96483326d8e1bd7ba7d9368d8e7552ec19b27a74,keras/layers/recurrent.py,GRU,get_constants,#GRU#Any#Any#,419

Before Change

    def get_constants(self, X, train=False):
        nb_samples = K.shape(X)[0]
        if K._BACKEND == "tensorflow":
            nb_samples = int(nb_samples)
        retain_p_W = 1. - self.p_W
        retain_p_U = 1. - self.p_U
        if train and self.p_W > 0 and self.p_U > 0:
            B_W = K.random_binomial((3, nb_samples, self.input_dim), p=retain_p_W)

After Change

    def get_constants(self, X, train=False):
        nb_samples = K.shape(X)[0]
        if K._BACKEND == "tensorflow" and train and self.p_W > 0 and self.p_U > 0:
            if not self.input_shape[0]:
                raise Exception("For RNN dropout in tensorflow, a complete " +
                                "input_shape must be provided (including batch size).")
            nb_samples = self.input_shape[0]
        retain_p_W = 1. - self.p_W
        retain_p_U = 1. - self.p_U
        if train and self.p_W > 0 and self.p_U > 0:
            B_W = K.random_binomial((3, nb_samples, self.input_dim), p=retain_p_W)

Instances