{"id":77663,"date":"2020-04-27T13:00:07","date_gmt":"2020-04-27T07:30:07","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=77663"},"modified":"2021-08-25T13:55:17","modified_gmt":"2021-08-25T08:25:17","slug":"keras-deep-learning","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/keras-deep-learning\/","title":{"rendered":"Deep Learning with Keras Implementation and Example"},"content":{"rendered":"<p>Welcome back to <strong>DataFlair Keras Tutorial series.\u00a0<\/strong>In this Keras tutorial, we will walk through <strong>deep learning with keras<\/strong> and an important <strong>deep learning algorithm used in keras.\u00a0<\/strong>We will study the applications of this algorithm and also its implementation in Keras.<\/p>\n<p>Deep Learning is a subset of machine learning which concerns the algorithms inspired by the architecture of the brain. In the last decade, there have been many major developments to support deep learning research. Keras is the result of one of these recent developments which allow us to define and create neural network models in a few lines of code.<\/p>\n<p>There has been a boom in the research of <strong>Deep Learning algorithms<\/strong>. Keras ensures the ease of users to create these algorithms.<\/p>\n<p>But before we begin with Tensorflow Keras Deep learning article, let us do <a href=\"https:\/\/data-flair.training\/blogs\/install-keras-on-linux-windows\/\">keras installation.<\/a><\/p>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/deep-learning-with-keras.jpg\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-77664\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/deep-learning-with-keras.jpg\" alt=\"deep learning with keras\" width=\"802\" height=\"420\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/deep-learning-with-keras.jpg 802w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/deep-learning-with-keras-150x79.jpg 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/deep-learning-with-keras-300x157.jpg 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/deep-learning-with-keras-768x402.jpg 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/deep-learning-with-keras-520x272.jpg 520w\" sizes=\"auto, (max-width: 802px) 100vw, 802px\" \/><\/a><\/p>\n<h3>Popular Algorithms for Deep Learning with Keras<\/h3>\n<p>Below are mentioned some of the <strong>popular algorithms in deep learning:<\/strong><\/p>\n<ul>\n<li>Auto-Encoders<\/li>\n<li>Convolution Neural Nets<\/li>\n<li>Recurrent Neural Nets<\/li>\n<li>Long Short Term Memory Nets<\/li>\n<li>Deep Boltzmann Machine(DBM)<\/li>\n<li>Deep Belief Nets(DBN)<\/li>\n<\/ul>\n<p>There are implementations of <strong>convolution neural nets, recurrent neural nets, and LSTM<\/strong> in our previous articles.<\/p>\n<p>Here we will take a tour of <strong>Auto Encoders algorithm of deep learning.<\/strong><\/p>\n<h3>Auto-Encoders<\/h3>\n<p>These types of neural networks are able to compress the input data and reconstruct it again. These are very old deep learning algorithms. It encodes the input upto a bottleneck layer and then decodes it to get the input back. At the bottleneck layer, we get a compressed form of input.<\/p>\n<p><strong>Anomaly detection and denoising an image<\/strong> are a few of the major applications of Auto-Encoders.<\/p>\n<h3>Types of Auto-Encoders<\/h3>\n<p>There are seven types of deep learning auto encoders as mentioned below:<\/p>\n<ul>\n<li>Denoising autoencoders<\/li>\n<li>Deep autoencoders<\/li>\n<li>Sparse autoencoders<\/li>\n<li>Contractive autoencoders<\/li>\n<li>Convolutional autoencoders<\/li>\n<li>Variational autoencoders<\/li>\n<li>Undercomplete autoencoders<\/li>\n<\/ul>\n<p>For our study, we will create a Denoising autoencoder.<\/p>\n<h3>Implementation of Denoising Auto-encoder in Keras<\/h3>\n<p>For the purpose of its implementation in Keras, we will work on MNIST handwritten digit dataset.<\/p>\n<p>Firstly, we will introduce some noise in the MNIST images. Then we will create an <a href=\"https:\/\/www.deeplearningbook.org\/contents\/autoencoders.html\">Auto &#8211; Encoder<\/a> for removing noise from the images and reconstruct the original images.<\/p>\n<p><strong>1. Import required modules<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">import numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom keras.datasets import mnist\r\nfrom keras.layers import Input,Dense,Conv2D,MaxPooling2D,UpSampling2D\r\n\r\nfrom keras.models import Model\r\nfrom keras import backend as K<\/pre>\n<p><strong>2. Load MNIST images from datasets module of keras<\/strong><\/p>\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<p><strong>3. Convert dataset in range of 0 to 1<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">x_train=x_train.astype('float32')\/255\r\nx_test=x_test.astype('float32')\/255\r\n\r\n\r\nx_train=np.reshape(x_train,(len(x_train),28,28,1))\r\nx_test=np.reshape(x_test,(len(x_test),28,28,1))<\/pre>\n<p><strong>4. Introducing noise in MNIST images using Gaussian distribution<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">noise_factor=0.5\r\n\r\nx_train_noisy=x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0,size=x_train.shape)\r\nx_test_noisy=x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0,size=x_test.shape)\r\n\r\nx_train_noisy= np.clip(x_train_noisy,0.,1.)\r\nx_test_noisy= np.clip(x_test_noisy,0.,1.)<\/pre>\n<p><strong>5. Visualize the noise introduced<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">n=5\r\n\r\nplt.figure(figsize=(20,2))\r\n\r\nfor i in range(n):\r\nax=plt.subplot(1,n,i+1)\r\nplt.imshow(x_test_noisy[i].reshape(28,28))\r\nplt.gray()\r\nax.get_xaxis().set_visible(False)\r\nax.get_yaxis().set_visible(False)\r\nplt.show()<\/pre>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/1_dnoised_image.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-77665\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/1_dnoised_image.png\" alt=\"denoised_image\" width=\"1425\" height=\"911\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/1_dnoised_image.png 1425w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/1_dnoised_image-150x96.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/1_dnoised_image-300x192.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/1_dnoised_image-768x491.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/1_dnoised_image-1024x655.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/1_dnoised_image-520x332.png 520w\" sizes=\"auto, (max-width: 1425px) 100vw, 1425px\" \/><\/a><\/p>\n<p><strong>6. Specify input layer and create model<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">input_img=Input(shape=(28,28,1))\r\n\r\nx=Conv2D(32,(3,3),activation='relu',padding='same')(input_img)\r\nx=MaxPooling2D((2,2),padding='same')(x)\r\nx=Conv2D(32,(3,3),activation='relu',padding='same')(x)\r\nencoded=MaxPooling2D((2,2),padding='same')(x)<\/pre>\n<p><strong>7. Encoded is the bottleneck layer and consists of a compressed form of images.<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">x=Conv2D(32,(3,3),activation='relu',padding='same')(encoded)\r\nx=UpSampling2D((2,2))(x)\r\nx=Conv2D(32,(3,3),activation='relu',padding='same')(x)\r\nx=UpSampling2D((2,2))(x)\r\ndecoded=Conv2D(1,(3,3),activation='sigmoid',padding='same')(x)<\/pre>\n<p><strong>8. Train the autoencoder<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">x=Conv2D(32,(3,3),activation='relu',padding='same')(encoded)\r\nx=UpSampling2D((2,2))(x)\r\nx=Conv2D(32,(3,3),activation='relu',padding='same')(x)\r\nx=UpSampling2D((2,2))(x)\r\ndecoded=Conv2D(1,(3,3),activation='sigmoid',padding='same')(x)<\/pre>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/2_training_epoch.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-77666\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/2_training_epoch.png\" alt=\"Training epoch in deep learning\" width=\"1425\" height=\"911\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/2_training_epoch.png 1425w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/2_training_epoch-150x96.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/2_training_epoch-300x192.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/2_training_epoch-768x491.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/2_training_epoch-1024x655.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/2_training_epoch-520x332.png 520w\" sizes=\"auto, (max-width: 1425px) 100vw, 1425px\" \/><\/a><\/p>\n<p>Here I am training my model on 20 epochs only,\u00a0 you can train it with 100 epochs as well.<\/p>\n<p><strong>9. Get prediction on noisy data<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">x_test_result = autoencoder.predict(x_test_noisy, batch_size=128)<\/pre>\n<p><strong>10. Again visualize the reconstructed images<\/strong><\/p>\n<pre class=\"EnlighterJSRAW\" data-enlighter-language=\"null\">n=5\r\n\r\nplt.figure(figsize=(20,2))\r\n\r\nfor i in range(n):\r\nax=plt.subplot(1,n,i+1)\r\nplt.imshow(x_test_result[i].reshape(28,28))\r\nplt.gray()\r\nax.get_xaxis().set_visible(False)\r\nax.get_yaxis().set_visible(False)\r\nplt.show()<\/pre>\n<p><a href=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/3_result.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-77667\" src=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/3_result.png\" alt=\"Deep learning auto encoder result\" width=\"1425\" height=\"911\" srcset=\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/3_result.png 1425w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/3_result-150x96.png 150w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/3_result-300x192.png 300w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/3_result-768x491.png 768w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/3_result-1024x655.png 1024w, https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2020\/04\/3_result-520x332.png 520w\" sizes=\"auto, (max-width: 1425px) 100vw, 1425px\" \/><\/a><\/p>\n<p>You can see our Auto Encoder is able to reconstruct the images and remove its noise. We will get better quality if we increase the epoch count of training.<\/p>\n<h3>Summary<\/h3>\n<p>To conclude, we have seen Deep learning with Keras implementation and example. This article concerns the Keras library and its support to deploy major deep learning algorithms. It also introduces you to Auto-Encoders, its different types, its applications, and its implementation.<br \/>\nIt explains how to build a neural network for removing noise from our data.<\/p>\n<p><strong>When you have learnt deep learning with keras, let us implement <a href=\"https:\/\/data-flair.training\/blogs\/deep-learning-project-ideas\/\">deep learning projects<\/a> for better knowledge. <\/strong><\/p>\n<p><strong>Do share your feedback in the comment section.<\/strong><span hidden class=\"__iawmlf-post-loop-links\" data-iawmlf-links=\"[{&quot;id&quot;:1149,&quot;href&quot;:&quot;https:\\\/\\\/www.deeplearningbook.org\\\/contents\\\/autoencoders.html&quot;,&quot;archived_href&quot;:&quot;http:\\\/\\\/web-wp.archive.org\\\/web\\\/20250820165039\\\/https:\\\/\\\/www.deeplearningbook.org\\\/contents\\\/autoencoders.html&quot;,&quot;redirect_href&quot;:&quot;&quot;,&quot;checks&quot;:[{&quot;date&quot;:&quot;2025-12-09 01:55:48&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-01-31 08:05:46&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-02-12 06:32:18&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-03-15 10:52:23&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-06-15 16:06:39&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-06-21 20:41:40&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-07-01 15:33:47&quot;,&quot;http_code&quot;:206},{&quot;date&quot;:&quot;2026-07-13 05:27:57&quot;,&quot;http_code&quot;:206}],&quot;broken&quot;:false,&quot;last_checked&quot;:{&quot;date&quot;:&quot;2026-07-13 05:27:57&quot;,&quot;http_code&quot;:206},&quot;process&quot;:&quot;done&quot;}]\"><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Welcome back to DataFlair Keras Tutorial series.\u00a0In this Keras tutorial, we will walk through deep learning with keras and an important deep learning algorithm used in keras.\u00a0We will study the applications of this algorithm&#46;&#46;&#46;<\/p>\n","protected":false},"author":10,"featured_media":77664,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[22185],"tags":[22203,22205,22204],"class_list":["post-77663","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-keras","tag-deep-learning-with-keras","tag-keras-deep-learning","tag-tensorflow-keras"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.0 - 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