Python | Classify Handwritten Digits with Tensorflow Last Updated : 08 May, 2024 Comments Improve Suggest changes Like Article Like Report Classifying handwritten digits is the basic problem of the machine learning and can be solved in many ways here we will implement them by using TensorFlowUsing a Linear Classifier Algorithm with tf.contrib.learn linear classifier achieves the classification of handwritten digits by making a choice based on the value of a linear combination of the features also known as feature values and is typically presented to the machine in a vector called a feature vector. Modules required :NumPy: $ pip install numpy Matplotlib: $ pip install matplotlib Tensorflow: $ pip install tensorflow  Steps to follow Step 1 : Importing all dependence  Python3 import numpy as np import matplotlib.pyplot as plt import tensorflow as tf learn = tf.contrib.learn tf.logging.set_verbosity(tf.logging.ERROR) Step 2 : Importing Dataset using MNIST Data  Python3 mnist = learn.datasets.load_dataset('mnist') data = mnist.train.images labels = np.asarray(mnist.train.labels, dtype=np.int32) test_data = mnist.test.images test_labels = np.asarray(mnist.test.labels, dtype=np.int32) after this step a dataset of mnist will be downloaded. output :  Extracting MNIST-data/train-images-idx3-ubyte.gz Extracting MNIST-data/train-labels-idx1-ubyte.gz Extracting MNIST-data/t10k-images-idx3-ubyte.gz Extracting MNIST-data/t10k-labels-idx1-ubyte.gz Step 3 : Making dataset Python3 max_examples = 10000 data = data[:max_examples] labels = labels[:max_examples] Step 4 : Displaying dataset using MatplotLib  Python3 def display(i): img = test_data[i] plt.title('label : {}'.format(test_labels[i])) plt.imshow(img.reshape((28, 28))) # image in TensorFlow is 28 by 28 px display(0) To display data we can use this function - display(0) output :  Step 5 : Fitting data, using linear classifier  Python3 feature_columns = learn.infer_real_valued_columns_from_input(data) classifier = learn.LinearClassifier(n_classes=10, feature_columns=feature_columns) classifier.fit(data, labels, batch_size=100, steps=1000) Step 6 : Evaluate accuracy  Python3 classifier.evaluate(test_data, test_labels) print(classifier.evaluate(test_data, test_labels)["accuracy"]) Output :  0.9137 Step 7 : Predicting data  Python3 prediction = classifier.predict(np.array([test_data[0]], dtype=float), as_iterable=False) print("prediction : {}, label : {}".format(prediction, test_labels[0]) ) Output :  prediction : [7], label : 7 Full Code for classifying handwritten  Python3 # importing libraries import numpy as np import matplotlib.pyplot as plt import tensorflow as tf learn = tf.contrib.learn tf.logging.set_verbosity(tf.logging.ERROR)\ # importing dataset using MNIST # this is how mnist is used mnist contain test and train dataset mnist = learn.datasets.load_dataset('mnist') data = mnist.train.images labels = np.asarray(mnist.train.labels, dtype = np.int32) test_data = mnist.test.images test_labels = np.asarray(mnist.test.labels, dtype = np.int32) max_examples = 10000 data = data[:max_examples] labels = labels[:max_examples] # displaying dataset using Matplotlib def display(i): img = test_data[i] plt.title('label : {}'.format(test_labels[i])) plt.imshow(img.reshape((28, 28))) # img in tf is 28 by 28 px # fitting linear classifier feature_columns = learn.infer_real_valued_columns_from_input(data) classifier = learn.LinearClassifier(n_classes = 10, feature_columns = feature_columns) classifier.fit(data, labels, batch_size = 100, steps = 1000) # Evaluate accuracy classifier.evaluate(test_data, test_labels) print(classifier.evaluate(test_data, test_labels)["accuracy"]) prediction = classifier.predict(np.array([test_data[0]], dtype=float), as_iterable=False) print("prediction : {}, label : {}".format(prediction, test_labels[0]) ) if prediction == test_labels[0]: display(0) Using Deep learning with tf.kerasDeep learning is a subpart of machine learning and artificial intelligence which is also known as deep neural network this networks capable of learning unsupervised from provided data which is unorganized or unlabeled. today, we will implement a neural network in TensorFlow to classify handwritten digit.Modules required :NumPy: $ pip install numpy Matplotlib: $ pip install matplotlib Tensorflow: $ pip install tensorflow  Steps to follow Step 1 : Importing all dependence  Python3 import tensorflow as tf import numpy as np import matplotlib.pyplot as plt Step 2 : Import data and normalize it  Python3 mnist = tf.keras.datasets.mnist (x_train,y_train) , (x_test,y_test) = mnist.load_data() x_train = tf.keras.utils.normalize(x_train,axis=1) x_test = tf.keras.utils.normalize(x_test,axis=1) Step 3 : view data  Python3 def draw(n): plt.imshow(n,cmap=plt.cm.binary) plt.show() draw(x_train[0]) Step 4 : make a neural network and train it  Python3 #there are two types of models #sequential is most common, why? model = tf.keras.models.Sequential() model.add(tf.keras.layers.Flatten(input_shape=(28, 28))) #reshape model.add(tf.keras.layers.Dense(128,activation=tf.nn.relu)) model.add(tf.keras.layers.Dense(128,activation=tf.nn.relu)) model.add(tf.keras.layers.Dense(10,activation=tf.nn.softmax)) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'] ) model.fit(x_train,y_train,epochs=3) Step 5 : check model accuracy and loss  Python3 val_loss,val_acc = model.evaluate(x_test,y_test) print("loss-> ",val_loss,"\nacc-> ",val_acc) Step 6 : prediction using model  Python3 predictions=model.predict([x_test]) print('label -> ',y_test[2]) print('prediction -> ',np.argmax(predictions[2])) draw(x_test[2]) saving and testing model  saving the model  Python3 #saving the model # .h5 or .model can be used model.save('epic_num_reader.h5') loading the saved model Python3 new_model = tf.keras.models.load_model('epic_num_reader.h5') prediction using new model Python3 predictions=new_model.predict([x_test]) print('label -> ',y_test[2]) print('prediction -> ',np.argmax(predictions[2])) draw(x_test[2]) Comment More infoAdvertise with us Next Article Recognizing HandWritten Digits in Scikit Learn I itsvinayak Follow Improve Article Tags : Technical Scripter TechTips Machine Learning Tensorflow Artificial Intelligence python +2 More Practice Tags : Machine Learningpython Similar Reads 100+ Machine Learning Projects with Source Code [2025] This article provides over 100 Machine Learning projects and ideas to provide hands-on experience for both beginners and professionals. 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It used in various artificial intelligence applications such as home automation, speech to text, etc. In this article, you’ll learn how to do basic speech recognition in Python using the Google Speech Recognition API.Step 1: Install Require 2 min read Voice Assistant using pythonSpeech recognition is the process of turning spoken words into text. It is a key part of any voice assistant. In Python the SpeechRecognition module helps us do this by capturing audio and converting it to text. In this guide we’ll create a basic voice assistant using Python.Step 1: Install Required 3 min read Human Activity Recognition - Using Deep Learning ModelHuman activity recognition using smartphone sensors like accelerometer is one of the hectic topics of research. HAR is one of the time series classification problem. In this project various machine learning and deep learning models have been worked out to get the best final result. In the same seque 6 min read Fine-tuning BERT model for Sentiment AnalysisGoogle created a transformer-based machine learning approach for natural language processing pre-training called Bidirectional Encoder Representations from Transformers. It has a huge number of parameters, hence training it on a small dataset would lead to overfitting. This is why we use a pre-train 6 min read Sentiment Classification Using BERTBERT stands for Bidirectional Representation for Transformers and was proposed by researchers at Google AI language in 2018. Although the main aim of that was to improve the understanding of the meaning of queries related to Google Search, BERT becomes one of the most important and complete architec 12 min read Sentiment Analysis with an Recurrent Neural Networks (RNN)Recurrent Neural Networks (RNNs) are used in sequence tasks such as sentiment analysis due to their ability to capture context from sequential data. In this article we will be apply RNNs to analyze the sentiment of customer reviews from Swiggy food delivery platform. The goal is to classify reviews 5 min read Building an Autocorrector Using NLP in PythonAutocorrect feature predicts and correct misspelled words, it helps to save time invested in the editing of articles, emails and reports. This feature is added many websites and social media platforms to ensure easy typing. In this tutorial we will build a Python-based autocorrection feature using N 4 min read Python | NLP analysis of Restaurant reviewsNatural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. It is the branch of mach 7 min read Restaurant Review Analysis Using NLP and SQLiteNormally, a lot of businesses are remained as failures due to lack of profit, lack of proper improvement measures. Mostly, restaurant owners face a lot of difficulties to improve their productivity. This project really helps those who want to increase their productivity, which in turn increases thei 9 min read Twitter Sentiment Analysis using PythonThis article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mini 10 min read Clustering ProjectsCustomer Segmentation using Unsupervised Machine Learning in PythonCustomer Segmentation involves grouping customers based on shared characteristics, behaviors and preferences. By segmenting customers, businesses can tailor their strategies and target specific groups more effectively and enhance overall market value. Today we will use Unsupervised Machine Learning 5 min read Music Recommendation System Using Machine LearningWhen did we see a video on youtube let's say it was funny then the next time you open your youtube app you get recommendations of some funny videos in your feed ever thought about how? This is nothing but an application of Machine Learning using which recommender systems are built to provide persona 4 min read K means Clustering – IntroductionK-Means Clustering is an Unsupervised Machine Learning algorithm which groups unlabeled dataset into different clusters. It is used to organize data into groups based on their similarity. Understanding K-means ClusteringFor example online store uses K-Means to group customers based on purchase frequ 4 min read Image Segmentation using K Means ClusteringImage segmentation is a technique in computer vision that divides an image into different segments. This can help identify specific objects, boundaries or patterns in the image. Image is basically a set of given pixels and in image segmentation pixels with similar intensity are grouped together. Im 2 min read Recommender System Project Like