Question

我有一个多次使用np.random的程序。现在，我不想让用户传递参数gpu=True/False。如何覆盖np.random返回cm.CUDAMatrix(np.random.uniform(low=low, high=high, size=size))而不以递归结束？或者有更好的方法来使用cudamat进行小代码更改吗？

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class FeedForwardNetwork():

    def __init__(self, input_dim, hidden_dim, output_dim, dropout=False, dropout_prop=0.5, gpu=True):            
        np.random.seed(1)
        self.input_layer = np.array([])
        self.hidden_layer = np.array([])
        self.output_layer = np.array([])
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        self.dropout = dropout
        self.dropout_prop = dropout_prop

        r_input_hidden = math.sqrt(6 / (input_dim + hidden_dim))
        r_hidden_output = math.sqrt(6 / (hidden_dim + output_dim))

        self.weights_input_hidden = np.random.uniform(low=-0.01, high=0.01, size=(input_dim, hidden_dim))
        self.weights_hidden_output = np.random.uniform(low=-0.01, high=0.01, size=(hidden_dim, output_dim))

Answer 1

class FeedForwardNetwork():

def __init__(self, input_dim, hidden_dim, output_dim, dropout=False, dropout_prop=0.5, gpu=True):            
    np.random.seed(1)
    self.input_layer = np.array([])
    self.hidden_layer = np.array([])
    self.output_layer = np.array([])
    self.input_dim = input_dim
    self.hidden_dim = hidden_dim
    self.output_dim = output_dim
    self.dropout = dropout
    self.dropout_prop = dropout_prop

    r_input_hidden = math.sqrt(6 / (input_dim + hidden_dim))
    r_hidden_output = math.sqrt(6 / (hidden_dim + output_dim))

    self.weights_input_hidden = np.random.uniform(low=-0.01, high=0.01, size=(input_dim, hidden_dim))
    self.weights_hidden_output = np.random.uniform(low=-0.01, high=0.01, size=(hidden_dim, output_dim))

def np_random(self, gpu):
   '''gpu:bool'''
     if gpu:
         return np.random.uniform(low=-0.01, high=0.01, size=(self.input_dim, self.hidden_dim))
    else:
         return np.random.uniform(low=-0.01, high=0.01, size=(self.hidden_dim, self.output_dim))

然后你可以从你的实例中调用它：

instance = FeedForwardNetwork(**kwargs)

instance.np_random(True/False)

覆盖numpy.random以使用cudamat

1 个答案: