Defines a CNN model using Keras sequential model
  :param logits: If set to False, returns a Keras model, otherwise will also
                  return logits tensor
  :param input_ph: The TensorFlow tensor for the input
                  (needed if returning logits)
                  ("ph" stands for placeholder but it need not actually be a
                  placeholder)
  :param img_rows: number of row in the image
  :param img_cols: number of columns in the image
  :param channels: number of color channels (e.g., 1 for MNIST)
  :param nb_filters: number of convolutional filters per layer
  :param nb_classes: the number of output classes
  :return:

def cnn_model(logits=False, input_ph=None, img_rows=28, img_cols=28,
              channels=1, nb_filters=64, nb_classes=10):
  """
  Defines a CNN model using Keras sequential model
  :param logits: If set to False, returns a Keras model, otherwise will also
                  return logits tensor
  :param input_ph: The TensorFlow tensor for the input
                  (needed if returning logits)
                  ("ph" stands for placeholder but it need not actually be a
                  placeholder)
  :param img_rows: number of row in the image
  :param img_cols: number of columns in the image
  :param channels: number of color channels (e.g., 1 for MNIST)
  :param nb_filters: number of convolutional filters per layer
  :param nb_classes: the number of output classes
  :return:
  """
  model = Sequential()

  # Define the layers successively (convolution layers are version dependent)
  if tf.keras.backend.image_data_format() == 'channels_first':
    input_shape = (channels, img_rows, img_cols)
  else:
    assert tf.keras.backend.image_data_format() == 'channels_last'
    input_shape = (img_rows, img_cols, channels)

  layers = [conv_2d(nb_filters, (8, 8), (2, 2), "same",
                    input_shape=input_shape),
            Activation('relu'),
            conv_2d((nb_filters * 2), (6, 6), (2, 2), "valid"),
            Activation('relu'),
            conv_2d((nb_filters * 2), (5, 5), (1, 1), "valid"),
            Activation('relu'),
            Flatten(),
            Dense(nb_classes)]

  for layer in layers:
    model.add(layer)

  if logits:
    logits_tensor = model(input_ph)
  model.add(Activation('softmax'))

  if logits:
    return model, logits_tensor
  else:
    return model