improving deep neural networks notes

The normal cost function that we want to minimize is: The L1 regularization version makes a lot of w values become zeros, which makes the model size smaller. Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance. (Has to be the largest set). You should try the previous two points until you have a low bias and low variance. The code inside an epoch should be vectorized. Multiple Neural Networks Another simple way to improve generalization, especially when caused by noisy data or a small dataset, is to train multiple neural networks and average their outputs. You can always update your selection by clicking Cookie Preferences at the bottom of the page. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization This is the second course of the deep learning specialization at Coursera which is moderated by course is taught by Andrew Ng. Code v.2 (we feed the inputs to the algorithm through coefficients): We use optional third-party analytics cookies to understand how you use so we can build better products. Run the Session. Vector d[l] is used for forward and back propagation and is the same for them, but it is different for each iteration (pass) or training example. To make inputs belong to other distribution (with other mean and variance). You will also learn TensorFlow. We will take these parameters as the best parameters. But its advantage is that you don't need to search a hyperparameter like in other regularization approaches (like. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. Each of C values in the output layer will contain a probability of the example to belong to each of the classes. If we are using batch normalization parameters b[1], ..., b[L] doesn't count because they will be eliminated after mean subtraction step, so: So if you are using batch normalization, you can remove b[l] or make it always zero. And dropout is a regularization technique to prevent overfitting. Another idea to get the bias / variance if you don't have a 2D plotting mechanism: high Bias (underfitting) && High variance (overfitting) for example: These Assumptions came from that human has 0% error. It will take a long time for gradient descent to learn anything. 0.97%. The last example explains that the activations (and similarly derivatives) will be decreased/increased exponentially as a function of number of layers. As mentioned before mini-batch gradient descent won't reach the optimum point (converge). Millions of developers and companies build, ship, and maintain their software on GitHub — the largest and most advanced development platform in the world. In the last layer we will have to activate the Softmax activation function instead of the sigmoid activation. Let's say you have a specific range for a hyperparameter from "a" to "b". Apply dropout both during forward and backward propagation. Softmax is a generalization of logistic activation function to C classes. In this technique we plot the training set and the dev set cost together for each iteration. The new term (1 - (learning_rate*lambda)/m) * w[l] causes the weight to decay in proportion to its size. The course is taught by Andrew Ng. A downside of dropout is that the cost function J is not well defined and it will be hard to debug (plot J by iteration). Thus, by penalizing the square values of the weights in the cost function you drive all the weights to smaller values. L2-regularization relies on the assumption that a model with small weights is simpler than a model with large weights. If you're more worried about some layers overfitting than others, you can set a lower. Deep Neural Networks are the solution to complex tasks like Natural Language Processing, Computer Vision, Speech Synthesis etc. In practice most often you will use a deep learning framework and it will contain some default implementation of doing such a thing. 5 stars. If the problem isn't like that you'll need to use human error as baseline. Artificial neural network with a chip. Bias / Variance techniques are Easy to learn, but difficult to master. This makes your inputs centered around 0. We do this because we want the neural network to generalise well. It could contain some ups and downs but generally it has to go down (unlike the batch gradient descent where cost function descreases on each iteration). If it has, then it will perform badly on new data that it hasn’t been trained on. If we don't normalize the inputs our cost function will be deep and its shape will be inconsistent (elongated) then optimizing it will take a long time. The second reason is that batch normalization reduces the problem of input values changing (shifting). Here are some of the leading deep learning frameworks: These frameworks are getting better month by month. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. It has to be a power of 2 (because of the way computer memory is layed out and accessed, sometimes your code runs faster if your mini-batch size is a power of 2): Make sure that mini-batch fits in CPU/GPU memory. Andrew prefers to use L2 regularization instead of early stopping because this technique simultaneously tries to minimize the cost function and not to overfit which contradicts the orthogonalization approach (will be discussed further). Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking. In deep learning frameworks there are a lot of things that you can do with one line of code like changing the optimizer. For point to be a local optima it has to be a local optima for each of the dimensions which is highly unlikely. too noisy regarding cost minimization (can be reduced by using smaller learning rate), won't ever converge (reach the minimum cost), make progress without waiting to process the entire training set, doesn't always exactly converge (oscelates in a very small region, but you can reduce learning rate). And If W < I (Identity matrix) the activation and gradients will vanish. It depends a lot on your problem. In every example we have used so far we were talking about binary classification. Recall the housing price prediction problem from before: given the size of the house, we want to … Suppose we have m = 50 million. Run gradient checking at random initialization and train the network for a while maybe there's a bug which can be seen when w's and b's become larger (further from 0) and can't be seen on the first iteration (when w's and b's are very small). In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. If small training set (< 2000 examples) - use batch gradient descent. 1. The Vanishing / Exploding gradients occurs when your derivatives become very small or very big. Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization. L2 regularization makes your decision boundary smoother. For example if cat training pictures is from the web and the dev/test pictures are from users cell phone they will mismatch. Implementation tip: if you implement gradient descent, one of the steps to debug gradient descent is to plot the cost function J as a function of the number of iterations of gradient descent and you want to see that the cost function J decreases monotonically after every elevation of gradient descent with regularization. Common Challenges with Deep Learning Models. core principles of neural networks and deep learning, rather than a hazy understanding of a long laundry list of ideas. A better terminology is to call it a dev set as its used in the development. In the older days before deep learning, there was a "Bias/variance tradeoff". they're used to gather information about the pages you visit and how many clicks you need to accomplish a task. A lot of researchers are using dropout with Computer Vision (CV) because they have a very big input size and almost never have enough data, so overfitting is the usual problem. To train this data it will take a huge processing time for one step. But because now you have more options/tools for solving the bias and variance problem its really helpful to use deep learning. For example in OCR, you can impose random rotations and distortions to digits/letters. so the trend on the ratio of splitting the models: If size of the dataset is 100 to 1000000 ==> 60/20/20, If size of the dataset is 1000000 to INF ==> 98/1/1 or 99.5/0.25/0.25. You only use dropout during training. Improving the Accuracy of Deep Neural Networks Through Developing New Activation Functions @article{Mercioni2020ImprovingTA, title={Improving the Accuracy of Deep Neural Networks Through Developing New Activation Functions}, author={Marina Adriana Mercioni and Angel Marcel Tat and S. Holban}, journal={2020 IEEE 16th … In most cases Andrew Ng tells that he uses the L2 regularization. Programming frameworks can not only shorten your coding time but sometimes also perform optimizations that speed up your code. Training a bigger neural network never hurts. Deep neural networks show promise for predicting future self-harm based on clinical notes . Making the NN learn the distribution of the outputs. If λ is too large, it is also possible to "oversmooth", resulting in a model with high bias. The weights $W^{[l]}$ should be initialized randomly to break symmetry, It is however okay to initialize the biases $b^{[l]}$ to zeros. While in Mini-Batch gradient descent we run the gradient descent on the mini datasets. You will try to build a model upon training set then try to optimize hyperparameters on dev set as much as possible. 0.10%. You signed in with another tab or window. In TensorFlow a placeholder is a variable you can assign a value to later. and the copyright belongs to 10.56%. In mini-batch algorithm, the cost won't go down with each step as it does in batch algorithm. By plotting various metrics during training, you can learn how the training is progressing. In this paper, we propose a new approach for improving accuracy of traffic sign recognition. For example, if. During training time, divide each dropout layer by keep_prob to keep the same expected value for the activations. 80% stay, 20% dropped, # increase a3 to not reduce the expected value of output, # (ensures that the expected value of a3 remains the same) - to solve the scaling problem, # can be written as this - cost = w**2 - 10*w + 25, # Runs the definition of w, if you print this it will print zero, # better for cleaning up in case of error/exception. So If W > I (Identity matrix) the activation and gradients will explode. RMSprop will make the cost function move slower on the vertical direction and faster on the horizontal direction in the following example: With RMSprop you can increase your learning rate. Then, if we have 2 hidden units per layer and x1 = x2 = 1, we result in: A partial solution to the Vanishing / Exploding gradients in NN is better or more careful choice of the random initialization of weights, In a single neuron (Perceptron model): Z = w1x1 + w2x2 + ... + wnxn, So it turns out that we need the variance which equals 1/n_x to be the range of W's. layers; Hidden units; Learning rates ; Activation functions; Idea - Code - Experiment. But the code is more efficient and faster using the exponentially weighted averages algorithm. Deep Learning models usually perform really well on most kinds of data. dw[l] = (from back propagation), The new way: In practice this penalizes large weights and effectively limits the freedom in your model. We need another dat… : What you should remember: My personal notes ${1_{st}}$ week: practical-aspects-of-deep … If you want a good papers in deep learning look at the ICLR proceedings (Or NIPS proceedings) and that will give you a really good view of the field. DOI: 10.1109/ICCP51029.2020.9266162 Corpus ID: 227232667. they're used to log you in. And this makes training difficult, especially if your gradients are exponentially smaller than L, then gradient descent will take tiny little steps. As was presented in the neural networks tutorial, we always split our available data into at least a training and a test set. Don't use dropout (randomly eliminate nodes) during test time. There are optimization algorithms that are better than. When we train a NN with Batch normalization, we compute the mean and the variance of the mini-batch. Improving Generalization for Convolutional Neural Networks Carlo Tomasi October 26, 2020 Stochastic Gradient Descent (SGD) minimizes the training risk L T(w) of neural network hover the set of all possible network parameters in w 2Rm. The value of λ is a hyperparameter that you can tune using a dev set. We will use the estimated values of the mean and variance to test. This method is also sometimes called "Running average". The shape of the cost function will be consistent (look more symmetric like circle in 2D example) and we can use a larger learning rate alpha - the optimization will be faster. Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. Discussion and Review Please click TOC 1.1 Welcome The courses are in this following sequence (a specialization): 1) Neural Networks and Deep Learning, 2) Improving Deep Neural Networks: Hyperparameter tuning, Regu- Improving their performance is as important as understanding how they work. And each day you nudge your parameters a little during training. If we just throw all the data we have at the network during training, we will have no idea if it has over-fitted on the training data. This will run the operations you'd written above. We can implement this algorithm with more accurate results using a moving window. Improving the Robustness of Deep Neural Networks via Stability Training Stephan Zheng Google, Caltech Yang Song Google Thomas Leung Google Ian Goodfellow Google Abstract In this paper we address the issue of output instability of deep neural networks: small perturbations in the visual input can … Deep Learning Toolbox™ provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. The mean and the variance of one example won't make sense. In Batch gradient descent we run the gradient descent on the whole dataset. L2 matrix norm because of arcane technical math reasons is called Frobenius norm: The normal cost function that we want to minimize is: The bias correction helps make the exponentially weighted averages more accurate. In the previous video, the intuition was that dropout randomly knocks out units in your network. Make sure the dev and test set are coming from the same distribution. Specifically, the overall reduction in epochs was 13.67%. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. 3 stars. It's possible to show that dropout has a similar effect to L2 regularization. In this set of notes, we give an overview of neural networks, discuss vectorization and discuss training neural networks with backpropagation. 1 star. weights end up smaller ("weight decay") - are pushed to smaller values. 88.30%. With such a deep neural network, if your activations or gradients increase or decrease exponentially as a function of L, then these values could get really big or really small. Train/dev/test sets. For Andrew Ng, learning rate decay has less priority. Now lets compute the Exponentially weighted averages: If we plot this it will represent averages over, Best beta average for our case is between 0.9 and 0.98. But a lot of people in this case call the dev set as the test set. If algorithm fails grad check, look at components to try to identify the bug. So lets say when we initialize W's like this (better to use with tanh activation): Setting initialization part inside sqrt to 2/n[l-1] for ReLU is better: Number 1 or 2 in the nominator can also be a hyperparameter to tune (but not the first to start with), This is one of the best way of partially solution to Vanishing / Exploding gradients (ReLU + Weight Initialization with variance) which will help gradients not to vanish/explode too quickly. There are many good deep learning frameworks. For regularization use other regularization techniques (L2 or dropout). This leads to a smoother model in which the output changes more slowly as the input changes. This is the second course of the Deep Learning Specialization. Its impossible to get all your hyperparameters right on a new application from the first time. There is a partial solution that doesn't completely solve this problem but it helps a lot - careful choice of how you initialize the weights (next video). This is my personal summary after studying the course, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, which belongs to Deep Learning Specialization. Like the course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences – but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not – and as a result, they are more expressive, and more powerful than anything we’ve seen on tasks that we haven’t made progress on in decades. This tutorial is divided into five parts; they are: 1. Comparison between them can be found, Ease of programming (development and deployment), Truly open (open source with good governance). We will pick the point at which the training set error and dev set error are best (lowest training cost with lowest dev cost). Gradient checking doesn't work with dropout because J is not consistent. Improving Deep Neural Networks Posted on 2019-04-20 | Edited on 2019-04-24 ... Specialization. And when it comes to image data, deep learning models, especially convolutional neural networks (CNNs), outperform almost all other models. And this makes training difficult, especially if your gradients are exponentially smaller than L, then gradient descent will take tiny little steps. 1 Neural Networks We will start small and slowly build up a neural network, step by step. You could also apply a random position and rotation to an image to get more data. This is my personal summary after studying the course, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, which belongs to Deep Learning Specialization. Note about TensorFlow 1 and TensorFlow 2 10m. 2 stars. Since the risk is a very non-convex function of w, the nal vector w^ of weights typically only achieves a local minimum. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, Understanding mini-batch gradient descent, Understanding exponentially weighted averages, Bias correction in exponentially weighted averages, Hyperparameter tuning, Batch Normalization and Programming Frameworks, Using an appropriate scale to pick hyperparameters, Hyperparameters tuning in practice: Pandas vs. Caviar, Fitting Batch Normalization into a neural network. Then after your model is ready you try and evaluate the testing set. ${1_{st}}$ week: practical-aspects-of-deep-learning, $3_{rd}$ week: hyperparameter-tuning-batch-normalization-and-programming-frameworks, week1: practical-aspects-of-deep-learningweek2: optimization-algorithmsweek3: hyperparameter-tuning-batch-normalization-and-programming-frameworks, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, 01_setting-up-your-machine-learning-application, 02_why-regularization-reduces-overfitting, 03_weight-initialization-for-deep-networks, 06_gradient-checking-implementation-notes, 02_understanding-mini-batch-gradient-descent, 04_understanding-exponentially-weighted-averages, 05_bias-correction-in-exponentially-weighted-averages, hyperparameter-tuning-batch-normalization-and-programming-frameworks, 02_using-an-appropriate-scale-to-pick-hyperparameters, 03_hyperparameters-tuning-in-practice-pandas-vs-caviar, 02_fitting-batch-norm-into-a-neural-network, 04_introduction-to-programming-frameworks, Course Videos on YouTube 4. Week 1 Practical aspects of Deep Learning. One of the ways to tune is to sample a grid with. Other learning rate decay methods (continuous): Some people perform learning rate decay discretely - repeatedly decrease after some number of epochs. There's a numerical way to calculate the derivative: Gradient checking approximates the gradients and is very helpful for finding the errors in your backpropagation implementation but it's slower than gradient descent (so use only for debugging). Adding regularization to NN will help it reduce variance (overfitting). Plateau is a region where the derivative is close to zero for a long time. There are some debates in the deep learning literature about whether you should normalize values before the activation function. In programming language terms, think of it as mastering the core syntax, libraries and data structures of a new language. The dev set rule is to try them on some of the good models you've created. In deep learning, the number of hidden layers, mostly non-linear, can be large; say about 1000 layers. Department of Computer Science, University of Toronto y IBM T. J. Watson Research Center, Yorktown Heights, NY 10598 ABSTRACT Recently, pre-trained deep neural networks (DNNs) have outperformed traditional acoustic models based on … Hyperparameter tuning, Batch Normalization, Programming ... 6395 reviews. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. 1 practice exercise. As the recent success of deep neural networks solved many single domain tasks, next generation problems should be on multi-domain tasks. To its previous stage, we investigated how auxiliary information can affect the deep learning model. Deep learning is now in the phase of doing something with the frameworks and not from scratch to keep on going. With such a deep neural network, if your activations or gradients increase or decrease exponentially as a function of L, then these values could get really big or really small. When you find some hyperparameters values that give you a better performance - zoom into a smaller region around these values and sample more densely within this space. What is L2-regularization actually doing? Your data will be split into three parts: Training set. We need to tune our hyperparameters to get the best out of them. Case Study: Improving the Performance of our Vehicle Classification Model . If you don't have much computational resources you can use the "babysitting model": Day 0 you might initialize your parameter as random and then start training. There is an technique called gradient checking which tells you if your implementation of backpropagation is correct. There's an activation which is called hard max, which gets 1 for the maximum value and zeros for the others. The contribution of this work is three-fold: First, region proposal based on segmentation technique is applied to cluster traffic signs into several sub regions depending upon the supplemental signs and the main sign color. Adam optimization and RMSprop are among the optimization algorithms that worked very well with a lot of NN architectures. Module 1: Practical Aspects of Deep Learning. \"`Artificial Neural Networks' are massively parallel interconnectednetworks of simple (usually adaptive) elements and their hierarchicalorganizations which are intended to interact with the objects of thereal world in the same way as biological nervous systems do.\" -- T. Kohonen 3. By setting the primary class and auxiliary classes, characteristics of deep learning models can be studied when the additional task is added to … Forcing the inputs to a distribution with zero mean and variance of 1. Mini-batch gradient descent works much faster in the large datasets. If C = 2 softmax reduces to logistic regression. Improving the Robustness of Deep Neural Networks via Stability Training Abstract: In this paper we address the issue of output instability of deep neural networks: small perturbations in the visual input can significantly distort the feature embeddings and output of a neural network.

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