45a10bc6d708fade197a37bfbc62312caf70e6a7,keras/layers/convolutional_recurrent.py,ConvLSTM2D,get_constants,#ConvLSTM2D#Any#Any#,438

Before Change

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

        if 0 < self.recurrent_dropout < 1:
            ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1)))
            ones = K.tile(ones, (1, self.filters))

            def dropped_inputs():
                return K.dropout(ones, self.recurrent_dropout)
            rec_dp_mask = [K.in_train_phase(dropped_inputs,

After Change

            shape = list(self.kernel_shape)
            shape[-1] = self.filters
            ones = K.zeros_like(inputs)
            ones = K.sum(ones, axis=1)
            ones = self.input_conv(ones, K.zeros(shape),
                                   padding=self.padding)
            ones += 1.

            def dropped_inputs():
                return K.dropout(ones, self.recurrent_dropout)
            rec_dp_mask = [K.in_train_phase(dropped_inputs,

Instances