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Classify Handwritten Digits using Deep learning with Tensorflow
by vinayakApril 24th, 2020
Too Long; Didn't Read
Deep learning is a subpart of machine learning and artificial intelligence which is also known as deep neural network this networks are capable of learning unsupervised from provided data which is unorganized or unlabeled. today, we will implement a neural network in 6 easy steps using TensorFlow to classify handwritten digits. We will use NumPy, Matplotlib, NumPy and Tensorflow to build a deep learning neural network. We'll use these modules to train our neural network and make a prediction.
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 6 easy steps using TensorFlow to classify handwritten digits.
Modules required :
NumPy:
$ pip install numpy Matplotlib:
$ pip install matplotlib Tensorflow:
$ pip install tensorflow Steps to follow:
Step 1 : Importing all dependence
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as pltStep 2 : Import data and normalize it
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
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
#there are two types of models
#sequential is most common
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
val_loss,val_acc = model.evaluate(x_test,y_test)
print("loss-> ",val_loss,"\nacc-> ",val_acc)
Step 6 : prediction using model
predictions=model.predict([x_test])
print('lable -> ',y_test[2])
print('prediction -> ',np.argmax(predictions[2]))
draw(x_test[2])
L O A D I N G
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