私たちがmxnetで必要としているようにカーネルを制御する方法

Question

例：テンソルフローでは次のようにすることができます。mxnetでカーネルを制御するにはどうすればいいですか（重み=重み*マスク）。

if mask_type is not None: 
     #C 
     mask[:center_h, :, :, :] = 1 
     if mask_type == 'A': 
     mask[center_h, :center_w, :, :] = 1 

     if mask_type == 'B': 
     mask[center_h, :center_w+1, :, :] = 1 

    else: 
     mask[:, :, :, :] = 1 

    weights_shape = [kernel_h, kernel_w, in_channel, num_outputs] 
    weights = tf.get_variable("weights", weights_shape, 
     tf.float32, tf.truncated_normal_initializer(stddev=0.1)) 
    weights = weights * mask 
    biases = tf.get_variable("biases", [num_outputs], 
      tf.float32, tf.constant_initializer(0.0)) 

    outputs = tf.nn.conv2d(inputs, weights, [1, stride_h, stride_w, 1], padding="SAME") 
    outputs = tf.nn.bias_add(outputs, biases) 

＃ #########これはtensorflow

def conv2d(inputs, num_outputs, kernel_shape, strides=[1, 1], mask_type=None, scope="conv2d"): 
    with tf.variable_scope(scope) as scope: 
    kernel_h, kernel_w = kernel_shape 
    stride_h, stride_w = strides 
    batch_size, height, width, in_channel = inputs.get_shape().as_list() 

    center_h = kernel_h // 2 
    center_w = kernel_w // 2 

    assert kernel_h % 2 == 1 and kernel_w % 2 == 1, "kernel height and width must be odd number" 
    mask = np.zeros((kernel_h, kernel_w, in_channel, num_outputs), dtype=np.float32) 
    if mask_type is not None: 
     #C 
     mask[:center_h, :, :, :] = 1 
     if mask_type == 'A': 
     mask[center_h, :center_w, :, :] = 1 

     if mask_type == 'B': 
     mask[center_h, :center_w+1, :, :] = 1 

    else: 
     mask[:, :, :, :] = 1 

    weights_shape = [kernel_h, kernel_w, in_channel, num_outputs] 
    weights = tf.get_variable("weights", weights_shape, 
     tf.float32, tf.truncated_normal_initializer(stddev=0.1)) 
    weights = weights * mask 
    biases = tf.get_variable("biases", [num_outputs], 
      tf.float32, tf.constant_initializer(0.0)) 

    outputs = tf.nn.conv2d(inputs, weights, [1, stride_h, stride_w, 1], padding="SAME") 
    outputs = tf.nn.bias_add(outputs, biases) 

    return outputs 

Answer 1

によって完全なコードです申し訳ありませんが、私はあなたが求めているものを理解していないが？