Python utils.utils方法代码示例

# 需要导入模块: import utils [as 别名]
# 或者: from utils import utils [as 别名]
def trainer(network,number_of_images):
    #find error like squared error but better
    cross_entropy=tf.nn.softmax_cross_entropy_with_logits_v2(logits=network,labels=labels_ph)

    #now minize the above error
    #calculate the total mean of all the errors from all the nodes
    cost=tf.reduce_mean(cross_entropy)
    tf.summary.scalar("cost", cost)#for tensorboard visualisation

    #Now backpropagate to minimise the cost in the network.
    optimizer=tf.train.AdamOptimizer().minimize(cost)
    #print(optimizer)
    session.run(tf.global_variables_initializer())
    writer = tf.summary.FileWriter(model_save_name, graph=tf.get_default_graph())
    merged = tf.summary.merge_all()
    saver = tf.train.Saver(max_to_keep=4)
    counter=0
    for epoch in range(epochs):
        tools = utils()
        for batch in range(int(number_of_images / batch_size)):
            counter+=1
            images, labels = tools.batch_dispatch()
            if images == None:
                break
            loss,summary = session.run([cost,merged], feed_dict={images_ph: images, labels_ph: labels})
            print('loss', loss)
            session.run(optimizer, feed_dict={images_ph: images, labels_ph: labels})

            print('Epoch number ', epoch, 'batch', batch, 'complete')
            writer.add_summary(summary,counter)
        saver.save(session, os.path.join(model_save_name)) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import utils [as 别名]
def trainer(network,number_of_images):
    #find error like squared error but better
    cross_entropy=tf.nn.softmax_cross_entropy_with_logits_v2(logits=network,labels=labels_ph)

    #now minize the above error
    #calculate the total mean of all the errors from all the nodes
    cost=tf.reduce_mean(cross_entropy)
    tf.summary.scalar("cost", cost)#for tensorboard visualisation

    #Now backpropagate to minimise the cost in the network.
    optimizer=tf.train.AdamOptimizer().minimize(cost)
    #print(optimizer)
    session.run(tf.global_variables_initializer())
    writer = tf.summary.FileWriter(model_save_name, graph=tf.get_default_graph())
    merged = tf.summary.merge_all()
    saver = tf.train.Saver(max_to_keep=4)
    counter=0
    for epoch in range(epochs):
        tools = utils()
        for batch in range(int(number_of_images / batch_size)):
            counter+=1
            images, labels = tools.batch_dispatch()
            if images == None:
                break
            loss,summary = session.run([cost,merged], feed_dict={images_ph: images, labels_ph: labels})
            print('loss', loss)
            session.run(optimizer, feed_dict={images_ph: images, labels_ph: labels})

            print('Epoch number ', epoch, 'batch', batch, 'complete')
            writer.add_summary(summary,counter)
        saver.save(session, model_save_name) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import utils [as 别名]
def main(_):
   """Begins the execution of the program

   Args:
      _ : Tensorflow flags app instance
   """
   if FLAGS.create != "":
      dataset = utils.Dataset(FLAGS)
      dataset.create_records(FLAGS.create)
      exit()

   if not FLAGS.test:
      priliminary_checks(FLAGS)
      idx = get_runid(FLAGS)
      create_rundirs(FLAGS, idx)
      dump_model_params(FLAGS)
      log_config(idx, FLAGS.__flags)

   if FLAGS.archi:
      net = nnet.Model(FLAGS, is_training=False)
      net.test_graph()
      exit()


   FLAGS.h = 600 if FLAGS.dataset == 'maps' else 256
   FLAGS.w = FLAGS.h

   if FLAGS.train or FLAGS.resume:
      net = nnet.Model(FLAGS, is_training=True)
      net.train()
      print ' - Done training the network...'
   else:
      print ' - Testing the model...'
      net = nnet.Model(FLAGS, is_training=False)
      net.test(FLAGS.test_source) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import utils [as 别名]
def main():
    util                    = utils.utils()
    #loading MITRE ATT&CK enterpise matrix from file.

    mitre_attack_matrix     = json.loads(util.read_attack_matrix())['objects']
    
    parse_mitre_attack_tactics(mitre_attack_matrix)
    parse_mitre_attack_techniques(mitre_attack_matrix) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import utils [as 别名]
def read_triviaqa_data(qajson):
    data = utils.utils.read_json(qajson)
    # read only documents and questions that are a part of clean data set
    if data['VerifiedEval']:
        clean_data = []
        for datum in data['Data']:
            if datum['QuestionPartOfVerifiedEval']:
                if data['Domain'] == 'Web':
                    datum = read_clean_part(datum)
                clean_data.append(datum)
        data['Data'] = clean_data
    return data