c1a72b36444c8027c27c2dc02dc03d5b69a5e389,keras/layers/recurrent_convolutional.py,LSTMConv2D,get_constants,#LSTMConv2D#Any#,482

Before Change

    def get_constants(self, x):
        constants = []
        if 0 < self.dropout_U < 1:
            ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
            ones = K.concatenate([ones] * self.output_dim, 1)
            B_U = [K.in_train_phase(K.dropout(ones, self.dropout_U), ones)
                   for _ in range(4)]
            constants.append(B_U)
        else:
            constants.append([K.cast_to_floatx(1.) for _ in range(4)])

        if 0 < self.dropout_W < 1:
            input_shape = self.input_spec[0].shape
            input_dim = input_shape[-1]
            ones = K.ones_like(K.reshape(x[:, 0, 0], (-1, 1)))
            ones = K.concatenate([ones] * input_dim, 1)
            B_W = [K.in_train_phase(K.dropout(ones, self.dropout_W), ones)
                   for _ in range(4)]

After Change

            constants.append([K.cast_to_floatx(1.) for _ in range(4)])

        if 0 < self.dropout_W < 1:
            ones = K.zeros_like(x)
            ones = K.sum(ones, axis=1)
            ones = ones + 1
            B_W = [K.in_train_phase(K.dropout(ones, self.dropout_W), ones)
                   for _ in range(4)]

Instances