BIDI_ML_INFO_EXTRACTION/BiddingKG/dl/test/5.py

'''
Created on 2019年1月15日

@author: User
'''

import tensorflow as tf
from tensorflow.contrib.crf import crf_log_likelihood

path = "D://Anaconda3.4//envs//dl_nlp//fool//pos.pb"


def loss_layer(project_logits,y_target,trans,max_steps):
    with tf.variable_scope("crf_loss1"):
        log_likelihood, trans = crf_log_likelihood(inputs=project_logits, tag_indices=y_target,
                                                   transition_params=trans, sequence_lengths=max_steps)
    return tf.reduce_mean(-log_likelihood)

def load_graph(path):
    with tf.gfile.GFile(path, mode='rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        for i,n in enumerate(graph_def.node):
            print("Name of the node - %s" % n.name)
    with tf.Graph().as_default() as graph:
        tf.import_graph_def(graph_def, name="prefix")
    return graph
'''
with tf.gfile.GFile(path, mode='rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    for i,n in enumerate(graph_def.node):
        print("Name of the node - %s" % n.name)
with tf.Graph().as_default() as graph:
    tf.import_graph_def(graph_def)
    trans = graph.get_tensor_by_name("prefix/crf_loss/transitions:0")
    logits = graph.get_tensor_by_name("prefix/project/logits:0")
    y_target = tf.placeholder()
    loss = loss_layer(logits, y_target, trans, 100)
    summaryWriter = tf.summary.FileWriter('log/', graph)
    #tf.Graph().get_operations()
    
'''

def buildModel():
    graph = load_graph(path)
    with graph.as_default():
        trans = graph.get_tensor_by_name("prefix/crf_loss/transitions:0")
        lengths = graph.get_tensor_by_name("prefix/lengths:0")
        logits = graph.get_tensor_by_name("prefix/project/logits:0")
        print(logits)
        print(trans)
        y_target = tf.placeholder(dtype=tf.int32, shape=[None, None], name='y_target')
        #loss = loss_layer(logits, y_target, trans, lengths)
        summaryWriter = tf.summary.FileWriter('log/', graph)
    
if __name__=="__main__":
    # import fool
    # a = fool.LEXICAL_ANALYSER
    # a._load_ner_model()
    # _dict = a.ner_model.id_to_tag
    # for _key in _dict.keys():
    #     print(_key,_dict[_key])
    # load_graph(path)
    a = [1,2,3,44]
    print(a[-100:])