Setting up venv and tf

cd ~
mkdir .envs
cd .envs
mkdir mnist_cnn_tf
virtualenv --no-site-packages -p python3 ./mnist_cnn_tf
cd #project-directory
source ~/.envs/mnist_cnn_tf/bin/activate
pip3 install --upgrade tensorflow

After installing new modules via pip

pip3 freeze > requirements.txt
cat requirements.txt

CNN Architecture

Methods in layers module expect input tensors to have shape [batch_size, image_height, image_width, channels].

Under 'SAME' padding scheme, output is calulated as such:

out_height = ceil(float(in_height) / float(strides[1]))
out_width  = ceil(float(in_width) / float(strides[2]))

Under 'VALID' padding scheme, output is calulated as such:

out_height = ceil(float(in_height - filter_height + 1) / float(strides[1]))
out_width  = ceil(float(in_width - filter_width + 1) / float(strides[2]))

1. Input Layer (MNIST data)

   Output Shape: [-1, 28, 28, 1]

2. Convolution Layer 1

   Input Shape: [-1, 28, 28, 1]
   Filter Shape: [5, 5]
   Number of Filters: 32
   Strides Shape: [1, 1]
   Output Shape (Same Padding): [-1, 28, 28, 32]
   Activation Function: ReLU

3. Pooling Layer 1

   Input Shape: [-1, 28, 28, 32]
   Filter Shape: [2, 2]
   Strides Shape: [2, 2]
   Output Shape (Valid Padding): [-1, 14, 14, 32]

4. Convolution Layer 2

   Input Shape: [-1, 14, 14, 32]
   Filter Shape: [5, 5]
   Number of Filters: 64
   Strides Shape: [1, 1]
   Output Shape (Same Padding): [-1, 14, 14, 64]
   Activation Function: ReLU

5. Pooling Layer 2

   Input Shape: [-1, 14, 14, 64]
   Filter Shape: [2, 2]
   Strides Shape: [2, 2]
   Output Shape (Valid Padding): [-1, 7, 7, 64]

6. Dense Layer 1

   Input Shape: [-1, 7 * 7 * 64]
   Number of Neurons: 1024
   Output Shape: [-1, 1024]
   Activation Function: ReLU

7. Dropout Layer 1

   Input Shape: [-1, 1024]
   Dropout Rate: 0.4
   Output Shape: [-1, 1024]

8. Dense Layer 2 (Logits)

   Input Shape: [-1, 1024] Number of Neurons: 10 Output Shape: [-1, 10]