GRU的自定义实现不执行

Question

我在TensorFlow中创建了一个GRU的自定义实现来进行实验。在训练时，它不像原生TensorFlow GRUcell那样执行任何操作。我一直试图找出原因，而根本不能。任何输入将不胜感激。

原生实施：

encoder = tf.nn.rnn_cell.GRUCell(feature_no,activation = tf.nn.tanh)
encoding = tf.nn.dynamic_rnn(encoder,inputs,dtype=tf.float32)[1]

我的实施：

class GRU:
    def __init__(self, hid_dim, vec_dim,
                 name = None):
        self.hid_dim = hid_dim                  #Dimension of the hidden layer
        self.vec_dim = vec_dim                  #Dimension of the input layer
        ## Declare the variables
        ## Update gate
        self.W_ux = tf.get_variable(name = name + ".GRU.W_ux",
                                    shape = [vec_dim, hid_dim],
                                    initializer = tf.random_normal_initializer())
        self.W_uh = tf.get_variable(name = name + ".GRU.W_uh",
                                    shape = [hid_dim, hid_dim],
                                    initializer = tf.random_normal_initializer())
        self.b_u = tf.get_variable(name = name + ".GRU.g_u",
                                   shape = [hid_dim],
                                   initializer = tf.random_normal_initializer())
        ## Forget gate
        self.W_rx = tf.get_variable(name = name + ".GRUt.W_rx",
                                    shape = [vec_dim, hid_dim],
                                    initializer = tf.random_normal_initializer())
        self.W_rh = tf.get_variable(name = name + ".GRU.W_rh",
                                    shape = [hid_dim, hid_dim],
                                    initializer = tf.random_normal_initializer())
        self.b_r = tf.get_variable(name = name + ".GRU.b_r",
                                   shape = [hid_dim],
                                   initializer = tf.random_normal_initializer())
        ## Update function
        self.W_hx = tf.get_variable(name = name + ".GRU.W_hx",
                                    shape = [vec_dim, hid_dim],
                                    initializer = tf.random_normal_initializer())
        self.W_hh = tf.get_variable(name = name + ".GRU.W_hh",
                                    shape = [hid_dim, hid_dim],
                                    initializer = tf.random_normal_initializer())
        self.b_h = tf.get_variable(name = name + ".GRU.b_h",
                                   shape = [hid_dim],
                                   initializer = tf.random_normal_initializer())

    def update_state(self, x, h):
        u = tf.sigmoid(tf.matmul(x, self.W_ux) + tf.matmul(h, self.W_uh) +
                       self.b_u)

        ## 'Forget' gate
        r = tf.sigmoid(tf.matmul(x, self.W_rx) + tf.matmul(h, self.W_rh) + self.b_r)

        ## Hidden state
        hp = tf.tanh(tf.matmul(x, self.W_hx) + r*tf.matmul(h, self.W_hh)
                        + self.b_h)
        return (1 - u) * hp + u * h

    def get_states(self, x):
        init = tf.reshape(tf.tile(tf.zeros_like(x[:,0,0]),[self.hid_dim]),
                                            shape = [-1,self.hid_dim])
        x_t = tf.transpose(x,perm=[1,0,2])
        self.h_set = tf.transpose(tf.scan(lambda h, x: self.update_state(x, h),
                                          x_t, init), perm = [1,0,2])
        self.h = self.h_set[:,-1]

    def __call__(self, x):
        self.get_states(x)
        return self.h

encoder = GRU(feature_no,vec_dim,name='encoder')
encoding = encoder(sent)

Answer 1

发现了问题。我知道RNN对初始化非常敏感，但我使用的是tf.random_normal_initializer()而不是tf.glorot_uniform_initializer()。我做了交换，现在他们表现相同。