import tensorflow as tf
import logging
import numpy as np
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')
output_path = './checkpoint'
ckpt = tf.train.get_checkpoint_state(output_path)
saver = tf.train.import_meta_graph(ckpt.model_checkpoint_path+'.meta')
var = [v for v in tf.trainable_variables()]
#var = [v for v in tf.get_default_graph().as_graph_def().node]
for i in range(len(var)):
logging.info(var[i].name)
not_restore = ['Variable_44:0',"Variable_45:0"]
restore_var = [v for v in tf.trainable_variables() if v.name not in not_restore]
saver = tf.train.Saver(restore_var)
ckpt = tf.train.get_checkpoint_state('./checkpoint')
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
saver.restore(sess, ckpt.model_checkpoint_path)
output_path = './checkpoint'
ckpt = tf.train.get_checkpoint_state(output_path)
saver = tf.train.import_meta_graph(ckpt.model_checkpoint_path+'.meta')
g = tf.get_default_graph()
input_tensor = g.get_tensor_by_name("input:0")
output_tensor = g.get_tensor_by_name("output:0")
with tf.Session() as sess:
saver.restore(sess, ckpt.model_checkpoint_path)
output = sess.run(output_tensor, feed_dict={input_tensor: x_test})
