layers.py 文件源码

def stimuli(self, layer=-1, location=[.5], corrsort=True, activation=1.0,
                static_hidden=True, overlay=None):

        if np.isscalar(location): location = [location]
        coders = self.coders
        if layer < 0: layer += len(coders)
        out_shape = coders[layer].output_shape(reduced=False)
        n_hidden = out_shape[-1]

        values = np.zeros([n_hidden] + list(out_shape[1:]),
                          dtype=self.dtype.as_numpy_dtype)

        mid_indices = [0 for j in range(len(out_shape) - 2)]
        for i in range(n_hidden):
            for loc in location:
                if len(mid_indices):
                    mid_indices[0] = int(out_shape[1] * loc)
                indices = [i] + mid_indices + [i]
                values[tuple(indices)] = activation                   

        self.set_batch_size(n_hidden)
        values = coders[layer].get_reconstructed_input(values, reduced=False, overlay=overlay,
                                                       static_hidden=static_hidden)
        for i in range(layer - 1, -1, -1):
            if coders[i].output_shape() != coders[i+1].input_shape():
                values = tf.reshape(values, coders[i].output_shape())
            values = coders[i].get_reconstructed_input(values, reduced=True, overlay=overlay,
                                                       static_hidden=static_hidden)

        values = values.eval().squeeze()
        if corrsort: return values[features.corrsort(values, use_tsp=True)]
        else: return values