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+ ] + }, + { + "cell_type": "code", + "source": [ + "# Install tensorflow datasets package\n", + "\n", + "!pip install -U tensorflow_datasets" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "N-J7VaVcuN3v", + "outputId": "90f227c0-2133-43ad-b423-4fc7673e9aef" + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Requirement already satisfied: tensorflow_datasets in /usr/local/lib/python3.10/dist-packages (4.9.2)\n", + "Requirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (1.4.0)\n", + "Requirement already satisfied: array-record in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (0.2.0)\n", + "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (8.1.3)\n", + 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"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->tensorflow_datasets) (1.26.15)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->tensorflow_datasets) (2022.12.7)\n", + "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->tensorflow_datasets) (2.0.12)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->tensorflow_datasets) (3.4)\n", + "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from promise->tensorflow_datasets) (1.16.0)\n", + "Requirement already satisfied: googleapis-common-protos<2,>=1.52.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow-metadata->tensorflow_datasets) (1.59.0)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "# Improt TF and TF datasets\n", + "import tensorflow as tf\n", + "import tensorflow_datasets as tfds\n", + "\n", + "import math\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import tqdm\n", + "import tqdm.auto\n", + "tqdm.tqdm = tqdm.auto.tqdm\n", + "\n", + "\n", + "# Sanity check\n", + "print(tf.__version__)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4nIVdg2DubdI", + "outputId": "fa37a8d5-abcc-4f50-cad7-e2118c559267" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2.12.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Load dataset\n", + "\n", + "dataset, metadata = tfds.load('fashion_mnist', as_supervised=True, with_info=True)\n", + "train_dataset, test_dataset = dataset['train'], dataset['test']" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 169, + "referenced_widgets": [ + "e5f3954fdebf40abac63385a0756fd75", + "b1251681ce5a419e9a52199a1585ce35", + "2f624ed3f5ef4e4c95ccc6a9aa1885b8", + "7e3df00255f744bf90b2a8b88519a0be", + "9855e39e6aac4d8986e0ec40799f149f", + "84b9cca84dd84b5180c070b24aff4fe0", + "5ed40e7f584b42a9be58d8716d29f8ce", + "19b2bd8e91ea4b3e9a8b6145651bfe93", + "4c4c180500174c25aa3d50a8ab3cf772", + "9be47bbdd74049f59a81d7fae5380a19", + "9f6719a037b7415b8b08c285f0679cab", + "815a13d32a71499fb5c8414a42cc5899", + "df9f3934a8eb4aa5a60312df9d7651f8", + "13896318b06e431086a97590717dba4d", + "e7e72abd9d29413481f7e5442cd20b9a", + "cd98c67fe3c44123add574a1d1077b42", + "dbb536ab3805464cae88b3b037cdb0ca", + "f1f506e3515e46028fa24b351a9511fa", + "3bb23adda2df48b9a62d92d245161c01", + "38344bc8476045f5ba2e6e43096d3b75", + "54f1b2593d2d4788ba51bf743d90223f", + "bbbef04d0cbd4b14a02e9fa0d959b332", + "394f80622c33490e8da87d7646919ca4", + "0077e164fe9a40109e0c721a6a52cb8f", + "58390a1fd6df4fdb9e4baa8cf7849697", + "0555e36cf83d41ed933c97ed551bae48", + "78cedc9bff2f4cbf85b301849b3abb15", + "15907d492f5040f8ba168922730471dc", + "307483efc9ca4ee89070299ad013df1d", + "cd0dcf1fd2b5480d9c0a8fd328d0ab5e", + "65e5492be479437ea2ac98b58a36b163", + "88e99f55f54d42328919f489f8cb6090", + "490fe2be33fb40ddae83a4e2e88d2492", + "2a31392e9b6241418e5588841b724e8b", + "7d2c0f2662114095854f8e3eb2d7e49d", + "2a823183ad404549857cb35f1af9404e", + "e620ddd21e2e4617b05aadf00508ed80", + "94892572cd11449db02f19f7e93f3e54", + "c3132505f6334463931e981be02055a4", + "c59c5894389d4c719a0c45a20f7aee10", + "2181e2df414a4a098d09dfc4b05a852e", + "fe2facc07d7744519deb5eedfac63d6f", + "b7c35c58fa3549868b62e1d7ee44a99f", + "0810f9eb88c34ca48d007e115c8bbd95", + "8f9d4e072320424b84a0377baa5f9619", + "3f510f8794a14cb29b723fcb469c7afb", + "32ceb35969144ba985617886a3b526de", + "1fb678aa224a4d6a93b01c68f0f9a3fb", + "c38bf81e16c94bb09c2d6642baa14fdc", + "939427ad901143a0bc21920b673f019f", + "582e92cf83544c199b853d4b3f8d1c25", + "b6c41781cd014ab295182ba4c73537c7", + "5c32e299b00e4a7fa4e75c4a4c072be4", + "b35c858fe3a74021bd3afd96d07e96a3", + "b52c71cf9ddb4b94974c263fe2e80178", + "7a1b79297b1c45d38da610634eb99184", + "5e3779b003a2428791eefac341e1bfc5", + "862619a8718f429fbad9c3d4b2349575", + "e6ba3c1adb7a430ba106f41ee32b84a3", + "0699e91da0814130a5384a3693df8772", + "8e03431fe3bb49f390635f920374d131", + "03a0e00dae404556a2ef7068c2d3bcd8", + "e7e9b45b7e4949e18325113f233eae45", + "d90d26b1d8e54308a9e70203fd19d635", + "26c046b3f2e64c539c19ed905128a42c", + "a8647a74d41248e7a50659b5b4f299c3", + "acd27a33afa943fd82f5cc9a4fc272b3", + "b4bbe21a462d4dddb966c09e303b98e1", + "4f50f97d577e42108f56969a895f0dab", + "c6590e2a68d9407e8844c6cb6ff54f0f", + "80ab64403cd54e03bd845d4a2295e3d3", + "036c3a7b358240458da8aaee81aeda50", + "49304cab8f3b4410942717c00bd2cab7", + "fe72204d17e04bf9a69959a0582fa007", + "4149e8e92b73414a852f769848ae817b", + "adc56044ba754551aad1dc1f86d40a70", + "5a03a918cf774f01ba07bb3e287e6985", + "6c834bc7a0f4429c981d554df921d56f", + "5b079ad4641647fc813724ec6ef315b6", + "5bb710e080c64e5e81e3b585fb84c1be", + "ebd7a771ecd94528bbfafa13ab61a10c", + "383f6f41a8a84ac0bf50e2215a16a9b5", + "a4b4b1678b504a43b0f93a2b566399bc", + "d09e4f6385d14203af850c2ec03b8325", + "30b0622227cc4f7f9131d0b24dcfbca3", + "ae1a226e51f041be8b63f1bc07905a4a", + "5c3e99e036c44c2b939a33150c14fe13", + "28feb045370043749738c0fc1008ad0a" + ] + }, + "id": "q2biLKRdvAcn", + "outputId": "a587a7b2-2380-47a0-d2aa-f81807590ec1" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading and preparing dataset 29.45 MiB (download: 29.45 MiB, generated: 36.42 MiB, total: 65.87 MiB) to /root/tensorflow_datasets/fashion_mnist/3.0.1...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Dl Completed...: 0 url [00:00, ? url/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "e5f3954fdebf40abac63385a0756fd75" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Dl Size...: 0 MiB [00:00, ? MiB/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "815a13d32a71499fb5c8414a42cc5899" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Extraction completed...: 0 file [00:00, ? file/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "394f80622c33490e8da87d7646919ca4" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Generating splits...: 0%| | 0/2 [00:00" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "## Build the model\n", + "\n", + "model = tf.keras.Sequential([\n", + " tf.keras.layers.Flatten(input_shape=(28,28,1)), # convert to 1D array of 784 pixels (28*28)\n", + " tf.keras.layers.Dense(128, activation = tf.nn.relu), # fully connec ted layer\n", + " tf.keras.layers.Dense(10, activation=tf.nn.softmax) # 10 neurons correspond to 10 class labels, total sum of output = 1 (probability)\n", + "])\n", + "\n", + "model.compile(optimizer='adam', loss = 'sparse_categorical_crossentropy', metrics=['accuracy'])" + ], + "metadata": { + "id": "gSMpp1Q5xwtT" + }, + "execution_count": 18, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "BATCH_SIZE=32 # batch size to speed up training\n", + "# repeat forever from the same train dataset, shuffle the training examples and specify batch size -- pretty standard for all algorithms\n", + "train_dataset = train_dataset.repeat().shuffle(num_train_examples).batch(BATCH_SIZE)\n", + "# only need to specify batch size\n", + "test_dataset = test_dataset.batch(BATCH_SIZE)\n", + "\n", + "model.fit(train_dataset, epochs=5, steps_per_epoch=math.ceil(num_train_examples/BATCH_SIZE))\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fBKl44vWy81_", + "outputId": "88057e08-3b20-4f52-8504-9f01a9bf8078" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/5\n", + "1875/1875 [==============================] - 21s 6ms/step - loss: 1.0890 - accuracy: 0.6570\n", + "Epoch 2/5\n", + "1875/1875 [==============================] - 10s 6ms/step - loss: 0.6401 - accuracy: 0.7678\n", + "Epoch 3/5\n", + "1875/1875 [==============================] - 10s 5ms/step - loss: 0.5680 - accuracy: 0.7977\n", + "Epoch 4/5\n", + "1875/1875 [==============================] - 10s 5ms/step - loss: 0.5260 - accuracy: 0.8152\n", + "Epoch 5/5\n", + "1875/1875 [==============================] - 13s 7ms/step - loss: 0.5011 - accuracy: 0.8229\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 19 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Test the model on test dataset\n", + "# Calcualtes accuracy of the model on the test dataset\n", + "\n", + "test_loss, test_accuracy = model.evaluate(test_dataset, steps=math.ceil(num_test_examples/32))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vPzz63MPzwGV", + "outputId": "cebb705b-619c-475d-f354-90b12c8eed2f" + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "313/313 [==============================] - 2s 5ms/step - loss: 0.5144 - accuracy: 0.8138\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Make predictions\n", + "\n", + "for test_images, test_labels in test_dataset.take(1): # each take has 32 images as we re-organized using batch size\n", + " test_images = test_images.numpy()\n", + " test_labels = test_labels.numpy()\n", + " predictions = model.predict(test_images)\n", + "\n", + "print(predictions.shape) # 10 predictions per images as there are 10 class labels\n", + "\n", + "# Check first image's prediction\n", + "print(\"Prediction for first image\")\n", + "predictions[0]\n", + "print(np.argmax(predictions[0])) # prints index\n", + "\n", + "print(\"Corresponding label\")\n", + "print(test_labels[0])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0GhP-pWI0FWk", + "outputId": "3d593437-72cc-4ebf-c891-88fd54cab828" + }, + "execution_count": 24, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1/1 [==============================] - 0s 29ms/step\n", + "(32, 10)\n", + "Prediction for first image\n", + "4\n", + "Corresponding label\n", + "4\n" + ] + } + ] + } + ] +} \ No newline at end of file From 8c0ad67d373248fc533e4e34f28ad4d49c9ff2d2 Mon Sep 17 00:00:00 2001 From: Krishna Varadarajan <32742483+iamvarada@users.noreply.github.com> Date: Fri, 19 May 2023 18:47:08 -0700 Subject: [PATCH 2/3] Delete fashion_clothing_using_MNIST.ipynb --- fashion_clothing_using_MNIST.ipynb | 3357 ---------------------------- 1 file changed, 3357 deletions(-) delete mode 100644 fashion_clothing_using_MNIST.ipynb diff --git a/fashion_clothing_using_MNIST.ipynb b/fashion_clothing_using_MNIST.ipynb deleted file mode 100644 index 2d7af9fdd66d..000000000000 --- a/fashion_clothing_using_MNIST.ipynb +++ /dev/null @@ -1,3357 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "gpuType": "T4", - "authorship_tag": "ABX9TyPXP/iNHfOxw7OwswHMmFV9" - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "accelerator": "GPU", - "gpuClass": 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- } - } - } - }, - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "W_FdsAjCp2J7" - }, - "outputs": [], - "source": [ - "## Classification problem:\n", - "\n", - "# Dataset details:\n", - "# 70k fashion clothing images in this dataset, 10 diff labels\n", - "# each image is 28 x 28 pixel in this dataset\n", - "\n", - "# Objective:\n", - "# Goal: input an image of clothing (28*28 = 784 bytes of data), output what that clothing type is\n", - "# Output:\n", - "# Output would contain probability of each of the 10 classes (10 units as there are 10 labels in this dataset)\n", - "# Sum of the probabilities would be 1\n", - "\n", - "# Definitions:\n", - "# Flatteing : 2D image (aka array 28x28) is converted to a 1 day array of 784 pixels\n", - "# Activation function used: ReLu\n", - "\n", - "\n", - "# Training and testing dataset splits (avoiding overfitting)\n", - "# 60k for training, 10k for test\n", - "\n", - "# Using GPU for this work\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "source": [ - "# Install tensorflow datasets package\n", - "\n", - "!pip install -U tensorflow_datasets" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "N-J7VaVcuN3v", - "outputId": "90f227c0-2133-43ad-b423-4fc7673e9aef" - }, - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", - "Requirement already satisfied: tensorflow_datasets in /usr/local/lib/python3.10/dist-packages (4.9.2)\n", - "Requirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (1.4.0)\n", - "Requirement already satisfied: array-record in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (0.2.0)\n", - "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (8.1.3)\n", - "Requirement already satisfied: dm-tree in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (0.1.8)\n", - "Requirement already satisfied: etils[enp,epath]>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (1.2.0)\n", - "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (1.22.4)\n", - "Requirement already satisfied: promise in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (2.3)\n", - "Requirement already satisfied: protobuf>=3.20 in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (3.20.3)\n", - "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (5.9.5)\n", - "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (2.27.1)\n", - "Requirement already satisfied: tensorflow-metadata in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (1.13.1)\n", - "Requirement already satisfied: termcolor in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (2.3.0)\n", - "Requirement already satisfied: toml in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (0.10.2)\n", - "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (4.65.0)\n", - "Requirement already satisfied: wrapt in /usr/local/lib/python3.10/dist-packages (from tensorflow_datasets) (1.14.1)\n", - "Requirement already satisfied: importlib_resources in /usr/local/lib/python3.10/dist-packages (from etils[enp,epath]>=0.9.0->tensorflow_datasets) (5.12.0)\n", - "Requirement already satisfied: typing_extensions in /usr/local/lib/python3.10/dist-packages (from etils[enp,epath]>=0.9.0->tensorflow_datasets) (4.5.0)\n", - "Requirement already satisfied: zipp in /usr/local/lib/python3.10/dist-packages (from etils[enp,epath]>=0.9.0->tensorflow_datasets) (3.15.0)\n", - "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->tensorflow_datasets) (1.26.15)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->tensorflow_datasets) (2022.12.7)\n", - "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->tensorflow_datasets) (2.0.12)\n", - "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->tensorflow_datasets) (3.4)\n", - "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from promise->tensorflow_datasets) (1.16.0)\n", - "Requirement already satisfied: googleapis-common-protos<2,>=1.52.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow-metadata->tensorflow_datasets) (1.59.0)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "from __future__ import absolute_import, division, print_function\n", - "\n", - "# Improt TF and TF datasets\n", - "import tensorflow as tf\n", - "import tensorflow_datasets as tfds\n", - "\n", - "import math\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import tqdm\n", - "import tqdm.auto\n", - "tqdm.tqdm = tqdm.auto.tqdm\n", - "\n", - "\n", - "# Sanity check\n", - "print(tf.__version__)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4nIVdg2DubdI", - "outputId": "fa37a8d5-abcc-4f50-cad7-e2118c559267" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "2.12.0\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Load dataset\n", - "\n", - "dataset, metadata = tfds.load('fashion_mnist', as_supervised=True, with_info=True)\n", - "train_dataset, test_dataset = dataset['train'], dataset['test']" - ], - "metadata": { - "colab": { - "base_uri": 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "## Build the model\n", - "\n", - "model = tf.keras.Sequential([\n", - " tf.keras.layers.Flatten(input_shape=(28,28,1)), # convert to 1D array of 784 pixels (28*28)\n", - " tf.keras.layers.Dense(128, activation = tf.nn.relu), # fully connec ted layer\n", - " tf.keras.layers.Dense(10, activation=tf.nn.softmax) # 10 neurons correspond to 10 class labels, total sum of output = 1 (probability)\n", - "])\n", - "\n", - "model.compile(optimizer='adam', loss = 'sparse_categorical_crossentropy', metrics=['accuracy'])" - ], - "metadata": { - "id": "gSMpp1Q5xwtT" - }, - "execution_count": 18, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "BATCH_SIZE=32 # batch size to speed up training\n", - "# repeat forever from the same train dataset, shuffle the training examples and specify batch size -- pretty standard for all algorithms\n", - "train_dataset = train_dataset.repeat().shuffle(num_train_examples).batch(BATCH_SIZE)\n", - "# only need to specify batch size\n", - "test_dataset = test_dataset.batch(BATCH_SIZE)\n", - "\n", - "model.fit(train_dataset, epochs=5, steps_per_epoch=math.ceil(num_train_examples/BATCH_SIZE))\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "fBKl44vWy81_", - "outputId": "88057e08-3b20-4f52-8504-9f01a9bf8078" - }, - "execution_count": 19, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Epoch 1/5\n", - "1875/1875 [==============================] - 21s 6ms/step - loss: 1.0890 - accuracy: 0.6570\n", - "Epoch 2/5\n", - "1875/1875 [==============================] - 10s 6ms/step - loss: 0.6401 - accuracy: 0.7678\n", - "Epoch 3/5\n", - "1875/1875 [==============================] - 10s 5ms/step - loss: 0.5680 - accuracy: 0.7977\n", - "Epoch 4/5\n", - "1875/1875 [==============================] - 10s 5ms/step - loss: 0.5260 - accuracy: 0.8152\n", - "Epoch 5/5\n", - "1875/1875 [==============================] - 13s 7ms/step - loss: 0.5011 - accuracy: 0.8229\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 19 - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Test the model on test dataset\n", - "# Calcualtes accuracy of the model on the test dataset\n", - "\n", - "test_loss, test_accuracy = model.evaluate(test_dataset, steps=math.ceil(num_test_examples/32))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vPzz63MPzwGV", - "outputId": "cebb705b-619c-475d-f354-90b12c8eed2f" - }, - "execution_count": 20, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "313/313 [==============================] - 2s 5ms/step - loss: 0.5144 - accuracy: 0.8138\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Make predictions\n", - "\n", - "for test_images, test_labels in test_dataset.take(1): # each take has 32 images as we re-organized using batch size\n", - " test_images = test_images.numpy()\n", - " test_labels = test_labels.numpy()\n", - " predictions = model.predict(test_images)\n", - "\n", - "print(predictions.shape) # 10 predictions per images as there are 10 class labels\n", - "\n", - "# Check first image's prediction\n", - "print(\"Prediction for first image\")\n", - "predictions[0]\n", - "print(np.argmax(predictions[0])) # prints index\n", - "\n", - "print(\"Corresponding label\")\n", - "print(test_labels[0])" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0GhP-pWI0FWk", - "outputId": "3d593437-72cc-4ebf-c891-88fd54cab828" - }, - "execution_count": 24, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1/1 [==============================] - 0s 29ms/step\n", - "(32, 10)\n", - "Prediction for first image\n", - "4\n", - "Corresponding label\n", - "4\n" - ] - } - ] - } - ] -} \ No newline at end of file From e0b407ea5bd02d36cffc973992fdf87bbd6b2b0e Mon Sep 17 00:00:00 2001 From: Krishna Varadarajan <32742483+iamvarada@users.noreply.github.com> Date: Sun, 21 May 2023 14:06:09 -0700 Subject: [PATCH 3/3] Started numpy cheatsheet --- numpy_cheatsheet.ipynb | 1237 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1237 insertions(+) create mode 100644 numpy_cheatsheet.ipynb diff --git a/numpy_cheatsheet.ipynb b/numpy_cheatsheet.ipynb new file mode 100644 index 000000000000..aa0014529712 --- /dev/null +++ b/numpy_cheatsheet.ipynb @@ -0,0 +1,1237 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyPTFim18vBECZiyRxsa9+J4", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vdHbIT1E7Imi" + }, + "outputs": [], + "source": [ + "## Numpy cheatsheet and workbook" + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "my_list = [1,2,3,4,5]\n", + "arr = np.array(my_list)\n", + "type(arr)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "W3unrSoc7vk-", + "outputId": "2147df63-4e11-4489-f300-d49caff02ca8" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "metadata": {}, + "execution_count": 2 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Efficiency comparison with list\n", + "\n", + "# memory check\n", + "S = range(1000) # 1000 elements\n", + "D = np.arange(1000) # 1000 elements from similar function from numpy\n", + "\n", + "import sys\n", + "sys.getsizeof(5)*len(S),'bytes' # num bytes occupied by a variable\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NuLuw5b58Fdl", + "outputId": "c379dbe6-7729-43a6-c4b2-44de94520c76" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(28000, 'bytes')" + ] + }, + "metadata": {}, + "execution_count": 7 + } + ] + }, + { + "cell_type": "code", + "source": [ + "D.itemsize*D.size,'bytes' # returns size occupied by single element in the array\n", + "\n", + "# memory efficiency very high for numpy compared to built-in structures like list" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "D2x_14-k85_S", + "outputId": "0928b4bb-9891-47e8-fdce-ba68aef1d0f9" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(8000, 'bytes')" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# time efficiency check\n", + "import time\n", + "\n", + "SIZE = 1000000\n", + "\n", + "L1 = range(SIZE)\n", + "L2 = range(SIZE)\n", + "\n", + "A1 = np.arange(SIZE)\n", + "A2 = np.arange(SIZE)\n", + "\n", + "start_time = time.time()\n", + "result = [(x+y) for x,y in zip(L1,L2)] # list comprehensiion, zip() performs elementwise computation\n", + "end_time = time.time()\n", + "\n", + "print('Time taken by list to add values ', (end_time - start_time)*1000, 'ms')\n", + "\n", + "start_time = time.time()\n", + "result = A1+A2\n", + "end_time = time.time()\n", + "\n", + "print(\"Time taken by numpy to add values \", (end_time - start_time)*1000, \"ms\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JL-WhAXh9IAV", + "outputId": "d04e90f7-b272-4d02-daa5-720d5afdcead" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Time taken by list to add values 66.49637222290039 ms\n", + "Time taken by numpy to add values 16.24774932861328 ms\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "gMIrQ_Ac-CeB" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# 1D array\n", + "a = np.array([1,2,3,4,5])\n", + "print(a, a.shape, a.ndim)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "J4qq07nd-uMB", + "outputId": "5fb35fb8-d1eb-4eba-a9fe-80a232a38582" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[1 2 3 4 5] (5,) 1\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 2D array\n", + "b = np.array([[1,2,3],\n", + " [4,5,6]\n", + " ])\n", + "print(b, '\\n', b.shape, b.ndim)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BSE9g2wi-5ho", + "outputId": "65d1c753-9116-4755-e84f-0eee46006f6e" + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[1 2 3]\n", + " [4 5 6]] \n", + " (2, 3) 2\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# 3D array\n", + "c = np.array([\n", + " [[1,2,3], [4,5,6]],\n", + " [[7,8,9], [10,11,12]]\n", + " ])\n", + "\n", + "print(c, '\\n', c.shape, c.ndim)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VwI3Ihq4_VQJ", + "outputId": "29a1b332-e729-4435-b82c-b0f776c16c51" + }, + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[[ 1 2 3]\n", + " [ 4 5 6]]\n", + "\n", + " [[ 7 8 9]\n", + " [10 11 12]]] \n", + " (2, 2, 3) 3\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Re-shaping the arrays\n", + "\n", + "e = np.array([[1,2,3], [4,5,6]])\n", + "print(e, '\\n' , e.shape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_ga0Vfgz_zl_", + "outputId": "1cf449be-0cd5-48f0-f84c-a5e01c4c339f" + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[1 2 3]\n", + " [4 5 6]] \n", + " (2, 3)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "e.reshape(3,2)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QgZ2PiZy_-A2", + "outputId": "ecc796b3-069a-45bb-adfa-f610dfbea7dd" + }, + "execution_count": 26, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[1, 2],\n", + " [3, 4],\n", + " [5, 6]])" + ] + }, + "metadata": {}, + "execution_count": 26 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Flatten the array to 1D array\n", + "\n", + "print(e.flatten())\n", + "print(e.flatten().shape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "y7m6vk0OAIFm", + "outputId": "6ecaa9a7-7593-450d-af66-e65757e2fdc5" + }, + "execution_count": 30, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[1 2 3 4 5 6]\n", + "(6,)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Stacking -- Merging two nd arrays\n", + "\n", + "f = np.array([1,2,3])\n", + "g = np.array([4,5,6])\n", + "\n", + "# Horizontal stacking\n", + "print('Horizontal stacking \\n', np.hstack((f,g))) # passing both arrays as tuple (aka (a,b))\n", + "\n", + "# Vertical stacking\n", + "print('Vertical stacking results in 2D array \\n', np.vstack((f,g))) # passing both arrays as tuple (aka (a,b))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MWUWRXNAAVcn", + "outputId": "302cc210-9fa1-427e-92bb-5dbb4ec33db3" + }, + "execution_count": 33, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Horizontal stacking \n", + " [1 2 3 4 5 6]\n", + "Vertical stacking results in 2D array \n", + " [[1 2 3]\n", + " [4 5 6]]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Slicing\n", + "\n", + "e = np.array([(1,2,3), (4,5,6)])\n", + "print(e)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XOmS7gFaAtSh", + "outputId": "fbd7b100-0e16-47f3-a799-87ac296c15f5" + }, + "execution_count": 36, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[1 2 3]\n", + " [4 5 6]]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Indexing -- a form of basic slicing\n", + "print(e[0])\n", + "print(e[1])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BYzKuFSVBAWW", + "outputId": "4a736e3c-112d-4555-a83e-5b7428358555" + }, + "execution_count": 39, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[1 2 3]\n", + "[4 5 6]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Slicing\n", + "print(e[:,1]) # all rows and first column (starts from 0)\n", + "print(e[1,:2]) # 1st row, 2 columns starting from 0" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XDLWBECpBHje", + "outputId": "346939de-3ba0-4881-af9a-0e40409a9ebe" + }, + "execution_count": 43, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[2 5]\n", + "[4 5]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Intializing float arrays\n", + "list_ex = [[0,1,2], [3,4,5]]\n", + "arr = np.array(list_ex, dtype = 'float')\n", + "print(arr)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XyVFDFt0BiGf", + "outputId": "060fa156-19b2-4d37-f1fd-e46c9db05def" + }, + "execution_count": 44, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[0. 1. 2.]\n", + " [3. 4. 5.]]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Converting to another datatype after assiging the array\n", + "\n", + "print(arr.astype('int'))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DCr3TueqCMNh", + "outputId": "da09afeb-e9a7-44bf-d58e-58c0f6fdaf69" + }, + "execution_count": 45, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[0 1 2]\n", + " [3 4 5]]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Convering array to list\n", + "\n", + "print(arr.tolist(), type(arr.tolist()))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hiHpMyGtCWel", + "outputId": "6a0c6644-1ef4-4e95-d0ba-2f77eb66e8d2" + }, + "execution_count": 47, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]] \n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Manipulating arrays\n", + "\n", + "# Missing values\n", + "arr = np.array([[1,2,3], [4,5,6], [7,8,9]])\n", + "print(arr, arr.shape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5djnDpHkCc8e", + "outputId": "abe68ace-fb0b-40b1-b090-746282a32b75" + }, + "execution_count": 50, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[1 2 3]\n", + " [4 5 6]\n", + " [7 8 9]] (3, 3)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "arr[1,1] = np.nan\n", + "# gives error as nd arrays can only of one datatype, nan is float, arr is integer right" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 187 + }, + "id": "t0fC2qzYC6aU", + "outputId": "763823f0-8b34-4173-a541-e2abce2698cd" + }, + "execution_count": 51, + "outputs": [ + { + "output_type": "error", + "ename": "ValueError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0marr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;31m# gives error as nd arrays can only of one datatype, nan is float, arr is integer right\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: cannot convert float NaN to integer" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "arr = arr.astype('float')\n", + "arr" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X2DaQycrDLS9", + "outputId": "61621dd1-f65b-4179-af8f-7352bcf81c12" + }, + "execution_count": 55, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[1., 2., 3.],\n", + " [4., 5., 6.],\n", + " [7., 8., 9.]])" + ] + }, + "metadata": {}, + "execution_count": 55 + } + ] + }, + { + "cell_type": "code", + "source": [ + "arr[1,1] = np.nan\n", + "arr[2,1] = np.inf\n", + "arr" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hTO_jexoDRI6", + "outputId": "7642b916-5faa-4d00-fc9c-7b3955296976" + }, + "execution_count": 58, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[ 1., 2., 3.],\n", + " [ 4., nan, 6.],\n", + " [ 7., inf, 9.]])" + ] + }, + "metadata": {}, + "execution_count": 58 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Statistics\n", + "\n", + "# Computing mean, min, max on mdarray\n", + "arr[1,1] = 10\n", + "arr[2,1] = 20\n", + "arr\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "r1zcgRESDevW", + "outputId": "e8ebc93b-28cf-4850-ba41-cf227304a653" + }, + "execution_count": 59, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[ 1., 2., 3.],\n", + " [ 4., 10., 6.],\n", + " [ 7., 20., 9.]])" + ] + }, + "metadata": {}, + "execution_count": 59 + } + ] + }, + { + "cell_type": "code", + "source": [ + "print('Mean: ', arr.mean())\n", + "print('Min: ', arr.min())\n", + "print('Max: ', arr.max())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LG216lemDzft", + "outputId": "e6441acd-7431-4baf-bf94-49679a205dba" + }, + "execution_count": 60, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mean: 6.888888888888889\n", + "Min: 1.0\n", + "Max: 20.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Column-wise stats\n", + "print('Column-wise min', np.amin(arr, axis = 0)) # axis = 0 means column-wise\n", + "\n", + "# Row-wise stats\n", + "print('Row-wise min', np.amin(arr, axis=1)) # axis = 1 means row-wise" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DrimIDbCD9aa", + "outputId": "cf0bcbff-9525-46b6-8e18-d71bd89099ae" + }, + "execution_count": 62, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Column-wise min [1. 2. 3.]\n", + "Row-wise min [1. 4. 7.]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Filtering the data using numpy expressions\n", + "\n", + "a = np.array([[1,2,3], [2,3,4], [7,8,9]])\n", + "print(a, a.shape, a.ndim)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jUVaSOyCEaug", + "outputId": "1c7806d0-76e1-4667-906d-8b055bcc6bd7" + }, + "execution_count": 63, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[1 2 3]\n", + " [2 3 4]\n", + " [7 8 9]] (3, 3) 2\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Find values in the array greater than 2\n", + "bool_idx = (a > 2)\n", + "print(bool_idx)\n", + "\n", + "print('\\n\\nValues at all indices where the boolean expression is true')\n", + "a[bool_idx] " + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cbKhrlIFEoi1", + "outputId": "36e4926b-f262-47a9-de03-224cc38577b9" + }, + "execution_count": 67, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[False False True]\n", + " [False True True]\n", + " [ True True True]]\n", + "\n", + "\n", + "Values at all indices where the boolean expression is true\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([3, 3, 4, 7, 8, 9])" + ] + }, + "metadata": {}, + "execution_count": 67 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Math operations on nd array\n", + "\n", + "x = np.array([[1,2], [3,4]], dtype = 'float')\n", + "y = np.array([[5,6], [7,8]], dtype = np.float64) # another way to specify the data type\n", + "\n", + "print(x)\n", + "print(y)\n", + "\n", + "# Element wise\n", + "\n", + "# subract \n", + "print(x-y)\n", + "\n", + "# same as\n", + "np.subtract(x,y)\n", + "\n", + "# square root\n", + "np.sqrt(x)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bc9Df0o0FCb5", + "outputId": "7ce82e59-bf04-43ab-e461-30cf1f7996c5" + }, + "execution_count": 73, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[1. 2.]\n", + " [3. 4.]]\n", + "[[5. 6.]\n", + " [7. 8.]]\n", + "[[-4. -4.]\n", + " [-4. -4.]]\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[1. , 1.41421356],\n", + " [1.73205081, 2. ]])" + ] + }, + "metadata": {}, + "execution_count": 73 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Column-wise and row-wise math operations\n", + "print(np.sum(x)) # all elements of array\n", + "\n", + "print(np.sum(x, axis = 0)) # column-wise\n", + "print(np.sum(x, axis = 1)) # row-wise" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YaQ7LSGmFuUD", + "outputId": "b8514af8-ddd6-44c2-c65a-fab91090cebc" + }, + "execution_count": 75, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "10.0\n", + "[4. 6.]\n", + "[3. 7.]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Basic word problem\n", + "\n", + "# distance travelled by riders\n", + "dist = [181, 222, 445, 467]\n", + "# time taken by each rider\n", + "time = [2, 5, 6, 7]\n", + "\n", + "# speed of each rider\n", + "speed = dist/time # error as list is not divisible element wise" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "id": "33qogAAsGCZK", + "outputId": "2e4d1cdb-f55a-42ac-af42-dc5a23ff9e50" + }, + "execution_count": 76, + "outputs": [ + { + "output_type": "error", + "ename": "TypeError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;31m# speed of each rider\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mspeed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdist\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'list' and 'list'" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Convert to np array and it makes life easier\n", + "dist = np.array(dist)\n", + "time = np.array(time)\n", + "\n", + "speed = dist/time\n", + "print(speed)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IugWvgLqGaE4", + "outputId": "bf228084-7dd0-48aa-c93a-1e5a24ff5dfe" + }, + "execution_count": 79, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[90.5 44.4 74.16666667 66.71428571]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Another word problem\n", + "\n", + "hour_wage = np.array([12,45,677]) # in INR\n", + "\n", + "# conver to dollars (x80 for example)\n", + "\n", + "hour_wage*80" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "36vTsD9SGmxV", + "outputId": "b7fd293a-c6d1-4c7f-daf6-a5ab2e6f8b72" + }, + "execution_count": 80, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ 960, 3600, 54160])" + ] + }, + "metadata": {}, + "execution_count": 80 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Another problem\n", + "\n", + "weekly_hrs = np.array([40,506, 69076, 33])\n", + "weekly_hrs[weekly_hrs >= 40]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "n8AKkgVNG4eR", + "outputId": "28114dc8-5eb6-403f-e2ba-cdd0ac2f5537" + }, + "execution_count": 81, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ 40, 506, 69076])" + ] + }, + "metadata": {}, + "execution_count": 81 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Same thing using numpy logical operators\n", + "weekly_hrs[np.logical_not(weekly_hrs < 40)]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2htz9i0bHEoq", + "outputId": "a7e44b01-b780-49fd-f38a-97572dd6820d" + }, + "execution_count": 82, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ 40, 506, 69076])" + ] + }, + "metadata": {}, + "execution_count": 82 + } + ] + }, + { + "cell_type": "code", + "source": [ + "np.logical_and(weekly_hrs > 30, weekly_hrs <50)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5rqr44rMHONx", + "outputId": "285aab70-de61-43ab-f4b9-bd72256ce7fd" + }, + "execution_count": 84, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([ True, False, False, True])" + ] + }, + "metadata": {}, + "execution_count": 84 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Create arrays of ones\n", + "\n", + "print(np.ones((3,4))) # 3 rows, 4 columns\n", + "print('\\n\\n')\n", + "print(np.zeros((1,2)))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ci2CbsHIHWls", + "outputId": "02d314c4-f4ad-48bb-a055-a906d8442818" + }, + "execution_count": 87, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[1. 1. 1. 1.]\n", + " [1. 1. 1. 1.]\n", + " [1. 1. 1. 1.]]\n", + "\n", + "\n", + "\n", + "[[0. 0.]]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Random values\n", + "np.random.random((2,2))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "dicn2-qVHnlG", + "outputId": "bf69f1ee-a06a-49c5-edb6-8208d937f75a" + }, + "execution_count": 88, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[0.36653132, 0.27288394],\n", + " [0.47035967, 0.42114506]])" + ] + }, + "metadata": {}, + "execution_count": 88 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Empty array\n", + "np.empty((3,5))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RubxKhUlHrwT", + "outputId": "61d6037e-ed33-4a7b-d513-34e59347a4d4" + }, + "execution_count": 89, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[2.56032802e-316, 0.00000000e+000, 0.00000000e+000,\n", + " 4.79243676e-322, 6.92997366e-310],\n", + " [3.53407133e-316, 4.31174539e-096, 9.80058441e+252,\n", + " 1.23971686e+224, 1.05235720e-153],\n", + " [9.03292329e+271, 9.08366793e+223, 1.06244660e-153,\n", + " 3.44981369e+175, 7.11454530e-322]])" + ] + }, + "metadata": {}, + "execution_count": 89 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Full array\n", + "\n", + "np.full((2,2), 7)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jY7kQZQNHwhC", + "outputId": "0c1c7056-0502-4c5e-8c01-ebbcd22cd341" + }, + "execution_count": 92, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[7, 7],\n", + " [7, 7]])" + ] + }, + "metadata": {}, + "execution_count": 92 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Evenly spaced array\n", + "print(np.arange(10,25,5)) # start from 10, space of 5, end at 25\n", + "print(np.linspace(0,2,9)) # min = 0, max = 2, total elements = 9" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6OHXU-5mH5Pg", + "outputId": "6ba62433-ad04-416e-987a-471e20aa6b9c" + }, + "execution_count": 94, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[10 15 20]\n", + "[0. 0.25 0.5 0.75 1. 1.25 1.5 1.75 2. ]\n" + ] + } + ] + } + ] +} \ No newline at end of file