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 TensorFlow
Using 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
$ pip install matplotlib
$ pip install tensorflow
Steps to follow
Step 1 : Importing all dependence
Python3
import numpy as npimport matplotlib.pyplot as pltimport tensorflow as tflearn = tf.contrib.learntf.logging.set_verbosity(tf.logging.ERROR) |
Step 2 : Importing Dataset using MNIST Data
Python3
mnist = learn.datasets.load_dataset('mnist')data = mnist.train.imageslabels = np.asarray(mnist.train.labels, dtype=np.int32)test_data = mnist.test.imagestest_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 = 10000data = 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 pxdisplay(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 librariesimport numpy as npimport matplotlib.pyplot as pltimport tensorflow as tflearn = tf.contrib.learntf.logging.set_verbosity(tf.logging.ERROR)\# importing dataset using MNIST# this is how mnist is used mnist contain test and train datasetmnist = learn.datasets.load_dataset('mnist')data = mnist.train.imageslabels = np.asarray(mnist.train.labels, dtype = np.int32)test_data = mnist.test.imagestest_labels = np.asarray(mnist.test.labels, dtype = np.int32)max_examples = 10000data = data[:max_examples]labels = labels[:max_examples]# displaying dataset using Matplotlibdef 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 classifierfeature_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 accuracyclassifier.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.keras
Deep 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
$ pip install matplotlib
$ pip install tensorflow
Steps to follow
Step 1 : Importing all dependence
Python3
import tensorflow as tfimport numpy as npimport 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)))#reshapemodel.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 usedmodel.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]) |
