Python utils.get_optimizer() Examples

The following are 3 code examples of utils.get_optimizer(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module utils , or try the search function .
Example #1
Source File: models.py    From Recommender-Systems-Samples with MIT License 5 votes vote down vote up
def __init__(self, input_dim=None, output_dim=1, factor_order=10, init_path=None, opt_algo='gd', learning_rate=1e-2,
                 l2_w=0, l2_v=0, random_seed=None):
        Model.__init__(self)
        init_vars = [('w', [input_dim, output_dim], 'xavier', dtype),
                     ('v', [input_dim, factor_order], 'xavier', dtype),
                     ('b', [output_dim], 'zero', dtype)]
        self.graph = tf.Graph()
        with self.graph.as_default():
            if random_seed is not None: 
                tf.set_random_seed(random_seed)
            self.X = tf.sparse_placeholder(dtype)
            self.y = tf.placeholder(dtype)
            self.vars = utils.init_var_map(init_vars, init_path)

            X_square = tf.SparseTensor(self.X.indices, tf.square(self.X.values), tf.to_int64(tf.shape(self.X)))
            xv = tf.square(tf.sparse_tensor_dense_matmul(self.X, self.vars['v']))
            p = 0.5 * tf.reshape(
                tf.reduce_sum(xv - tf.sparse_tensor_dense_matmul(X_square, tf.square(self.vars['v'])), 1),
                [-1, output_dim])
            xw = tf.sparse_tensor_dense_matmul(self.X, self.vars['w'])
            logits = tf.reshape(xw + self.vars['b'] + p, [-1])
            self.y_prob = tf.sigmoid(logits)

            self.loss = tf.reduce_mean(
                tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=self.y)) + \
                        l2_w * tf.nn.l2_loss(xw) + \
                        l2_v * tf.nn.l2_loss(xv)
            self.optimizer = utils.get_optimizer(opt_algo, learning_rate, self.loss)

            config = tf.ConfigProto()
            config.gpu_options.allow_growth = True
            self.sess = tf.Session(config=config)
            tf.global_variables_initializer().run(session=self.sess) 
Example #2
Source File: trainer.py    From ChaLearn_liveness_challenge with MIT License 4 votes vote down vote up
def __init__(self, opt):
        self.opt = opt
        self.device = torch.device("cuda" if opt.ngpu else "cpu")
        
        self.model, self.classifier = models.get_model(opt.net_type, 
                                                       opt.classifier_type, 
                                                       opt.pretrained,
                                                       int(opt.nclasses))
        self.model = self.model.to(self.device)
        self.classifier = self.classifier.to(self.device)

        if opt.ngpu>1:
            self.model = nn.DataParallel(self.model)
            
        self.loss = models.init_loss(opt.loss_type)
        self.loss = self.loss.to(self.device)

        self.optimizer = utils.get_optimizer(self.model, self.opt)
        self.lr_scheduler = utils.get_lr_scheduler(self.opt, self.optimizer)
        self.alpha_scheduler = utils.get_margin_alpha_scheduler(self.opt)

        self.train_loader = datasets.generate_loader(opt,'train') 
        self.test_loader = datasets.generate_loader(opt,'val')    
        
        self.epoch = 0
        self.best_epoch = False
        self.training = False
        self.state = {}
        

        self.train_loss = utils.AverageMeter()
        self.test_loss  = utils.AverageMeter()
        self.batch_time = utils.AverageMeter()
        self.test_metrics = utils.ROCMeter()
        self.best_test_loss = utils.AverageMeter()                    
        self.best_test_loss.update(np.array([np.inf]))

        self.visdom_log_file = os.path.join(self.opt.out_path, 'log_files', 'visdom.log')
        self.vis = Visdom(port = opt.visdom_port,
                          log_to_filename=self.visdom_log_file,
                          env=opt.exp_name + '_' + str(opt.fold))

        self.vis_loss_opts = {'xlabel': 'epoch', 
                              'ylabel': 'loss', 
                              'title':'losses', 
                              'legend': ['train_loss', 'val_loss']}

        self.vis_tpr_opts = {'xlabel': 'epoch', 
                              'ylabel': 'tpr', 
                              'title':'val_tpr', 
                              'legend': ['tpr@fpr10-2', 'tpr@fpr10-3', 'tpr@fpr10-4']}

        self.vis_epochloss_opts = {'xlabel': 'epoch', 
                              'ylabel': 'loss', 
                              'title':'epoch_losses', 
                              'legend': ['train_loss', 'val_loss']} 
Example #3
Source File: trainer.py    From ChaLearn_liveness_challenge with MIT License 4 votes vote down vote up
def __init__(self, opt):
        self.opt = opt
        self.device = torch.device("cuda" if opt.ngpu else "cpu")
        
        self.model, self.classifier = models.get_model(opt.net_type, 
                                                       opt.loss_type, 
                                                       opt.pretrained,
                                                       int(opt.nclasses))
        self.model = self.model.to(self.device)
        self.classifier = self.classifier.to(self.device)

        if opt.ngpu>1:
            self.model = nn.DataParallel(self.model)
            
        self.loss = models.init_loss(opt.loss_type)
        self.loss = self.loss.to(self.device)

        self.optimizer = utils.get_optimizer(self.model, self.opt)
        self.lr_scheduler = utils.get_lr_scheduler(self.opt, self.optimizer)

        self.train_loader = datasets.generate_loader(opt,'train') 
        self.test_loader = datasets.generate_loader(opt,'val')    
        
        self.epoch = 0
        self.best_epoch = False
        self.training = False
        self.state = {}
        

        self.train_loss = utils.AverageMeter()
        self.test_loss  = utils.AverageMeter()
        self.batch_time = utils.AverageMeter()
        if self.opt.loss_type in ['cce', 'bce', 'mse', 'arc_margin']:
            self.test_metrics = utils.AverageMeter()
        else:
            self.test_metrics = utils.ROCMeter()

        self.best_test_loss = utils.AverageMeter()                    
        self.best_test_loss.update(np.array([np.inf]))

        self.visdom_log_file = os.path.join(self.opt.out_path, 'log_files', 'visdom.log')
        self.vis = Visdom(port = opt.visdom_port,
                          log_to_filename=self.visdom_log_file,
                          env=opt.exp_name + '_' + str(opt.fold))

        self.vis_loss_opts = {'xlabel': 'epoch', 
                              'ylabel': 'loss', 
                              'title':'losses', 
                              'legend': ['train_loss', 'val_loss']}

        self.vis_epochloss_opts = {'xlabel': 'epoch', 
                              'ylabel': 'loss', 
                              'title':'epoch_losses', 
                              'legend': ['train_loss', 'val_loss']}