Number_README.md

Link of the trained model

• link of the jupyter notebook here

Training and validation accuracy and loss

Example of prediction of number

Confusion Matrix For Number

Evaluation and Training accuracies For Number

• Evaluation Accuracy: 0.9953

• Training Accuracy: 0.9997

Accuracy, Precision, Recall, F1 Score for all class For Number

Overall	Accuracy	Precision	Recall	F1 Score
All	0.9952	0.9952	0.9952	0.9952

Accuracy, Precision, Recall, F1 Score for different class For Number

Class	Number of True Samples	Number of Classified Samples	Accuracy	Precision	Recall	F1 Score
0	999	1010	1.0	0.9891	1.0	0.9945
1	999	991	0.989	0.997	0.989	0.9929
2	999	1006	0.998	0.9911	0.998	0.9945
3	999	1000	0.995	0.994	0.995	0.9945
4	999	1000	0.995	0.994	0.995	0.9945
5	999	997	0.995	0.997	0.995	0.9959
6	998	992	0.994	1.0	0.994	0.997
7	999	1001	0.999	0.997	0.999	0.998
8	999	1004	0.999	0.994	0.999	0.9965
9	999	988	0.988	0.998	0.988	0.9935

Model Summery

Layer (type)	Output Shape	Param #
conv2d	(None, 26, 26, 64)	640
activation	(None, 26, 26, 64)	0
max_pooling2d	(None, 25, 25, 64)	0
batch_normalization	(None, 25, 25, 64)	256
conv2d_1	(None, 23, 23, 128)	73856
activation_1	(None, 23, 23, 128)	0
max_pooling2d_1	(None, 22, 22, 128)	0
batch_normalization_1	(None, 22, 22, 128)	512
conv2d_2	(None, 22, 22, 192)	24768
activation_2	(None, 22, 22, 192)	0
batch_normalization_2	(None, 22, 22, 192)	768
conv2d_3	(None, 20, 20, 192)	331968
activation_3	(None, 20, 20, 192)	0
batch_normalization_3	(None, 20, 20, 192)	768
conv2d_4	(None, 18, 18, 128)	221312
activation_4	(None, 18, 18, 128)	0
max_pooling2d_2	(None, 17, 17, 128)	0
batch_normalization_4	(None, 17, 17, 128)	512
flatten	(None, 36992)	0
dense	(None, 2048)	75761664
activation_5	(None, 2048)	0
dropout	(None, 2048)	0
batch_normalization_5	(None, 2048)	8192
dense_1	(None, 2048)	4196352
activation_6	(None, 2048)	0
dropout_1	(None, 2048)	0
batch_normalization_6	(None, 2048)	8192
dense_2	(None, 800)	1639200
activation_7	(None, 800)	0
dropout_2	(None, 800)	0
batch_normalization_7	(None, 800)	3200
dense_3	(None, 35)	28035
activation_8	(None, 35)	0
Total params	82,300,195
Trainable params	82,288,995
Non-trainable params	11,200