From 1b738d12a10e4a63c6fe666bdc11f1697f8bf02e Mon Sep 17 00:00:00 2001 From: danijoo Date: Thu, 23 Dec 2021 22:53:21 +0100 Subject: [PATCH] ex 3 --- Exercise3/exercise3.ipynb | 270 ++++++++++++++++++++++++++++++++++---- 1 file changed, 241 insertions(+), 29 deletions(-) diff --git a/Exercise3/exercise3.ipynb b/Exercise3/exercise3.ipynb index 044d363..2984e81 100755 --- a/Exercise3/exercise3.ipynb +++ b/Exercise3/exercise3.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -132,9 +132,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# Randomly select 100 data points to display\n", "rand_indices = np.random.choice(m, 100, replace=False)\n", @@ -158,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -267,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -344,13 +357,20 @@ " y = y.astype(int)\n", " \n", " # You need to return the following variables correctly\n", - " J = 0\n", - " grad = np.zeros(theta.shape)\n", + " # J = 0\n", + " # grad = np.zeros(theta.shape)\n", " \n", " # ====================== YOUR CODE HERE ======================\n", + " z = np.tensordot(theta.T, X, axes=(0, len(X.shape)-1))\n", + " hypothesis = 1 / ( 1 + np.exp( -z ))\n", + " \n", + " J = 1/m * (-y * np.log(hypothesis) - (1-y)*np.log(1-hypothesis) ).sum()\n", + " J += lambda_/(2*m)* (theta[1:]*theta[1:]).sum()\n", "\n", + " beta = hypothesis - y\n", + " grad = 1/m * X.T@beta\n", + " grad += np.concatenate([[0], lambda_/m * theta[1:]])\n", "\n", - " \n", " # =============================================================\n", " return J, grad" ] @@ -389,9 +409,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost : 2.534819\n", + "Expected cost: 2.534819\n", + "-----------------------\n", + "Gradients:\n", + " [0.146561, -0.548558, 0.724722, 1.398003]\n", + "Expected gradients:\n", + " [0.146561, -0.548558, 0.724722, 1.398003]\n" + ] + } + ], "source": [ "J, grad = lrCostFunction(theta_t, X_t, y_t, lambda_t)\n", "\n", @@ -417,9 +451,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise multi-class-classification-and-neural-networks\n", + "\n", + " Part Name | Score | Feedback\n", + " --------- | ----- | --------\n", + " Neural Network Prediction Function | 30 / 30 | Nice work!\n", + " Regularized Logistic Regression | 0 / 20 | Your answer is incorrect.\n", + " One-vs-All Classifier Training | 0 / 20 | Your answer is incorrect.\n", + " One-vs-All Classifier Prediction | 0 / 30 | Your answer is incorrect.\n", + " --------------------------------\n", + " | 30 / 100 | \n", + "\n" + ] + } + ], "source": [ "# appends the implemented function in part 1 to the grader object\n", "grader[1] = lrCostFunction\n", @@ -448,7 +501,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -526,8 +579,20 @@ " X = np.concatenate([np.ones((m, 1)), X], axis=1)\n", "\n", " # ====================== YOUR CODE HERE ======================\n", - " \n", - "\n", + " print(X.shape, all_theta.shape)\n", + " options = {'maxiter': 10}\n", + " \n", + " for class_ in range(num_labels):\n", + " print(f\"Class {class_} trained.\")\n", + " res = optimize.minimize(\n", + " lrCostFunction,\n", + " all_theta[class_],\n", + " (X, (y == class_), lambda_),\n", + " jac=True,\n", + " method=\"CG\",\n", + " options=options\n", + " )\n", + " all_theta[class_] = res.x\n", "\n", " # ============================================================\n", " return all_theta" @@ -542,9 +607,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5000, 401) (10, 401)\n", + "Class 0 trained.\n", + "Class 1 trained.\n", + "Class 2 trained.\n", + "Class 3 trained.\n", + "Class 4 trained.\n", + "Class 5 trained.\n", + "Class 6 trained.\n", + "Class 7 trained.\n", + "Class 8 trained.\n", + "Class 9 trained.\n" + ] + } + ], "source": [ "lambda_ = 0.1\n", "all_theta = oneVsAll(X, y, num_labels, lambda_)" @@ -559,9 +642,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise multi-class-classification-and-neural-networks\n", + "\n" + ] + } + ], "source": [ "grader[2] = oneVsAll\n", "grader.grade()" @@ -635,7 +728,9 @@ "\n", " # ====================== YOUR CODE HERE ======================\n", "\n", - "\n", + " z = np.tensordot(all_theta.T, X, axes=(0, len(X.shape)-1))\n", + " hypothesis = 1 / (1 + np.exp(-z))\n", + " p = np.argmax(z, axis=0)\n", " \n", " # ============================================================\n", " return p" @@ -652,7 +747,15 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Set Accuracy: 90.72%\n" + ] + } + ], "source": [ "pred = predictOneVsAll(all_theta, X)\n", "print('Training Set Accuracy: {:.2f}%'.format(np.mean(pred == y) * 100))" @@ -669,7 +772,31 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise multi-class-classification-and-neural-networks\n", + "\n", + "(16, 3) (4, 3)\n", + "Class 0 trained.\n", + "Class 1 trained.\n", + "Class 2 trained.\n", + "Class 3 trained.\n", + " Part Name | Score | Feedback\n", + " --------- | ----- | --------\n", + " Neural Network Prediction Function | 30 / 30 | Nice work!\n", + " Regularized Logistic Regression | 20 / 20 | Nice work!\n", + " One-vs-All Classifier Training | 20 / 20 | Nice work!\n", + " One-vs-All Classifier Prediction | 0 / 30 | Your answer is incorrect.\n", + " --------------------------------\n", + " | 70 / 100 | \n", + "\n" + ] + } + ], "source": [ "grader[3] = predictOneVsAll\n", "grader.grade()" @@ -692,7 +819,20 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "# training data stored in arrays X, y\n", "data = loadmat(os.path.join('Data', 'ex3data1.mat'))\n", @@ -830,11 +970,24 @@ " num_labels = Theta2.shape[0]\n", "\n", " # You need to return the following variables correctly \n", - " p = np.zeros(X.shape[0])\n", + " # p = np.zeros(X.shape[0])\n", "\n", " # ====================== YOUR CODE HERE ======================\n", "\n", + " # input layer\n", + " a1 = np.concatenate([np.ones((m, 1)), X], axis=1)\n", "\n", + " # hidden layer\n", + " z2 = np.tensordot(Theta1.T, a1, axes=(0, len(a1.shape)-1 ))\n", + " a2 = utils.sigmoid(z2)\n", + " a2 = np.concatenate([np.ones((m, 1)), a2.T], axis=1)\n", + "\n", + " # output layer\n", + " z3 = np.tensordot(Theta2.T, a2, axes=(0, len(a2.shape)- 1 ))\n", + " a3 = utils.sigmoid(z3)\n", + "\n", + " # calc probabilities from classes\n", + " p = np.argmax(a3, axis=0)\n", "\n", " # =============================================================\n", " return p" @@ -851,7 +1004,15 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Set Accuracy: 97.5%\n" + ] + } + ], "source": [ "pred = predict(Theta1, Theta2, X)\n", "print('Training Set Accuracy: {:.1f}%'.format(np.mean(pred == y) * 100))" @@ -870,7 +1031,27 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Neural Network Prediction: 6\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "if indices.size > 0:\n", " i, indices = indices[0], indices[1:]\n", @@ -892,11 +1073,42 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise multi-class-classification-and-neural-networks\n", + "\n", + "(16, 3) (4, 3)\n", + "Class 0 trained.\n", + "Class 1 trained.\n", + "Class 2 trained.\n", + "Class 3 trained.\n", + " Part Name | Score | Feedback\n", + " --------- | ----- | --------\n", + " Neural Network Prediction Function | 30 / 30 | Nice work!\n", + " Regularized Logistic Regression | 20 / 20 | Nice work!\n", + " One-vs-All Classifier Training | 20 / 20 | Nice work!\n", + " One-vs-All Classifier Prediction | 30 / 30 | Nice work!\n", + " --------------------------------\n", + " | 100 / 100 | \n", + "\n" + ] + } + ], "source": [ "grader[4] = predict\n", "grader.grade()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -915,7 +1127,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.6" + "version": "3.7.6" } }, "nbformat": 4,