我们从Python开源项目中，提取了以下2个代码示例，用于说明如何使用tensorflow.arg_min()。
def ctc_label_dense_to_sparse(labels, label_lengths, init_len): """ TODO: the number of non-zeros in every row of 'labels' must less than the corresponding value in 'label_lengths' """ label_shape = labels.get_shape().as_list() len_shape = label_lengths.get_shape().as_list()[0] batch = label_shape[0] assert(batch == len_shape) max_len = tf.reduce_max(init_len) cur_len = tf.constant(2.0) cur_len = tf.tile(tf.expand_dims(cur_len,axis=-1),[batch]) mask = tf.cast(tf.sequence_mask(label_lengths,max_len), tf.int32) #labels_split, buf = tf.split(labels, [max_len,-1], axis=1) #buf = tf.reduce_sum(buf) #tf.summary.scalar('buf', buf) labels = tf.multiply(labels, mask) #min_len = tf.arg_min(label_lengths, dimension=0) #mask = tf.fill(label_shape, 0) where_val = tf.less(tf.constant(0), labels) indices = tf.where(where_val) vals_sparse = tf.gather_nd(labels, indices) return indices, vals_sparse, tf.shape(labels), cur_len, mask
def arg_closest_anchor(bboxes, anchors): """Find the closest anchor. Box Format [ymin, xmin, ymax, xmax] """ num_anchors = anchors.get_shape().as_list()[0] num_bboxes = tf.shape(bboxes)[0] _indices = tf.reshape(tf.range(num_bboxes), shape=[-1, 1]) _indices = tf.reshape(tf.stack([_indices] * num_anchors, axis=1), shape=[-1, 1]) bboxes_m = tf.gather_nd(bboxes, _indices) # bboxes_m = tf.Print(bboxes_m, [bboxes_m], "bboxes_m", summarize=100) anchors_m = tf.tile(anchors, [num_bboxes, 1]) # anchors_m = tf.Print(anchors_m, [anchors_m], "anchors_m", summarize=100) square_dist = tf.squared_difference(bboxes_m[:, 0], anchors_m[:, 0]) + \ tf.squared_difference(bboxes_m[:, 1], anchors_m[:, 1]) + \ tf.squared_difference(bboxes_m[:, 2], anchors_m[:, 2]) + \ tf.squared_difference(bboxes_m[:, 3], anchors_m[:, 3]) square_dist = tf.reshape(square_dist, shape=[num_bboxes, num_anchors]) # square_dist = tf.Print(square_dist, [square_dist], "square_dist", summarize=100) indices = tf.arg_min(square_dist, dimension=1) return indices
