conv = Conv3x3(8)                  # 28x28x1 -> 26x26x8
pool = MaxPool2()                  # 26x26x8 -> 13x13x8
softmax = Softmax(13 * 13 * 8, 10) # 13x13x8 -> 10

def forward(image, label):
  '''
  Completes a forward pass of the CNN and calculates the accuracy and
  cross-entropy loss.
  - image is a 2d numpy array
  - label is a digit
  '''
  # We transform the image from [0, 255] to [-0.5, 0.5] to make it easier
  # to work with. This is standard practice.
  out = conv.forward((image / 255) - 0.5)
  out = pool.forward(out)
  out = softmax.forward(out)

  # Calculate cross-entropy loss and accuracy. np.log() is the natural log.
  loss = -np.log(out[label])
  acc = 1 if np.argmax(out) == label else 0

  return out, loss, acc