The dataset contains 43 different classes of images.
STEP 1: THE FIRST CONVOLUTIONAL LAYER #1 Input = 32x32x1 Output = 28x28x6 Output = (Input-filter+1)/Stride* => (32-5+1)/1=28 Used a 5x5 Filter with input depth of 3 and output depth of 6 Apply a RELU Activation function to the output pooling for input, Input = 28x28x6 and Output = 14x14x6
STEP 2: THE SECOND CONVOLUTIONAL LAYER #2 Input = 14x14x6 Output = 10x10x16 Layer 2: Convolutional layer with Output = 10x10x16 Output = (Input-filter+1)/strides => 10 = 14-5+1/1 Apply a RELU Activation function to the output Pooling with Input = 10x10x16 and Output = 5x5x16
STEP 3: FLATTENING THE NETWORK Flatten the network with Input = 5x5x16 and Output = 400
STEP 4: FULLY CONNECTED LAYER Layer 3: Fully Connected layer with Input = 400 and Output = 120 Apply a RELU Activation function to the output
STEP 5: ANOTHER FULLY CONNECTED LAYER Layer 4: Fully Connected Layer with Input = 120 and Output = 84 Apply a RELU Activation function to the output
STEP 6: FULLY CONNECTED LAYER Layer 5: Fully Connected layer with Input = 84 and Output = 43