MNIST dataset has 28x28 grayscale images of digits 0-9. The dataset has 60000 training images and 10000 testing images.
Model : Input(28x28x1) -> Conv1(28x28x4) -> ReLU(28x28x4) -> Conv2(28x28x8) -> ReLU(28x28x8) -> Max_Pool1(14x14x8) -> Conv3(14x14x16) -> ReLU(14x14x16) -> Conv4(14x14x32) -> ReLU(14x14x32) -> Max_Pool2(7x7x32) -> Fully_Connected_Layer1(1000) -> ReLU(1000) -> Fully_Connected_Layer2(10)
Kernel size: 5
Padding : 2
Stride : 1
Pool size : 2
Pool Stride : 2
Batch Size : 128
GPU used: No
Iterations : 4300
Test set Accuracy : 98.12%
Model: Input(28x28x1) -> Conv1(28x28x4) -> ReLU(28x28x32) -> Max_Pool1(14x14x32) -> Conv2(14x14x64) -> ReLU(14x14x64) -> Max_Pool2(7x7x64) -> Fully_Connected_Layer1(1024) -> ReLU(1024) -> Dropout(1024) -> Fully_Connected_Layer2(10) Kernel size: 5
Padding : 2
Stride : 1
Pool size : 2
Pool Stride : 2
Dropout Rate : 0.4
Batch Size : 128
GPU used : No
Iterations : 4300
Global Steps/sec : 2
Test set Accuracy : 97.99%
-Inspired from MobileNet
Summary Of MobileNet and Depthwise Convolutions
-Used Depthwise Sepereable Convolutions instead of Standard Convolutions
-Model is 3 times faster (Global steps/sec = 6 and time/100 steps = 20 sec)
-A slight accuracy drop of 1.5%
Model: Input(28x28x1) -> Depthwise_Separable_Conv1(28x28x4) -> ReLU(28x28x32) -> Max_Pool1(14x14x32) -> Depthwise_Separable_Conv2(14x14x64) -> ReLU(14x14x64) -> Max_Pool2(7x7x64) -> Fully_Connected_Layer1(1024) -> ReLU(1024) -> Dropout(1024) -> Fully_Connected_Layer2(10)
Kernel size: 5
Padding : 2
Stride : 1
Pool size : 2
Pool Stride : 2
Dropout Rate : 0.4
Width Multiplier : 1
Resolution Multiplier : 1
Batch Size : 128
GPU used : No
Iterations : 4300
Global Steps/sec : 6
Accuracy : 96.5%
