ab75f215b6d65704e7272e0a411d9d082c029846,keras/layers/convolutional.py,Convolution2D,get_output,#Convolution2D#Any#,177

Before Change

        if border_mode == "same":
            border_mode = "full"

        conv_out = T.nnet.conv.conv2d(X, self.W,
                                      border_mode=border_mode,
                                      subsample=self.subsample)
        if self.border_mode == "same":
            shift_x = (self.nb_row - 1) // 2
            shift_y = (self.nb_col - 1) // 2
            conv_out = conv_out[:, :, shift_x:X.shape[2] + shift_x, shift_y:X.shape[3] + shift_y]

After Change

    def get_output(self, train):
        X = self.get_input(train)
        border_mode = self.border_mode
        if border_mode == "same":
            assert(self.subsample == (1, 1))
            pad_x = (self.nb_row - self.subsample[0]) // 2
            pad_y = (self.nb_col - self.subsample[1]) // 2
            conv_out = dnn.dnn_conv(img=X,
                                    kerns=self.W,
                                    border_mode=(pad_x, pad_y))
        else:
            conv_out = dnn.dnn_conv(img=X,
                                    kerns=self.W,
                                    border_mode=border_mode,
                                    subsample=self.subsample)

        return self.activation(conv_out + self.b.dimshuffle("x", 0, "x", "x"))

    def get_config(self):
        return {"name": self.__class__.__name__,

Instances