{"id":77605,"date":"2020-04-20T12:40:25","date_gmt":"2020-04-20T07:10:25","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=77605"},"modified":"2023-07-27T12:54:26","modified_gmt":"2023-07-27T07:24:26","slug":"python-keras-features","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/python-keras-features\/","title":{"rendered":"Python Keras Features Must to Know with Real Time Use Case"},"content":{"rendered":"<p>In this <strong>DataFlair Keras features<\/strong> <strong>tutorial<\/strong>, you study some of the features of Keras that you must know.<\/p>\n<p>You will also perform handwritten digit classification on the MNIST dataset using Python Keras and its features. This is one of the top Keras use case.<\/p>\n<p>So let&#8217;s start.<\/p>\n<h3>What is Keras?<\/h3>\n<p>Keras is a neural network library in python that generally uses TensorFlow, Microsoft CNTK or Theano as its backend.<\/p>\n<p>It is more user friendly and easy as compared to TensorFlow.<\/p>\n<p>You can install Keras and its backend (preferably TensorFlow) from PyPI as:<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">pip install Keras\r\npip install tensorFlow<\/pre>\n<h3>Why Learn Keras?<\/h3>\n<p>Keras is based on python that is very easy to debug and explore. It focuses on user experiences. Using Keras you have to write minimum code in order to perform common functions. It is modular and extensible, you can reuse and extend a model or a piece of code in the future. It also supports almost all neural network models.<\/p>\n<p><span style=\"font-weight: 400\">It&#8217;s noteworthy to note that Keras AI offers a high-level and user-friendly interface for developing and refining deep learning models. Running on top of several backend deep learning frameworks like TensorFlow, Theano, and Microsoft Cognitive Toolkit (CNTK) is one of Keras&#8217; primary benefits. Due to their adaptability, developers and researchers may move between different backends without having to rewrite any of their code. <\/span><\/p>\n<p><span style=\"font-weight: 400\">Additionally, Keras provides an extensive library of pre-built layers, activation functions, and optimisation methods, greatly streamlining the process of building sophisticated neural networks. Keras is a great option for both novices and seasoned experts wishing to quickly build and experiment with cutting-edge deep learning models because of its clarity and straightforward design.<\/span><\/p>\n<h3>Keras Features<\/h3>\n<p>Let us see some of the top features of Keras that make it worth learning:<\/p>\n<h4>1. Prelabeled Datasets<\/h4>\n<ul>\n<li>Keras provides a ton of prelabeled datasets that you can directly import and load.<br \/>\n<strong>Example:<\/strong> CIFAR10 small image classification, IMDB movie review sentiment classification, Reuters newswire topics classification, MNIST handwritten digit dataset, and few others (these are the examples of some famous datasets that are available in Keras)<\/li>\n<li>To import and load this MNIST dataset (a dataset):<\/li>\n<\/ul>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">from Keras.datasets import mnist\r\n(X_train, y_train), (X_test, y_test) = mnist.load_data()<\/pre>\n<h4>2. Numerous implemented layers and parameters<\/h4>\n<p>Keras contains numerous implemented layers and parameters like loss functions, optimizers, evaluations metric.<\/p>\n<p>You can use these layers and parameters for construction, configuration, training, and evaluation of neural networks.<\/p>\n<ul>\n<li>You would load the required layers to build your digit classifier.<\/li>\n<\/ul>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">from keras.models import Sequential\r\nfrom keras.layers import Dense\r\nfrom keras.layers import Dropout\r\nfrom keras.layers import Flatten\r\nfrom keras.optimizers import Adam\r\nfrom keras.utils import np_utils<\/pre>\n<p>Keras also has support for 1D and 2D convolutions and recurrent neural nets and for our digit classifier, you would use Convolution neural nets(Conv2D layer).<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">from keras.layers.convolutional import Conv2D\r\nfrom keras.layers.convolutional import MaxPooling2D<\/pre>\n<h4>3. Multiple methods for Data Preprocessing<\/h4>\n<p>Keras also has a ton of methods for data preprocessing, here you would use Keras.np_utils.to_categorical() method for one-hot encoding of y_train and y_test.<\/p>\n<p>Before that, reshape and normalize the dataset for your requirements.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">#reshape in form of (60000, 28, 28, 1)\r\n\r\nX_train = X_train.reshape(X_train.shape[0], X_train.shape[1], X_train.shape[2], 1).astype('float32')\r\n\r\nX_test = X_test.reshape(X_test.shape[0], X_test.shape[1], X_test.shape[2], 1).astype('float32')\r\n\r\n#normalize to get data in range of 0-1\r\nX_train\/=255\r\nX_test\/=255\r\n\r\n\r\nnumber_of_classes = 10\r\ny_train = np_utils.to_categorical(y_train, number_of_classes)\r\ny_test = np_utils.to_categorical(y_test, number_of_classes)<\/pre>\n<h4>4. .add() Method in Keras<\/h4>\n<p>To add layers imported above by specifying parameters to build your digit classifier, it is done using .add() method.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">model = Sequential()\r\nmodel.add(Conv2D(32, (5, 5), input_shape=(X_train.shape[1], X_train.shape[2], 1), activation='relu'))\r\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\r\nmodel.add(Conv2D(32, (3, 3), activation='relu'))\r\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\r\nmodel.add(Dropout(0.5))\r\nmodel.add(Flatten())\r\nmodel.add(Dense(128, activation='relu'))\r\nmodel.add(Dropout(0.5))\r\nmodel.add(Dense(number_of_classes, activation='softmax')<\/pre>\n<h4>5. .compile() Method in Keras<\/h4>\n<p>Before training, you need to configure your learning process which is done using .compile() method.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy'])<\/pre>\n<h4>6. .fit() method<\/h4>\n<p>You can train Keras models on numpy arrays using .fit().<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5, batch_size=200)<\/pre>\n<p>The training may take some time, here I have used only 5 epochs but you can increase the epoch count as per your systems.<\/p>\n<p>The training looks like this:<\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/keras-features-model-train.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-77609 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/keras-features-model-train.png\" alt=\"Features of Keras\" width=\"1920\" height=\"911\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/keras-features-model-train.png 1920w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/keras-features-model-train-150x71.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/keras-features-model-train-300x142.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/keras-features-model-train-768x364.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/keras-features-model-train-1024x486.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/keras-features-model-train-520x247.png 520w\" sizes=\"auto, (max-width: 1920px) 100vw, 1920px\" \/><\/a><\/p>\n<h4>7. Model Evaluation<\/h4>\n<p>After training your model, you need to test your results on unseen data or you can evaluate your model using .predict_classes() or .evaluate().<\/p>\n<p>You can test your model on your own handwritten digits. I tested it on the following handwritten digit.<\/p>\n<p><strong><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/image.jpeg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-77610 size-full\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/image.jpeg\" alt=\"Keras\" width=\"773\" height=\"1001\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/image.jpeg 773w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/image-116x150.jpeg 116w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/image-232x300.jpeg 232w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/image-768x995.jpeg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/image-520x673.jpeg 520w\" sizes=\"auto, (max-width: 773px) 100vw, 773px\" \/><\/a><\/strong><\/p>\n<p>But before giving it as the input, you need to convert it in the form of MNIST dataset digits.<\/p>\n<p>MNIST dataset digits are grayscale images of (28*28*1) dimensions.<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">import cv2\r\nimg = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)\r\n \r\n# resize image\r\nresized = cv2.resize(img, (28,28), interpolation = cv2.INTER_AREA)\r\nimg = np.resize(resized, (28,28,1))\r\nim2arr = np.array(img)\r\nim2arr = im2arr.reshape(1,28,28,1)\r\ny_pred = model.predict_classes(im2arr)\r\nprint(y_pred)<\/pre>\n<p><strong>Output:<\/strong><\/p>\n<div class=\"code-output\">9<\/div>\n<p>You can see my model predicted it successfully.<\/p>\n<h3>8. Modularity<\/h3>\n<p>As discussed above, Keras is modular. You can save the model you train and use this model later by loading it.<\/p>\n<p>This is done as:<\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">model.save('model.h5')<\/pre>\n<h3>Summary<\/h3>\n<p>Finally, you have seen some common features of Keras. You also learned how to load a dataset, how to build a model, how to add layers with its parameters, how to compile, train, and evaluate a model in Keras.<\/p>\n<p>This article also has the codes to build a <a href=\"https:\/\/data-flair.training\/blogs\/python-deep-learning-project-handwritten-digit-recognition\/\">Handwritten digit classifier on MNIST dataset. <\/a>It shows how you can make deep learning projects in Keras in only tens of lines of code.<\/p>\n<p><span style=\"font-weight: 400\">By offering a potent yet user-friendly framework for creating cutting-edge artificial intelligence models, Keras has transformed the area of deep learning. Because of its emphasis on abstraction and simplicity, complicated neural networks may now be used by a larger range of people, unleashing the full potential of AI. <\/span><\/p>\n<p><span style=\"font-weight: 400\">As a result, Keras has been essential in advancing the adoption of deep learning across industries, including robotics and healthcare as well as computer vision and natural language processing. With its ongoing development and broad support, Keras continues to be a significant resource for the AI community, enabling academics, developers, and enthusiasts to push the boundaries of the technology and promote innovation.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this DataFlair Keras features tutorial, you study some of the features of Keras that you must know. You will also perform handwritten digit classification on the MNIST dataset using Python Keras and its&#46;&#46;&#46;<\/p>\n","protected":false},"author":10,"featured_media":77607,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[22185],"tags":[22187,22186,22188,22583],"class_list":["post-77605","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-keras","tag-features-of-keras","tag-keras-features","tag-python-keras-features","tag-why-learn-keras"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Python Keras Features Must to Know with Real Time Use Case - DataFlair<\/title>\n<meta name=\"description\" content=\"Learn Python Keras Features - What is Keras, Why learn Keras, key features of keras like prelabeled datasets, multiple methods in keras etc\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/python-keras-features\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python Keras Features Must to Know with Real Time Use Case - 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