examples/attention_models/baseline_model.py
def _glimpse_sensor(self, x_t, l_p):
        """
        Parameters:
            x_t - 28x28 image
            l_p - 2x1 focus vector
        Returns:
            4x12 matrix
        """
        # Turn l_p to the left-top point of rectangle
        l_p = l_p * 14 + 14 - 2
        l_p = T.cast(T.round(l_p), "int32")

        l_p = l_p * (l_p >= 0)
        l_p = l_p * (l_p < 24) + (l_p >= 24) * 23
        l_p2 = l_p - 2
        l_p2 = l_p2 * (l_p2 >= 0)
        l_p2 = l_p2 * (l_p2 < 20) + (l_p2 >= 20) * 19
        l_p3 = l_p - 6
        l_p3 = l_p3 * (l_p3 >= 0)
        l_p3 = l_p3 * (l_p3 < 16) + (l_p3 >= 16) * 15
        glimpse_1 = x_t[l_p[0]: l_p[0] + 4][:, l_p[1]: l_p[1] + 4]
        glimpse_2 = x_t[l_p2[0]: l_p2[0] + 8][:, l_p2[1]: l_p2[1] + 8]
        glimpse_2 = theano.tensor.signal.downsample.max_pool_2d(glimpse_2, (2,2))
        glimpse_3 = x_t[l_p3[0]: l_p3[0] + 16][:, l_p3[1]: l_p3[1] + 16]
        glimpse_3 = theano.tensor.signal.downsample.max_pool_2d(glimpse_3, (4,4))
        return T.concatenate([glimpse_1, glimpse_2, glimpse_3])
def _glimpse_sensor(self, x_t, l_p):
        """
        Parameters:
            x_t - 28x28 image
            l_p - 2x1 focus vector
        Returns:
            4x12 matrix
        """
        # Turn l_p to the left-top point of rectangle
        l_p = l_p * 14 + 14 - 2
        l_p = T.cast(T.round(l_p), "int32")

        l_p = l_p * (l_p >= 0)
        l_p = l_p * (l_p < 24) + (l_p >= 24) * 23
        l_p2 = l_p - 2
        l_p2 = l_p2 * (l_p2 >= 0)
        l_p2 = l_p2 * (l_p2 < 20) + (l_p2 >= 20) * 19
        l_p3 = l_p - 6
        l_p3 = l_p3 * (l_p3 >= 0)
        l_p3 = l_p3 * (l_p3 < 16) + (l_p3 >= 16) * 15
        glimpse_1 = x_t[l_p[0]: l_p[0] + 4][:, l_p[1]: l_p[1] + 4]
        glimpse_2 = x_t[l_p2[0]: l_p2[0] + 8][:, l_p2[1]: l_p2[1] + 8]
        glimpse_2 = theano.tensor.signal.downsample.max_pool_2d(glimpse_2, (2,2))
        glimpse_3 = x_t[l_p3[0]: l_p3[0] + 16][:, l_p3[1]: l_p3[1] + 16]
        glimpse_3 = theano.tensor.signal.downsample.max_pool_2d(glimpse_3, (4,4))
        return T.concatenate([glimpse_1, glimpse_2, glimpse_3])
Parameters:
            x_t - 28x28 image
            l_p - 2x1 focus vector
        Returns:
            4x12 matrix