "metadata": { + "id": "kWlpCWdAr8d3" + } + }, + { + "cell_type": "code", + "source": [ + "model = Sequential()\n", + "model.add(Conv2D(32, (3, 3), activation='relu',input_shape=(28, 28,1)))\n", + "model.add(Conv2D(64, (3, 3), activation='relu'))\n", + "model.add(MaxPool2D((2, 2)))\n", + "model.add(Dropout(0.25))\n", + "model.add(Flatten())\n", + "model.add(Dense(128, activation='relu'))\n", + "model.add(Dropout(0.5))\n", + "model.add(Dense(10, activation='softmax'))\n" + ], + "metadata": { + "id": "1L07EyQ0Yion" + }, + "execution_count": 256, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model.summary()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UajnXuHX0VOD", + "outputId": "d667ebac-d760-418b-8973-67c2bcd13cc4" + }, + "execution_count": 257, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model: \"sequential_16\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " conv2d_31 (Conv2D) (None, 26, 26, 32) 320 \n", + " \n", + " conv2d_32 (Conv2D) (None, 24, 24, 64) 18496 \n", + " \n", + " max_pooling2d_15 (MaxPoolin (None, 12, 12, 64) 0 \n", + " g2D) \n", + " \n", + " dropout_27 (Dropout) (None, 12, 12, 64) 0 \n", + " \n", + " flatten_14 (Flatten) (None, 9216) 0 \n", + " \n", + " dense_27 (Dense) (None, 128) 1179776 \n", + " \n", + " dropout_28 (Dropout) (None, 128) 0 \n", + " \n", + " dense_28 (Dense) (None, 10) 1290 \n", + " \n", + "=================================================================\n", + "Total params: 1,199,882\n", + "Trainable params: 1,199,882\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "model.compile(optimizer = 'adam' , loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])\n", + "# Compile the model (add optimizers and metrics)\n", + " \n" + ], + "metadata": { + "id": "nKEZ8cbO9JVV" + }, + "execution_count": 258, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "history = model.fit(X_train,Y_train,epochs = 10,validation_split = 0.2)\n", + "# Fit the model on the training data (specify validation_split, read about validation if new to you)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lLT9vX9OC9VF", + "outputId": "958af51b-a70c-45ba-af67-7dfe199245af" + }, + "execution_count": 259, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/10\n", + "200/200 [==============================] - 1s 5ms/step - loss: 0.6123 - accuracy: 0.8061 - val_loss: 0.1687 - val_accuracy: 0.9494\n", + "Epoch 2/10\n", + "200/200 [==============================] - 1s 4ms/step - loss: 0.2192 - accuracy: 0.9334 - val_loss: 0.1010 - val_accuracy: 0.9681\n", + "Epoch 3/10\n", + "200/200 [==============================] - 1s 4ms/step - loss: 0.1375 - accuracy: 0.9553 - val_loss: 0.0854 - val_accuracy: 0.9712\n", + "Epoch 4/10\n", + "200/200 [==============================] - 1s 4ms/step - loss: 0.1004 - accuracy: 0.9677 - val_loss: 0.0880 - val_accuracy: 0.9769\n", + "Epoch 5/10\n", + "200/200 [==============================] - 1s 4ms/step - loss: 0.0790 - accuracy: 0.9750 - val_loss: 0.0761 - val_accuracy: 0.9769\n", + "Epoch 6/10\n", + "200/200 [==============================] - 1s 4ms/step - loss: 0.0697 - accuracy: 0.9772 - val_loss: 0.0766 - val_accuracy: 0.9787\n", + "Epoch 7/10\n", + "200/200 [==============================] - 1s 4ms/step - loss: 0.0573 - accuracy: 0.9823 - val_loss: 0.0761 - val_accuracy: 0.9775\n", + "Epoch 8/10\n", + "200/200 [==============================] - 1s 4ms/step - loss: 0.0533 - accuracy: 0.9842 - val_loss: 0.0632 - val_accuracy: 0.9850\n", + "Epoch 9/10\n", + "200/200 [==============================] - 1s 4ms/step - loss: 0.0442 - accuracy: 0.9852 - val_loss: 0.0570 - val_accuracy: 0.9831\n", + "Epoch 10/10\n", + "200/200 [==============================] - 1s 4ms/step - loss: 0.0428 - accuracy: 0.9852 - val_loss: 0.0674 - val_accuracy: 0.9781\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Plot between accuracy and validation accuracy" + ], + "metadata": { + "id": "gHVzQklSPRhC" + } + }, + { + "cell_type": "code", + "source": [ + "plt.plot(history.history['accuracy'], label = 'accuracy')\n", + "plt.plot(history.history['val_accuracy'],label = 'validation_accuracy')\n", + "plt.xlabel('epoch')\n", + "plt.ylabel('accuracy')\n", + "plt.ylim([0.9,1])\n", + "plt.legend()" + ], + "metadata": { + "id": "fDTLFKPtPOvQ", + "outputId": "7f9e87d8-43b7-4ed2-e44c-26ea35c34496", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 + } + }, + "execution_count": 260, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 260 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Predict some images\n", + "Print the image along with its label (true value) and predicted value." + ], + "metadata": { + "id": "ml1Kna_DuJrL" + } + }, + { + "cell_type": "code", + "source": [ + "x=model.predict(X_test[:36])\n", + "arr=[]\n", + "for i in range(36):\n", + " n=np.where(x[i]==max(x[i]))\n", + " arr.append(n)\n", + "\n", + "for i in range(36):\n", + " \n", + " \n", + " plt.imshow(X_test[i],cmap = 'gray')\n", + " plt.xlabel(f\"{Y_test[i]}->{arr[i][0]}\")\n", + " plt.show()" + ], + "metadata": { + "id": "qAVveBGqRkbR", + "outputId": "aee2fa70-3c41-48d7-b8f2-14f786d08163", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "execution_count": 261, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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1OmNcJOmbanXOKJvvPZLOkbSy2X8B6tDvgR3RZxN1q4yIN9W6UcL4z/2zpMsl/Zukv4yIPW3mu1TSn0taGK1+2BhyhH3y+7xav6lftr2jmHZ3RGw66XPrJH0jIj7ocL6PqDUi7/PF8bwnI+L+OgpGMwj7JBcRv5DUydH1U7qBSETwt3Oa4Tc7OvU/kraOXVRTxvZX1Tpo9799qQod4+YVQBJs2YEkCDuQBGEHkiDsQBL/B6BZxgFPgXuNAAAAAElFTkSuQmCC\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" 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i6Wy17hP4H9ufuQMrWtdYL5R0l+3/lPReu3ViuBD2BCJieUT8WUScLWmPpD1jfu/sb/fPZ/sPbF+j1s0YJ0v6G7Vuzmi3zv+IiG9ExBxJT6p1YxOG2KAHdkQDbH8pInba/iO1Pq9/PSL+4VPz/EzSXEn/KumvI+K1ca7zCEk3SrqjT+2jJoQ9h1W2j1HrdtQlEfFOm3kelPS94ku88fh72xeodXR4T0T8W029ok+4xRXjcjC/umv7HEl/FxEX9LsvjB+f2TFev5W0Zsw38G3Z/q6kZWp9N4Ahwp4dSII9O5AEYQeSIOxAEoQdSOL/AYpRvRqYZ+QgAAAAAElFTkSuQmCC\n" 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\n" 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vf7e4vrCYCvya7R8UHxiR7Tttv237L/v5N6A+wj7FFVOK/1Gdz/qfJ2m57fNOuM2pxScGU/4nIs6PiF8U1++T9E1JZxc/l0lSRPyVpHV9/BPQJ4R96utmSvEMSbtt/5PtxVV3WHy887cj4t+j86msjWpoqi26R9invsopxRFxWNICSU9JutX2f9j+C9szE/d5IHWfaB/CDklSRPxvRDwSEZeoc+T/I0m/OP4aHSc/wj71TTal+JcTvu/sj48XbH/O9ncl/bOkUyR9Q9Lhkvs884T7bM00ZUxu2As7Yvh+PaVYnUBeJekbEfG3x29g+3fU+Zqmc9SZgXVFFPP6JxMRh2y/a/sr6nzTyjWS/mGAfwP6gLBPcRHxge0bJD2uztH6gYjYPclNfyDpqeh+GuSfS3pQ0m9J+pfiBy3GFFd0xfb7EXFal7e9WS34lll8HK/Z0a13J36opoztOyX9iaT/Hk5b6BZHdiATHNmBTBB2IBOEHcgEYQcy8f9E+0Kvff8DDAAAAABJRU5ErkJggg==\n" 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