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codebasics
GitHub Repository: codebasics/deep-learning-keras-tf-tutorial
 Path: blob/master/1_digits_recognition/digits_recognition_neural_network.ipynb
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Kernel: Python 3

Handwritten digits classification using neural network

In this notebook we will classify handwritten digits using a simple neural network which has only input and output layers. We will than add a hidden layer and see how the performance of the model improves

import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np

(X_train, y_train) , (X_test, y_test) = keras.datasets.mnist.load_data()

len(X_train)
60000
len(X_test)
10000
X_train[0].shape
(28, 28)
X_train[0]
array([[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 18, 18, 18, 126, 136, 175, 26, 166, 255, 247, 127, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 30, 36, 94, 154, 170, 253, 253, 253, 253, 253, 225, 172, 253, 242, 195, 64, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 49, 238, 253, 253, 253, 253, 253, 253, 253, 253, 251, 93, 82, 82, 56, 39, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 18, 219, 253, 253, 253, 253, 253, 198, 182, 247, 241, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 80, 156, 107, 253, 253, 205, 11, 0, 43, 154, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 1, 154, 253, 90, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 139, 253, 190, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 190, 253, 70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 35, 241, 225, 160, 108, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 81, 240, 253, 253, 119, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 45, 186, 253, 253, 150, 27, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16, 93, 252, 253, 187, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 249, 253, 249, 64, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 46, 130, 183, 253, 253, 207, 2, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39, 148, 229, 253, 253, 253, 250, 182, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 114, 221, 253, 253, 253, 253, 201, 78, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 23, 66, 213, 253, 253, 253, 253, 198, 81, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 18, 171, 219, 253, 253, 253, 253, 195, 80, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 55, 172, 226, 253, 253, 253, 253, 244, 133, 11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 136, 253, 253, 253, 212, 135, 132, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
plt.matshow(X_train[0])
<matplotlib.image.AxesImage at 0x1fe79cb99e8>
Image in a Jupyter notebook
y_train[0]
5
X_train = X_train / 255
X_test = X_test / 255

X_train[0]
array([[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.01176471, 0.07058824, 0.07058824, 0.07058824, 0.49411765, 0.53333333, 0.68627451, 0.10196078, 0.65098039, 1. , 0.96862745, 0.49803922, 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.11764706, 0.14117647, 0.36862745, 0.60392157, 0.66666667, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.88235294, 0.6745098 , 0.99215686, 0.94901961, 0.76470588, 0.25098039, 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.19215686, 0.93333333, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.98431373, 0.36470588, 0.32156863, 0.32156863, 0.21960784, 0.15294118, 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.07058824, 0.85882353, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.77647059, 0.71372549, 0.96862745, 0.94509804, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.31372549, 0.61176471, 0.41960784, 0.99215686, 0.99215686, 0.80392157, 0.04313725, 0. , 0.16862745, 0.60392157, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.05490196, 0.00392157, 0.60392157, 0.99215686, 0.35294118, 0. , 0. , 0. , 0. , 0. , 0. 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0.77647059, 0.31764706, 0.00784314, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0.07058824, 0.67058824, 0.85882353, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.76470588, 0.31372549, 0.03529412, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0.21568627, 0.6745098 , 0.88627451, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.95686275, 0.52156863, 0.04313725, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0.53333333, 0.99215686, 0.99215686, 0.99215686, 0.83137255, 0.52941176, 0.51764706, 0.0627451 , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ], [0. , 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X_train_flattened = X_train.reshape(len(X_train), 28*28)
X_test_flattened = X_test.reshape(len(X_test), 28*28)

X_train_flattened.shape
(60000, 784)
X_train_flattened[0]
array([0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.01176471, 0.07058824, 0.07058824, 0.07058824, 0.49411765, 0.53333333, 0.68627451, 0.10196078, 0.65098039, 1. , 0.96862745, 0.49803922, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.11764706, 0.14117647, 0.36862745, 0.60392157, 0.66666667, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.88235294, 0.6745098 , 0.99215686, 0.94901961, 0.76470588, 0.25098039, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.19215686, 0.93333333, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.98431373, 0.36470588, 0.32156863, 0.32156863, 0.21960784, 0.15294118, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.07058824, 0.85882353, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.77647059, 0.71372549, 0.96862745, 0.94509804, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.31372549, 0.61176471, 0.41960784, 0.99215686, 0.99215686, 0.80392157, 0.04313725, 0. , 0.16862745, 0.60392157, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.05490196, 0.00392157, 0.60392157, 0.99215686, 0.35294118, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.54509804, 0.99215686, 0.74509804, 0.00784314, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.04313725, 0.74509804, 0.99215686, 0.2745098 , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.1372549 , 0.94509804, 0.88235294, 0.62745098, 0.42352941, 0.00392157, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.31764706, 0.94117647, 0.99215686, 0.99215686, 0.46666667, 0.09803922, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.17647059, 0.72941176, 0.99215686, 0.99215686, 0.58823529, 0.10588235, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.0627451 , 0.36470588, 0.98823529, 0.99215686, 0.73333333, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.97647059, 0.99215686, 0.97647059, 0.25098039, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.18039216, 0.50980392, 0.71764706, 0.99215686, 0.99215686, 0.81176471, 0.00784314, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.15294118, 0.58039216, 0.89803922, 0.99215686, 0.99215686, 0.99215686, 0.98039216, 0.71372549, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.09411765, 0.44705882, 0.86666667, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.78823529, 0.30588235, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.09019608, 0.25882353, 0.83529412, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.77647059, 0.31764706, 0.00784314, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.07058824, 0.67058824, 0.85882353, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.76470588, 0.31372549, 0.03529412, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.21568627, 0.6745098 , 0.88627451, 0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.95686275, 0.52156863, 0.04313725, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.53333333, 0.99215686, 0.99215686, 0.99215686, 0.83137255, 0.52941176, 0.51764706, 0.0627451 , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ])

Very simple neural network with no hidden layers

model = keras.Sequential([
    keras.layers.Dense(10, input_shape=(784,), activation='sigmoid')
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(X_train_flattened, y_train, epochs=5)
Epoch 1/5 1875/1875 [==============================] - 3s 1ms/step - loss: 0.4886 - accuracy: 0.8775 Epoch 2/5 1875/1875 [==============================] - 3s 1ms/step - loss: 0.3060 - accuracy: 0.9156 Epoch 3/5 1875/1875 [==============================] - 2s 1ms/step - loss: 0.2848 - accuracy: 0.9214 Epoch 4/5 1875/1875 [==============================] - 2s 1ms/step - loss: 0.2747 - accuracy: 0.9243 Epoch 5/5 1875/1875 [==============================] - 2s 1ms/step - loss: 0.2677 - accuracy: 0.9262
<tensorflow.python.keras.callbacks.History at 0x1fe24f47a90>
model.evaluate(X_test_flattened, y_test)
313/313 [==============================] - 0s 985us/step - loss: 0.2670 - accuracy: 0.9257
[0.26697656512260437, 0.9257000088691711]
y_predicted = model.predict(X_test_flattened)
y_predicted[0]
array([1.7270680e-05, 1.3593615e-10, 4.5622761e-05, 7.5602829e-03, 1.3076769e-06, 7.5061922e-05, 1.7646971e-09, 6.9968843e-01, 7.8440302e-05, 8.1232190e-04], dtype=float32)
plt.matshow(X_test[0])
<matplotlib.image.AxesImage at 0x1fe2322e3c8>
Image in a Jupyter notebook

np.argmax finds a maximum element from an array and returns the index of it

np.argmax(y_predicted[0])
7
y_predicted_labels = [np.argmax(i) for i in y_predicted]

y_predicted_labels[:5]
[7, 2, 1, 0, 4]
cm = tf.math.confusion_matrix(labels=y_test,predictions=y_predicted_labels)
cm
<tf.Tensor: shape=(10, 10), dtype=int32, numpy= array([[ 960, 0, 0, 2, 0, 5, 10, 2, 1, 0], [ 0, 1109, 3, 2, 0, 1, 4, 2, 14, 0], [ 7, 7, 929, 11, 5, 4, 15, 8, 42, 4], [ 3, 0, 23, 910, 0, 27, 6, 11, 23, 7], [ 1, 1, 2, 1, 915, 0, 16, 4, 10, 32], [ 11, 2, 2, 27, 9, 773, 23, 4, 33, 8], [ 7, 3, 5, 1, 7, 7, 925, 2, 1, 0], [ 1, 6, 25, 5, 8, 0, 0, 941, 2, 40], [ 7, 5, 7, 18, 9, 22, 11, 8, 880, 7], [ 11, 7, 1, 10, 32, 6, 0, 18, 9, 915]])>
import seaborn as sn
plt.figure(figsize = (10,7))
sn.heatmap(cm, annot=True, fmt='d')
plt.xlabel('Predicted')
plt.ylabel('Truth')
Text(69.0, 0.5, 'Truth')
Image in a Jupyter notebook

Using hidden layer

model = keras.Sequential([
    keras.layers.Dense(100, input_shape=(784,), activation='relu'),
    keras.layers.Dense(10, activation='sigmoid')
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(X_train_flattened, y_train, epochs=5)
Epoch 1/5 1875/1875 [==============================] - 3s 2ms/step - loss: 0.2925 - accuracy: 0.9191 Epoch 2/5 1875/1875 [==============================] - 3s 2ms/step - loss: 0.1366 - accuracy: 0.9602 Epoch 3/5 1875/1875 [==============================] - 3s 2ms/step - loss: 0.0981 - accuracy: 0.9703 Epoch 4/5 1875/1875 [==============================] - 3s 2ms/step - loss: 0.0764 - accuracy: 0.9768 Epoch 5/5 1875/1875 [==============================] - 3s 2ms/step - loss: 0.0618 - accuracy: 0.9812
<tensorflow.python.keras.callbacks.History at 0x1fe230e7128>
model.evaluate(X_test_flattened,y_test)
313/313 [==============================] - 0s 1ms/step - loss: 0.0966 - accuracy: 0.9716
[0.09658893942832947, 0.9715999960899353]
y_predicted = model.predict(X_test_flattened)
y_predicted_labels = [np.argmax(i) for i in y_predicted]
cm = tf.math.confusion_matrix(labels=y_test,predictions=y_predicted_labels)

plt.figure(figsize = (10,7))
sn.heatmap(cm, annot=True, fmt='d')
plt.xlabel('Predicted')
plt.ylabel('Truth')
Text(69.0, 0.5, 'Truth')
Image in a Jupyter notebook

Using Flatten layer so that we don't have to call .reshape on input dataset

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(100, activation='relu'),
    keras.layers.Dense(10, activation='sigmoid')
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(X_train, y_train, epochs=10)
Epoch 1/10 1875/1875 [==============================] - 3s 2ms/step - loss: 0.2959 - accuracy: 0.9185 Epoch 2/10 1875/1875 [==============================] - 3s 2ms/step - loss: 0.1368 - accuracy: 0.9603 Epoch 3/10 1875/1875 [==============================] - 3s 2ms/step - loss: 0.0995 - accuracy: 0.9703 Epoch 4/10 1875/1875 [==============================] - 3s 2ms/step - loss: 0.0771 - accuracy: 0.9772 Epoch 5/10 1875/1875 [==============================] - 3s 2ms/step - loss: 0.0628 - accuracy: 0.9806 Epoch 6/10 1875/1875 [==============================] - 3s 2ms/step - loss: 0.0519 - accuracy: 0.9841 Epoch 7/10 1875/1875 [==============================] - 3s 2ms/step - loss: 0.0442 - accuracy: 0.9865 Epoch 8/10 1875/1875 [==============================] - 3s 2ms/step - loss: 0.0369 - accuracy: 0.9886 Epoch 9/10 1875/1875 [==============================] - 3s 2ms/step - loss: 0.0300 - accuracy: 0.9910 Epoch 10/10 1875/1875 [==============================] - 3s 2ms/step - loss: 0.0264 - accuracy: 0.9917
<tensorflow.python.keras.callbacks.History at 0x1fe24629e80>
model.evaluate(X_test,y_test)
313/313 [==============================] - 0s 1ms/step - loss: 0.0813 - accuracy: 0.9779
[0.08133944123983383, 0.9779000282287598]