cross-entropy loss function --> total loss function """ # Initialize the loss and gradient to zero. def cross_entropy_loss (pred, labels): """ Does an internal softmax before loss calculation. The Kullback-Leibler Divergence, … Cross Entropy Loss. log_softmax ( outputs , dim = 1 ) # compute the log of softmax values outputs = outputs [ range ( batch_size ), labels ] # pick the values corresponding to the labels return - torch . You need to make sure to have two neurons in the final layer of the model. Another note, the input for the loss criterion here needs to be a long tensor with dimension of n, instead of n by 1 which we had used previously for linear regression. To avoid that, we need to add a ‘minus’ sign when we take log because the minimum loss is 0 and cannot be negative. Hence, it leads us to the cross-entropy loss function for softmax function. where f y i f y i is the probability for correct class score and f j f j is the j j -th element of the score vector for each image. By far the most common form of loss for binary classification is binary cross entropy (BCE). Implementation in Popular Deep Learning Frameworks. Training deep learning models has never been easier. The key being sum=True is the default summed loss (current behavior), and sum=False being unreduced / unsummed per-sample loss. Our goal is to classify whether the image above contains a … Softmax gives values between 0 and 1, which means log softmax will give values between -infinity and 0. Converting a model with multiple outputs from PyTorch to TensorFlow can be a bit more challenging than doing the same process for a simple model with a single output, but can still be done. softmax_cross_entropy (x, t, normalize = True, cache_score = True, class_weight = None, ignore_label =-1, reduce = 'mean', enable_double_backprop = False, soft_target_loss = 'cross-entropy') [source] ¶ Computes cross entropy loss for pre-softmax activations. torch.nn.KLDivLoss. x (Variable or N-dimensional array) – Variable … log return loss. Please post such questions to the forum discuss.pytorch.org, rather than as a github issue. In PyTorch, nn.CrossEntropyLoss() is the same as F.nll_loss(F.log_softmax(...)).Therefore, in the implementation above, nll equates to H(q,p) from eq-3.And then, the loss/c equates to H(u,p) from eq-3 as well where, c equals total number of classes.. For reference again, we know that eq-3 was:. pred = F.log_softmax(x, dim=-1) loss = F.nll_loss(pred, target) loss. My question is about how is log softmax implemented in practice with the cross-entropy loss. Parameters. Categorical Cross Entropy Loss Function. q -> [0.033, 0.087, 0.236, 0.644] ケース1： p <- [0,0,0,1] 真の結果がクラス4の場合. Archbishop Mitty Alumni, Fashion Show Stage Layout, Introduction For Hotel Management Student, Numerical Methods Engineering, Diksha Certificate Issue, Cabin Rentals In Miami Beach Florida, Cerebus Syndrome Definition, Restaurants In Brazil Rio De Janeiro, Film Rewind Button Function, Frequency Distribution In Statistics Ppt, " /> cross-entropy loss function --> total loss function """ # Initialize the loss and gradient to zero. def cross_entropy_loss (pred, labels): """ Does an internal softmax before loss calculation. The Kullback-Leibler Divergence, … Cross Entropy Loss. log_softmax ( outputs , dim = 1 ) # compute the log of softmax values outputs = outputs [ range ( batch_size ), labels ] # pick the values corresponding to the labels return - torch . You need to make sure to have two neurons in the final layer of the model. Another note, the input for the loss criterion here needs to be a long tensor with dimension of n, instead of n by 1 which we had used previously for linear regression. To avoid that, we need to add a ‘minus’ sign when we take log because the minimum loss is 0 and cannot be negative. Hence, it leads us to the cross-entropy loss function for softmax function. where f y i f y i is the probability for correct class score and f j f j is the j j -th element of the score vector for each image. By far the most common form of loss for binary classification is binary cross entropy (BCE). Implementation in Popular Deep Learning Frameworks. Training deep learning models has never been easier. The key being sum=True is the default summed loss (current behavior), and sum=False being unreduced / unsummed per-sample loss. Our goal is to classify whether the image above contains a … Softmax gives values between 0 and 1, which means log softmax will give values between -infinity and 0. Converting a model with multiple outputs from PyTorch to TensorFlow can be a bit more challenging than doing the same process for a simple model with a single output, but can still be done. softmax_cross_entropy (x, t, normalize = True, cache_score = True, class_weight = None, ignore_label =-1, reduce = 'mean', enable_double_backprop = False, soft_target_loss = 'cross-entropy') [source] ¶ Computes cross entropy loss for pre-softmax activations. torch.nn.KLDivLoss. x (Variable or N-dimensional array) – Variable … log return loss. Please post such questions to the forum discuss.pytorch.org, rather than as a github issue. In PyTorch, nn.CrossEntropyLoss() is the same as F.nll_loss(F.log_softmax(...)).Therefore, in the implementation above, nll equates to H(q,p) from eq-3.And then, the loss/c equates to H(u,p) from eq-3 as well where, c equals total number of classes.. For reference again, we know that eq-3 was:. pred = F.log_softmax(x, dim=-1) loss = F.nll_loss(pred, target) loss. My question is about how is log softmax implemented in practice with the cross-entropy loss. Parameters. Categorical Cross Entropy Loss Function. q -> [0.033, 0.087, 0.236, 0.644] ケース1： p <- [0,0,0,1] 真の結果がクラス4の場合. Archbishop Mitty Alumni, Fashion Show Stage Layout, Introduction For Hotel Management Student, Numerical Methods Engineering, Diksha Certificate Issue, Cabin Rentals In Miami Beach Florida, Cerebus Syndrome Definition, Restaurants In Brazil Rio De Janeiro, Film Rewind Button Function, Frequency Distribution In Statistics Ppt, " /> cross-entropy loss function --> total loss function """ # Initialize the loss and gradient to zero. def cross_entropy_loss (pred, labels): """ Does an internal softmax before loss calculation. The Kullback-Leibler Divergence, … Cross Entropy Loss. log_softmax ( outputs , dim = 1 ) # compute the log of softmax values outputs = outputs [ range ( batch_size ), labels ] # pick the values corresponding to the labels return - torch . You need to make sure to have two neurons in the final layer of the model. Another note, the input for the loss criterion here needs to be a long tensor with dimension of n, instead of n by 1 which we had used previously for linear regression. To avoid that, we need to add a ‘minus’ sign when we take log because the minimum loss is 0 and cannot be negative. Hence, it leads us to the cross-entropy loss function for softmax function. where f y i f y i is the probability for correct class score and f j f j is the j j -th element of the score vector for each image. By far the most common form of loss for binary classification is binary cross entropy (BCE). Implementation in Popular Deep Learning Frameworks. Training deep learning models has never been easier. The key being sum=True is the default summed loss (current behavior), and sum=False being unreduced / unsummed per-sample loss. Our goal is to classify whether the image above contains a … Softmax gives values between 0 and 1, which means log softmax will give values between -infinity and 0. Converting a model with multiple outputs from PyTorch to TensorFlow can be a bit more challenging than doing the same process for a simple model with a single output, but can still be done. softmax_cross_entropy (x, t, normalize = True, cache_score = True, class_weight = None, ignore_label =-1, reduce = 'mean', enable_double_backprop = False, soft_target_loss = 'cross-entropy') [source] ¶ Computes cross entropy loss for pre-softmax activations. torch.nn.KLDivLoss. x (Variable or N-dimensional array) – Variable … log return loss. Please post such questions to the forum discuss.pytorch.org, rather than as a github issue. In PyTorch, nn.CrossEntropyLoss() is the same as F.nll_loss(F.log_softmax(...)).Therefore, in the implementation above, nll equates to H(q,p) from eq-3.And then, the loss/c equates to H(u,p) from eq-3 as well where, c equals total number of classes.. For reference again, we know that eq-3 was:. pred = F.log_softmax(x, dim=-1) loss = F.nll_loss(pred, target) loss. My question is about how is log softmax implemented in practice with the cross-entropy loss. Parameters. Categorical Cross Entropy Loss Function. q -> [0.033, 0.087, 0.236, 0.644] ケース1： p <- [0,0,0,1] 真の結果がクラス4の場合. Archbishop Mitty Alumni, Fashion Show Stage Layout, Introduction For Hotel Management Student, Numerical Methods Engineering, Diksha Certificate Issue, Cabin Rentals In Miami Beach Florida, Cerebus Syndrome Definition, Restaurants In Brazil Rio De Janeiro, Film Rewind Button Function, Frequency Distribution In Statistics Ppt, " />
To Top
Kezdőlap
Close
Bemutatkozás
Szakterület
Partnerek
Kapcsolat
13
jún 2021
No Comments
Egyéb
Permalink

pytorch cross entropy loss softmax

Do not call this op with the output of softmax… chainer.functions.softmax_cross_entropy¶ chainer.functions. New Tutorial series about Deep Learning with PyTorch!⭐ Check out Tabnine, the FREE AI … 3 practice exercises. Warning: This op expects unscaled logits, since it performs a softmax on logits internally for efficiency. CrossEntropyLoss in PyTorch. size ()[ 0 ] # batch_size outputs = F . logits = torch.rand([3,10]) I remember my first impression when I saw the formula for the cross-entropy loss. PyTorch cross entropy loss and the gradient SoftMax. Given an input (and some distinct classes), determine/predict the class to which the input belongs: this is a classification problem. F.binary_cross_entropy_with_logits. Overview¶. 3 videos. I was wondering is there an equivalent PyTorch loss function for TensorFlow's softmax_cross_entropy_with_logits? is there an equivalent PyTorch loss function for TensorFlow's softmax_cross_entropy_with_logits? This takes logits as inputs (performing log_softmax internally). So, to summarise, we started with the Cross Entropy loss and proved that minimising the Cross Entropy is equivalent to minimising the KL Divergence. Defining the Softmax Operation¶. @zou3519 will be implementing this initially for a couple of losses ( NLLLoss and MSELoss, and of course CrossEntropyLoss because it's LogSoftMax + NLLLoss ), and it will be tracked in #264. In pytorch, the cross entropy loss of softmax and the calculation of input gradient can be easily verified About softmax_ cross_ You can refer to here for the derivation process of entropy Examples： # -*- coding: utf-8 -*- import torch import torch.autograd as autograd from torch.autograd import Variable import torch.nn.functional as F import torch.nn as […] We can write our own Cross Entropy Loss function as below (note the NumPy-esque syntax): def myCrossEntropyLoss ( outputs , labels ): batch_size = outputs . 5.3 Logistic Regression Cross Entropy Loss 10m. for the K-dimensional case (described later). A solution. Derivative of Cross Entropy Loss with Softmax. The target is not a probability vector. Softmax and cross entropy are popular functions used in neural nets, especially in multiclass classification problems. It is used for multi-class classification. softmax (x) i = e x i ∑ 0 ≤ j ≤ n − 1 e x j. F.binary_cross_entropy_with_logits(x, y) Out: tensor(0.7739) For more details on the implementation of the functions above, see here for a side by side translation of all of Pytorch’s built-in loss functions to Python and Numpy. Defining the Softmax Operation¶. Texar-PyTorch is an open-source toolkit based on PyTorch, aiming to support a broad set of machine learning, especially text generation tasks, such as machine translation, dialog, summarization, content manipulation, language modeling, and so on. Posted on February 18, 2021 by February 18, 2021 by CrossEntropyLoss (x, y) := H (one_hot (y), softmax (x)) Note that one_hot is a function that takes an index y, and expands it into a one-hot vector. This is … Pytorch Cross Entropy Loss implementation counterintuitive. If you are designing a neural network multi-class classifier using PyTorch, you can use cross entropy loss (tenor.nn.CrossEntropyLoss) with logits output in the forward() method, or you can use negative log-likelihood loss (tensor.nn.NLLLoss) with log-softmax (tensor.LogSoftmax()) in the forward() method. Menu. Fastai/PyTorch Implementation of Label Smoothing Cross Entropy loss - label_smoothing_CE. 6.1 Softmax Function:Using Lines to Classify Data 5m. Optimizer and Loss Optimizer Adam, SGD etc. ys = torch.te... You can also check out this blog post from 2016 by Rob DiPietro titled “A Friendly Introduction to Cross-Entropy Loss” where he uses fun and easy-to-grasp examples and analogies to explain cross-entropy with more detail and with very little complex mathematics. It uses the probability distribution of the output class in the softmax operation. I was trying out the following network architecture to train a multi-class classifier. We first formally show that the softmax cross-entropy (SCE) loss and its variants convey inappropriate supervisory signals, which encourage the learned feature points to spread over the space sparsely in training. Cross-Entropy. softmax_cross_entropy (x, t, normalize = True, cache_score = True, class_weight = None, ignore_label =-1, reduce = 'mean', enable_double_backprop = False, soft_target_loss = 'cross-entropy') [source] ¶ Computes cross entropy loss for pre-softmax activations. Cross-entropy loss function for the softmax function To derive the loss function for the softmax function we start out from the likelihood function that a given set of parameters $\theta$ of the model can result in prediction of the correct class of each input sample, as in the derivation for the logistic loss … Reminder: \begin{equation} \begin{split} H(p, q) & = \sum_{k=0}^K p_k(x) \cdot \log \frac{1}{q_k(x)} \\ & = -\sum_{i=0}^c p_k(x) \cdot \log q_k(x) \\ \end{split} \end{equation} with !A paper also tries to analysis it:link. Task 2: Implement the computation of the cross-entropy loss. To interpret the cross-entropy loss for a specific image, it is the negative log of the probability for the correct class that are computed in the softmax function. Specifically. Pytorch's single binary_cross_entropy_with_logits function. Welcome to Texar-PyTorch’s documentation! [2.718, 7.389, 20.085, 54.598] そして合計を計算してから各値割合を計算. In PyTorch, when the loss criteria is specified as cross entropy loss, PyTorch will automatically perform Softmax classification based upon its inbuilt functionality. To demonstrate cross-entropy loss in action, consider the following figure: Figure 1: To compute our cross-entropy loss, let’s start with the output of our scoring function (the first column). Make sure that you do not add a softmax function. Softmax Rergresstion . 576 PyTorch Tutorial Beibin Li. chainer.functions.softmax_cross_entropy¶ chainer.functions. It is intended to use with binary classification where the target value is The paper uses 10. la: This is lambda in the above equation. 6.1 Softmax 7m. Don't use any build-in function of PyTorch for the cross-entropy. Softmax関数を取るには、まず指数を取る. mean def binary_xloss (logits, labels, ignore = None): """ Binary Cross entropy loss: logits: [B, H, W] Variable, logits at each pixel (between -\infty and +\infty) labels: [B, H, W] Tensor, binary ground truth masks (0 or 1) ignore: void class id """ This makes the forward pass stochastic, and your model – no longer deterministic. This takes lo... Cross-entropy loss together with softmax is arguably one of the most common used supervision components in convolutional neural networks (CNNs). import torch Which Object? def nll(self, … Project: naru Author: naru-project File: made.py License: Apache License 2.0. It is a Softmax activation plus a Cross-Entropy loss. You can do this in two steps using the softmax() and ln() functions, but it’s more efficient to use the built-in PyTorch LogSoftmax() function and do it in one step. exp ()). sum (-target * F. log_softmax (logits, -1), -1) mean_loss = loss. Pytorch also has some other functions for calculating loss, we saw this formula for calculating the Cross entropy. Model In PyTorch, a model is represented by a regular Python class that inherits from the ... Softmax Cross Entropy Embedding Layer Linear Layer Prediction Training Evaluation. Note: softmax can be considered in the sigmoid function family. Pytorch里的CrossEntropyLoss详解 - marsggbo - 博客园. I used Softmax at the output layer and cross entropy as the loss function. Loving Squash in Middlesex. from pytorch_metric_learning import losses, reducers reducer = reducers. In the PyTorch implementation looks like this: loss = F.cross_entropy (x, target) Which is equivalent to : lp = F.log_softmax (x, dim=-1) loss = F.nll_loss (lp, target) It is not F.binary_cross_entropy_with_logits because this function assumes multi label classification: Before implementing the softmax regression model, let us briefly review how the sum operator works along specific dimensions in a tensor, as discussed in Section 2.3.6 and Section 2.3.6.1.Given a matrix X we can sum over all elements (by default) or only over elements in the same axis, i.e., the same column (axis 0) or the same row (axis 1). This means that we cannot use one-hot encoding (one 1 and rest 0's) for our target labels anymore (correct me if I am wrong). Fastai/PyTorch Implementation of Label Smoothing Cross Entropy loss - label_smoothing_CE. This means that we cannot use one-hot encoding (one 1 and rest 0's) for our target labels anymore (correct me if I am wrong). Warning: This op expects unscaled logits, since it performs a softmax on logits internally for efficiency. This article will cover the relationships between the negative log likelihood, entropy, softmax vs. sigmoid cross-entropy loss, maximum likelihood estimation, Kullback-Leibler (KL) divergence, logistic regression, and neural networks. PyTorch Tutorial 11 - Softmax and Cross Entropy - YouTube. It is useful when training a classification problem with C classes. 앞에서 배운바와 같이 Cross-Entropy Loss를 적용하기 위해서는 Softmax를 우선 해줘야 하나 생각할 수 있는데, PyTorch에서는 softmax와 cross-entropy를 합쳐놓은 것 을 제공하기 때문에 맨 마지막 layer가 softmax일 필요가 없습니다. @Blade Here's the solution I came up with! Cross entropy loss operates on logits after softmax. After simplifying, we get the following for gradient and Hessian of cross-entropy loss: For implementation, you have to come down from the the tower of Evolved Notation© and fill in the details. x (Variable or N-dimensional array) – Variable … is there an equivalent PyTorch loss function for TensorFlow's softmax_cross_entropy_with_logits? Also called Softmax Loss. Then you compute the negative sum of the log_softmax values that correspond to the targets. If we use this loss, we will train a CNN to output a probability over the \(C\) classes for each image. ; If you want to get into the heavy mathematical aspects of cross-entropy, you can go to this 2016 post by Peter Roelants … The above but in pytorch. I am using a neural network to predict the quality of the Red Wine dataset, available on UCI machine Learning, using Pytorch, and Cross Entropy Loss as loss function. My question is about how is log softmax implemented in practice with the cross-entropy loss. def softmax_loss_vectorized ( W , X , y , reg ): """ Softmax loss function --> cross-entropy loss function --> total loss function """ # Initialize the loss and gradient to zero. def cross_entropy_loss (pred, labels): """ Does an internal softmax before loss calculation. The Kullback-Leibler Divergence, … Cross Entropy Loss. log_softmax ( outputs , dim = 1 ) # compute the log of softmax values outputs = outputs [ range ( batch_size ), labels ] # pick the values corresponding to the labels return - torch . You need to make sure to have two neurons in the final layer of the model. Another note, the input for the loss criterion here needs to be a long tensor with dimension of n, instead of n by 1 which we had used previously for linear regression. To avoid that, we need to add a ‘minus’ sign when we take log because the minimum loss is 0 and cannot be negative. Hence, it leads us to the cross-entropy loss function for softmax function. where f y i f y i is the probability for correct class score and f j f j is the j j -th element of the score vector for each image. By far the most common form of loss for binary classification is binary cross entropy (BCE). Implementation in Popular Deep Learning Frameworks. Training deep learning models has never been easier. The key being sum=True is the default summed loss (current behavior), and sum=False being unreduced / unsummed per-sample loss. Our goal is to classify whether the image above contains a … Softmax gives values between 0 and 1, which means log softmax will give values between -infinity and 0. Converting a model with multiple outputs from PyTorch to TensorFlow can be a bit more challenging than doing the same process for a simple model with a single output, but can still be done. softmax_cross_entropy (x, t, normalize = True, cache_score = True, class_weight = None, ignore_label =-1, reduce = 'mean', enable_double_backprop = False, soft_target_loss = 'cross-entropy') [source] ¶ Computes cross entropy loss for pre-softmax activations. torch.nn.KLDivLoss. x (Variable or N-dimensional array) – Variable … log return loss. Please post such questions to the forum discuss.pytorch.org, rather than as a github issue. In PyTorch, nn.CrossEntropyLoss() is the same as F.nll_loss(F.log_softmax(...)).Therefore, in the implementation above, nll equates to H(q,p) from eq-3.And then, the loss/c equates to H(u,p) from eq-3 as well where, c equals total number of classes.. For reference again, we know that eq-3 was:. pred = F.log_softmax(x, dim=-1) loss = F.nll_loss(pred, target) loss. My question is about how is log softmax implemented in practice with the cross-entropy loss. Parameters. Categorical Cross Entropy Loss Function. q -> [0.033, 0.087, 0.236, 0.644] ケース1： p <- [0,0,0,1] 真の結果がクラス4の場合.

Archbishop Mitty Alumni, Fashion Show Stage Layout, Introduction For Hotel Management Student, Numerical Methods Engineering, Diksha Certificate Issue, Cabin Rentals In Miami Beach Florida, Cerebus Syndrome Definition, Restaurants In Brazil Rio De Janeiro, Film Rewind Button Function, Frequency Distribution In Statistics Ppt,

Vélemény, hozzászólás?

Az email címet nem tesszük közzé. A kötelező mezőket * karakterrel jelöljük.

0-24

Annak érdekében, hogy akár hétvégén vagy éjszaka is megfelelő védelemhez juthasson, telefonos ügyeletet tartok, melynek keretében bármikor hívhat, ha segítségre van szüksége.

 Tel.: +36702062206

×
Büntetőjog

Amennyiben Önt letartóztatják, előállítják, akkor egy meggondolatlan mondat vagy ésszerűtlen döntés később az eljárás folyamán óriási hátrányt okozhat Önnek.

Tapasztalatom szerint már a kihallgatás első percei is óriási pszichikai nyomást jelentenek a terhelt számára, pedig a „tiszta fejre” és meggondolt viselkedésre ilyenkor óriási szükség van. Ez az a helyzet, ahol Ön nem hibázhat, nem kockáztathat, nagyon fontos, hogy már elsőre jól döntsön!

Védőként én nem csupán segítek Önnek az eljárás folyamán az eljárási cselekmények elvégzésében (beadvány szerkesztés, jelenlét a kihallgatásokon stb.) hanem egy kézben tartva mérem fel lehetőségeit, kidolgozom védelmének precíz stratégiáit, majd ennek alapján határozom meg azt az eszközrendszert, amellyel végig képviselhetem Önt és eredményül elérhetem, hogy semmiképp ne érje indokolatlan hátrány a büntetőeljárás következményeként.

Védőügyvédjeként én nem csupán bástyaként védem érdekeit a hatóságokkal szemben és dolgozom védelmének stratégiáján, hanem nagy hangsúlyt fektetek az Ön folyamatos tájékoztatására, egyben enyhítve esetleges kilátástalannak tűnő helyzetét is.

×
Polgári jog

Jogi tanácsadás, ügyintézés. Peren kívüli megegyezések teljes körű lebonyolítása. Megállapodások, szerződések és az ezekhez kapcsolódó dokumentációk megszerkesztése, ellenjegyzése. Bíróságok és más hatóságok előtti teljes körű jogi képviselet különösen az alábbi területeken:

×
Ingatlanjog

Ingatlan tulajdonjogának átruházáshoz kapcsolódó szerződések (adásvétel, ajándékozás, csere, stb.) elkészítése és ügyvédi ellenjegyzése, valamint teljes körű jogi tanácsadás és földhivatal és adóhatóság előtti jogi képviselet.

Bérleti szerződések szerkesztése és ellenjegyzése.

Ingatlan átminősítése során jogi képviselet ellátása.

Közös tulajdonú ingatlanokkal kapcsolatos ügyek, jogviták, valamint a közös tulajdon megszüntetésével kapcsolatos ügyekben való jogi képviselet ellátása.

Társasház alapítása, alapító okiratok megszerkesztése, társasházak állandó és eseti jogi képviselete, jogi tanácsadás.

Ingatlanokhoz kapcsolódó haszonélvezeti-, használati-, szolgalmi jog alapítása vagy megszüntetése során jogi képviselet ellátása, ezekkel kapcsolatos okiratok szerkesztése.

Ingatlanokkal kapcsolatos birtokviták, valamint elbirtoklási ügyekben való ügyvédi képviselet.

Az illetékes földhivatalok előtti teljes körű képviselet és ügyintézés.

×
Társasági jog

Cégalapítási és változásbejegyzési eljárásban, továbbá végelszámolási eljárásban teljes körű jogi képviselet ellátása, okiratok szerkesztése és ellenjegyzése

Tulajdonrész, illetve üzletrész adásvételi szerződések megszerkesztése és ügyvédi ellenjegyzése.

×
Állandó, komplex képviselet

Még mindig él a cégvezetőkben az a tévképzet, hogy ügyvédet választani egy vállalkozás vagy társaság számára elegendő akkor, ha bíróságra kell menni.

Semmivel sem árthat annyit cége nehezen elért sikereinek, mint, ha megfelelő jogi képviselet nélkül hagyná vállalatát!

Irodámban egyedi megállapodás alapján lehetőség van állandó megbízás megkötésére, melynek keretében folyamatosan együtt tudunk működni, bármilyen felmerülő kérdés probléma esetén kereshet személyesen vagy telefonon is.  Ennek nem csupán az az előnye, hogy Ön állandó ügyfelemként előnyt élvez majd időpont-egyeztetéskor, hanem ennél sokkal fontosabb, hogy az Ön cégét megismerve személyesen kezeskedem arról, hogy tevékenysége folyamatosan a törvényesség talaján maradjon. Megismerve az Ön cégének munkafolyamatait és folyamatosan együttműködve vezetőséggel a jogi tudást igénylő helyzeteket nem csupán utólag tudjuk kezelni, akkor, amikor már „ég a ház”, hanem előre felkészülve gondoskodhatunk arról, hogy Önt ne érhesse meglepetés.

×