tf_cnnvis.py 文件源码

def _deconvolution(graph, sess, op_tensor, X, feed_dict):
    out = []
    with graph.as_default() as g:
        # get shape of tensor
        tensor_shape = op_tensor.get_shape().as_list()

        with sess.as_default() as sess:
            # creating placeholders to pass featuremaps and
            # creating gradient ops
            featuremap = [tf.placeholder(tf.int32) for i in range(config["N"])]
            reconstruct = [tf.gradients(tf.transpose(tf.transpose(op_tensor)[featuremap[i]]), X)[0] for i in range(config["N"])]

            # Execute the gradient operations in batches of 'n'
            for i in range(0, tensor_shape[-1], config["N"]):
                c = 0
                for j in range(config["N"]):
                    if (i + j) < tensor_shape[-1]:
                        feed_dict[featuremap[j]] = i + j
                        c += 1
                if c > 0:
                    out.extend(sess.run(reconstruct[:c], feed_dict = feed_dict))
    return out