tf.summary可视化计算流graph 可视化.ckpt和.pb中的计算流graph
'''
对graph的计算流的可视化
'''
import tensorflow as tf a=tf.constant(1,name="input_a") b=tf.constant(2,name='input_b') c=tf.multiply(a,b, name="mul_c") sess=tf.Session() summary_writer=tf.summary.FileWriter('./my_graph',sess.graph) tf.summary.scalar('c', c) sum_ops = tf.summary.merge_all()#自动管理 metall,c_ = sess.run([sum_ops,c]) summary_writer.add_summary(metall) # 写入文件 print(c_)
''' 通过.ckpt文件,可视化.meta中的图 ''' from tensorflow.python.platform import gfile import tensorflow as tf model_path = r".\checkpoint\model_yolov3-tiny_ckpt\model.ckpt" saver = tf.train.import_meta_graph(model_path+'.meta',clear_devices=True) graph = tf.get_default_graph() with tf.Session( graph=graph) as sess: sess.run(tf.global_variables_initializer()) saver.restore(sess,model_path) summaryWriter = tf.summary.FileWriter('./log_graph_model_yolov3-tiny_ckpt/', graph)
''' 可视化.pb文件中的图模型 ''' from tensorflow.python.platform import gfile import tensorflow as tf model = r'./data/darknet_weights/frozen_darknet_yolov3spp_model.pb' graph = tf.get_default_graph() graph_def = graph.as_graph_def() graph_def.ParseFromString(gfile.FastGFile(model, 'rb').read()) tf.import_graph_def(graph_def, name='graph') summaryWriter = tf.summary.FileWriter('./log_graph_yolov3spp_pb/', graph)