// pseudo code
x = conv2d(x, filters=32, kernel=[3,3])->batch_norm()->relu()
x = conv2d(x, filters=32, kernel=[3,3])->batch_norm()->relu()
x = conv2d(x, filters=32, kernel=[3,3])->batch_norm()->relu()
x = maxpool(x, size=[2,2], stride=[2,2])

x = conv2d(x, filters=64, kernel=[3,3])->batch_norm()->relu()
x = conv2d(x, filters=64, kernel=[3,3])->batch_norm()->relu()
x = conv2d(x, filters=64, kernel=[3,3])->batch_norm()->relu()
x = maxpool(x, size=[2,2], stride=[2,2])

x = conv2d(x, filters=128, kernel=[3,3])->batch_norm()->relu()
x = conv2d(x, filters=128, kernel=[3,3])->batch_norm()->relu()
x = conv2d(x, filters=128, kernel=[3,3])->batch_norm()->relu()
x = maxpool(x, size=[2,2], stride=[2,2])

x = dropout()->conv2d(x, filters=128, kernel=[1, 1])->batch_norm()->relu()
x = dropout()->conv2d(x, filters=32, kernel=[1, 1])->batch_norm()->relu()

y = dense(x, units=1)

// loss = mean_squared_error(y, labels)