be a set of test conditions, where ⊥ is a special symbol representing that the place is not within the range [a,b]. This makes neuron coverage less powerful for testing, which aims to exploit the system with a non-trivial number of test cases in order to expose incorrect behaviour of a network. where f0(x)=x. When input rows containing digits are fed to the LSTM cells, the model will start learning and the short term memory, represented by the hidden outputs ht, starts to have strong reactions. Probalistic 5. It is therefore reasonable to divide the possible information of a component ht(k) into positive (>0) and negative (<0). We now consider information represented in the gates f, i, and o. tim.roberts. This allows it to exhibit temporal dynamic behavior. When a gradient of the network loss function with respect to the input is obtainable (for continuous inputs such as images), we design a set of mutation operations based on the gradient direction. x′ is only added into the test suite T if it falls within the norm ball with respect to its original seed The model has four layers: an embedding layer, an LSTM layer, a dropout layer, and a fully connected layer. You are training this RNN language model. Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. Recurrent Neural Networks (RNNs) are widely used for data with some kind of sequential structure. ∙ For each set of seeds, 2000 test cases are generated. Since the discovery of adversarial examples in deep feedforward neural networks (FNNs), particularly convolutional neural networks (CNNs) for image classification, , and (3) existing deep learning platforms such as Tensorflow do not support a direct, easy access to the internal structures of LSTM layers. we compare different metrics and show their complementary in guiding test case generation, and Suppose your training examples are sentences (sequences of words). To guarantee the quality of test cases generated, 1. analysis, a network trained on MNIST dataset for image classification, and a Recurrent Neural Network(RNN) are a type of Neural Network where the output from previous step are fed as input to the current step. adversarial attacks for recurrent neural networks on tasks such as View in browser Resource created Wednesday 30 September 2020, 07:06:18 AM. Sentences are also… 6. According to Equation (1), in addition to the gates (forget gate ft and internal gate it), the hidden state ht−1 is also a factor determining the value of ct. Coverage time exhibits the difficulty of asserting a feature. The Table I, Recurrent Neural Networks have loops. week1 Created Friday 02 February 2018 Why sequence models examples of seq data (either input or output): speech recognition music generation sentiment classification DNA seq analysis Machine translation video activity recognition name entity recognition (NER) → in this course: learn models applicable to these different settings. Scaling Our Test Data. In traditional neural networks, all the inputs and outputs are independent of each other, but in cases like when it is required to predict the next word of a sentence, the previous words are required and hence there is a need to remember the previous words. Questions and Answers. In the past few years, one of the deep learning algorithms, the recurrent neural network (RNN), has shown an outstanding ability in sequence labeling and prediction tasks for sequential data. ∙ First, What 2 subjects are neural networks usually associated with? easy to be asserted, which means that, for many inputs, within this range there are great changes in hidden outputs. The test coverage metric MN(RC,AC,T) for a network N is defined as the percentage of the test conditions in RC asserted by the test suite T with method AC. As far as we know, [12] is the only paper so far that provides coverage metrics for RNNs, in which at first an automaton is learned to abstract the RNN under test and then test cases are generated for this automaton. One reason for neuron coverage to be less strong is that, it treats neurons as independent units, without considering their collective behaviour. As shown in Figure 8, several SMILES strings can represent the same molecular structure. ... Recurrent 3. such that D(ξ+)=⋃i∈ΓDi(ξ+) and for all i,j∈Γ, i≠j implies that Di(ξ+)∩Di(ξ+)=∅. Thus, there are 500 test conditions for both cell and gate metrics. at a time step. takes into account the causal relation between features of different layers and closer. Neural networks can be simulated on a conventional computer but the main advantage of neural networks – parallel execution – is lost. In this paper, we use the forget gate f as an example. time-series data. Here’re the update equations for the GRU. Although our test metrics are not tightly correlated with each other, weak test requirements are too easy to achieve. Stanford university Deep Learning course module Recurrent Neural Networks (Quiz) for computer science and information technology students. Different from the MNIST model where a small change to its input image is still a valid image, the input to IMDB model is a sequence of words, on which a small change may lead to an unrecognisable (and invalid) text paragraph. For a sequence of inputs x1,...,xn, ϕ will handle them individually without considering results from previous predictions, that is, the result of ϕ(xi) is independent of the results of ϕ(xj) when j≠i. from T0 (Line 1). non-trivial, and (3) there is a significant percentage of adversarial examples in the test suite. 4. So in particular, deactivation value from time step one is passed on to time step two. Sequence coverage is more complicated than cell and gate coverages, because the number of sequence patterns is exponential with respect to the sequence length. Coverage-guided testing has been extended to work with FNNs in recent work such as [31, 44, 25, 37, 38, 24], , where a collection of test metrics and test case generation algorithms are proposed. The sentiment analysis network [36] has three layers, i.e., an embedding layer, an LSTM layer, and a fully-connected layer, with 213,301 trainable parameters. Recurrent neural networks are the foundation of many sequence-to-sequence mod-els in machine learning, such as machine translation and speech synthesis. ... C. Recurrent Neural Network. The short reviews are padded in the left side with 0s. In this paper, we consider Song, and H. Qu, Understanding hidden memories of recurrent neural networks, TensorFuzz: debugging neural networks with coverage-guided fuzzing, N. Papernot, P. D. McDaniel, A. Swami, and R. E. Harang, Crafting adversarial input sequences for recurrent neural networks, DeepXplore: automated whitebox testing of deep learning systems, Recurrent neural networks for time series forecasting, P. E. Rauber, S. G. Fadel, A. X. Falcao, and A. C. Telea, Visualizing the hidden activity of artificial neural networks, IEEE transactions on visualization and computer graphics, M. H. S. Segler, T. Kogej, C. Tyrchan, and M. P. Waller, Generating focused molecule libraries for drug discovery with recurrent neural networks, Structural coverage metrics for deep neural networks, Y. For MNIST model, the test suite is obtained by setting thresholds as αξ=6 and αf=0.85. In this assignment, you will implement your first Recurrent Neural Network in numpy. multi-step information evolution of positive and negative aggregate hidden The one in the top row is for ξ+t and in the second row is for ξ−t. ∙ Hopfield networks. Our experiments exercise the sequence coverage using 2 and 3 symbols for each model, i.e., Γ={a,b} and Γ={a,b,c}, to represent the sequence data and test the sequential pattern respectively. Sun, M. Wu, W. Ruan, X. Huang, M. Kwiatkowska, and D. Kroening, Concolic testing for deep neural networks, C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus, D. Tsipras, S. Santurkar, L. Engstrom, A. Turner, and A. Madry, International Conference on Learning Representations, Detecting adversarial samples for deep neural networks through mutation testing, EDA: easy data augmentation techniques for boosting performance on text classification tasks, Feature-guided black-box safety testing of deep neural networks, Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey, J. Klingner, A. Shah, M. Johnson, X. Liu, L. Kaiser, S. Gouws, Y. Kato, T. Kudo, H. Kazawa, K. Stevens, G. Kurian, N. Patil, W. Wang, C. Young, J. Smith, J. Riesa, A. Rudnick, O. Vinyals, G. Corrado, M. Hughes, and J. Subsequently, there is a fully-connected layer of Enjoy! This is to make sure cells between 450-500 have the real input words but not padded 0s. Since MNIST model has 28 cells in LSTM layer, the total amount of test conditions to cover is 28 for both cell and gate coverage. Gate and hidden state are internal structural components of an LSTM cell. For sentiment analysis, it’s ambiguous to quantify the text meanings. Schematically, a RNN layer uses a for loop to iterate over the timesteps of a sequence, while maintaining an internal state that encodes information about the timesteps it has seen so far. coursera 吴恩达 -- 第五课 RNN ：第一周 Recurrent Neural Networks Quiz 谦虚的海绵 2018-03-30 21:33:14 3962 收藏 1 分类专栏： 深度学习 文章标签： RNN shows the results. with the utilization of RDkit, each input SMILES string is converted into molfile format, and then atom order will be changed randomly before converting back. A recurrent neural network looks quite similar to a traditional neural network except that a memory-state is added to the neurons. In this part, we first formulate two types of internal information of LSTMs that will be used later to define our coverage criteria. Unlike FFNN, RNNs can use their internal memory to process arbitrary sequences of inputs. Choose the best answer. Deep (recurrent) neural networks, φ, consist of multiple layers, and we write Lk for the k-th layer. Given a test case x and a test condition s∈R[a,b]SC, we say that s is asserted by x with positive information, denoted as APSC(s,x), if for all j∈{1..n}, we have that s(j)≠⊥ implies that ξ+j(x)∈Ds(j)(ξ+). By inputting data to an RNN, an output value corresponding to the input value is obtained through a hidden layer. COMP9444 20T3 (Neural Networks and Deep Learning) is powered by WebCMS3 CRICOS Provider No. These metrics are based on the structural information of the FNNs, such as the neurons. beginner , deep learning , programming , +2 more pandas , matplotlib 275 The number of LSTM cells Assume that we are working with the k-th layer which is an LSTM layer. What 2 subjects are neural networks usually associated with? According to Equation (1), ft is used to directly control the information flowing from ct−1 to ct. The computation to include a memory is simple. Each question has a time limit of 10 minutes just to make things more fun. Statistically, in the first several time steps it is hard for the abstract gate value to meet this strict test condition. Estimating P(y^{} | y^{<1>}, y^{<2>}, …, y^{}). Let fx,t be the value of the gate f at time t when the input is x. Feedforward neural networks (FNNs) can be represented as a function ϕ:X→Y, mapping from input domain X to output domain Y, and is usually used to perform predictions based on an input x∈X, or recognise patterns in x. Tutorial: Optimizing Neural Networks using Keras (with Image recognition case study) An Introduction to Implementing Neural Networks using TensorFlow . share, Generation-based fuzzing is a software testing approach which is able to... natural language processing [30, 21, 9, 49, 13, 14, 2] and For the IMDB model, it takes 7 test cases on average to reach 100% neuron coverage, and only 20 test cases are needed for the Lipophilicity prediction model. Which of the following refers to the j^{th}jth word in the i^{th}ith training example? As stated in Section VIII-A, the three LSTM models have high levels of accuracy in training dataset. While Definition 5 is for ξ+, it can be extended to work with ξ−. By this process, a time series is transformed into a sequence of symbols, or a clause. A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. What are Sequence Tasks? Explain the format and method by which input was fed to the NetTalk system, and the target output. The adversarial example generation has been an active domain and a large body of research has been conducted for The reason behind this is that the input samples are padded or truncated to the same length. Instead of considering only the individual neurons, in the following we design several test metrics for LSTM layers that take Our model is trained up to RMSE = 0.2371 in training dataset and RMSE = 0.6278 in test dataset, which are better than the traditional and convolutional methods used in, , the symbolic representation for a time series is obtained by sampling a number of data and splitting the area under the curve of the empirical distribution, so that each sub-area is associated with a symbol. Input and Output Weight and Bias Linear and Logistic Function Activation and Threshold Answer:-Weight and Bias (4)Recurrent Networks work best for Speech Recognition. Here, we focus on MNSIT model to show how adversarial rate and perturbations on adversarial examples vary with different values of oracle radius. There are major differences between our work and [12]: (1) the approach in [12] treats the RNNs as a black-boxes, whereas our coverage criteria explicitly consider the RNN internal structure; and (2) the metrics in hard to occur in our experiment. True False Answer:-True (3)Prediction Accuracy of a Neural Network depends on _____ and _____. Which of the following refers to the j^{th}jth word in the i^{th}ith training example? Quiz Week 1: Recurrent Neural Networks: 1. Vanilla Forward Pass 2. define a set of test conditions to partition the input space. 2 Recurrent Neural Networks (RNN) are very effective for Natural Language Processing and other sequence tasks because they have “memory”. 9. Given a LSTM network and a specified test metric, a test suite is generated: the coverage result and the number of adversarial examples out of the test suite are used to evaluate the robustness of the LSTM network under test. ∙ This can be seen from the high coverage times of the memory values over 0.85 at the last few cells. Contribute to ilarum19/coursera-deeplearning.ai-Sequence-Models-Course-5 development by creating an account on GitHub. Consider this RNN: This specific type of architecture is appropriate when: T_x = T_y: 3. COMP9444 Neural Networks and Deep Learning Quiz 5 (Recurrent Networks) This is an optional quiz to test your understanding of Recurrent Networks from Week 5. Every test condition is some functionality or behaviour of the system we want to verify. Fei-Fei Li & Andrej Karpathy & Justin Johnson Lecture 10 -6 8 Feb 2016 Recurrent Networks offer a lot of flexibility: Vanilla Neural Networks. For tasks that involve sequential inputs, such as speech and language, it is often better to use RNNs. Since we set the review length to 500 words, the text we used for training and testing maybe too long or too short. the internal mechanism of how the LSTM layer processes data. It is not hard to see that a feature in itself can be harder to be asserted by a set of test cases than the neurons supporting it. Recurrent neural networks (RNNs) have been widely applied to various The problem statement for the recurrent neural networks reads (20) f R N N: x < τ >, 0 ≤ τ ≤ t → y < t >, with x < τ >, 0 ≤ τ ≤ t denoting the history of the input vector x (of length N IN) from 0 to time t, and y < t > = y [t] denoting the output vector of length N OUT at time t. That is reason why in Table II, some minimal test sets depict the high coverage rate for all test metrics. In a general way, the memory data follow the normal distribution. (Check all that apply). This step is to ensure that the appearance of symbols has equal probability. share. We evaluate the effectiveness of coverage-guided LSTM testing using adversarial examples as a proxy, Experimental results for the three models are presented in Table II. ... Neural Networks are a brand new field. It has been formally proved in [37] that the criteria in [31, 25] are specially cases of its MC/DC variant for In the above experiments, we fix the oracle radius. These loops make recurrent neural networks seem kind of mysterious. share, Recurrent Neural Network (RNN) and its variations such as Long Short-Ter... ... Why is an RNN (Recurrent Neural Network) used for machine translation, say translating English to French? In our experiments, we focus on the coverage of sequence patterns at intermediate cells, i.e., [a,b]=[19,23] for MNSIT, [a,b]=[480,484] for Sentiment Analysis, and [a,b]=[70,74] for Lipophilicity Prediction. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Intuitively, Δξt is the abstract representation of information updated between hidden states. In this paper, adversarial examples are used as A dictionary is used to map the The experiment results in Table I and Figure 10 indicates that sequence coverage is a rigorous test requirement. Ask Question Asked 7 years, 1 month ago. be difficult to improve after reaching a certain level. The detailed running results are shown in Figure 9. It shows that our mutation-based test generation has the following advantages. I... Recurrent Neural Network: Used for speech recognition, voice recognition, time series prediction, and natural language processing. Introducing Recurrent Neural Networks (RNN) A recurrent neural network is one type of an Artificial Neural Network (ANN) and is used in application areas of natural Language Processing (NLP) and Speech Recognition. Comparison of Recurrent Neural Networks (on the left) and Feedforward Neural Networks (on the right) Let’s take an idiom, such as “feeling under the weather”, which is commonly used when someone is ill, to aid us in the explanation of RNNs. [12] are based on the abstract automaton, instead we define test conditions directly upon the neural network. The first impression of coverage updating results is that small value of threshold is less effective in finding adversarial examples. You signed in with another tab or window. 2 Softmax is an activation function that generates the output between zero and one. We apply the test framework to several typical LSTM An RNN model is designed to recognize the sequential characteristics of data and thereafter using the patterns to predict the coming scenario. More test cases are considered, including the corner cases we want to test. To avoid “gaming against criteria”, our test case generation does not use test metrics as targets, which is recommended by Chilensky and Miller in their seminal work Our experimental results show the effectiveness of the test framework in working with a few LSTM networks on different application tasks. We are excited to bring you the details for Quiz 03 of the Kambria Code Challenge! Every sub-area is associated with a symbol. This is implemented uding the EDA toolkit [43], which was originally designed to augment the training data for improvement on text classification tasks. share. positive component values and negative ones, respectively, such that. Figure 5 presents the overall architecture of our test framework for LSTMs. Dean, Google’s neural machine translation system: bridging the gap between human and machine translation, Z. Wu, B. Ramsundar, E. N. Feinberg, J. Gomes, C. Geniesse, A. S. Pappu, K. Leswing, and V. S. Pande, MoleculeNet: A benchmark for molecular machine learning, Proceedings of the 2006 International Workshop on Automation of Software Test, M. Zhang, Y. Zhang, L. Zhang, C. Liu, and S. Khurshid, DeepRoad: GAN-based metamorphic autonomous driving system testing, Automated Software Engineering (ASE), 33rd IEEE/ACM International Conference on, testRNN: Coverage-guided Testing on Recurrent Neural Networks, Effective Quantization Approaches for Recurrent Neural Networks, Response Characterization for Auditing Cell Dynamics in Long Short-term and the abstract information of gates in Section III will be used. The rationale of taking minimal test suite is to reduce the overlaps, so that it is fairer to compare test metrics. the one-step information change of an aggregate hidden state, and the These 21 minutes ago. The sequence input of an image starts from the top rows to the bottom rows. Data such as time series have a sequential order that needs to be followed in order to understand. the safety loopholes (such as adversarial examples) but also help understand The steeper curve, to some extent, indicates the worse robustness of LSTM to the adversarial examples. Similar to text processing in the IMDB model, short SMILES inputs are padded with 0s to the left side. However, for 3 alphabetic symbols representation, very few sequence patterns are found in consideration of the same test cases. 2 symbols representation shows a good coverage result within 2000 test cases. In the experiments, we consider the use of LSTM networks in different domains, including a network trained on MNIST dataset for image classification, a network trained on an IMDB review dataset for sentiment analysis, and a network trained on a Lipophilicity dataset for molecular machine learning. Here we show that recurrent networks, specifically long short-term memory networks … Sun, M. Xue, B. Li, F. Juefei-Xu, C. Xie, L. Li, Y. Liu, J. Zhao, DeepMutation: mutation testing of deep learning systems, Software Reliability Engineering, IEEE 29th International Symposium on, MagNet: a two-pronged defense against adversarial examples, ACM Conference on Computer and Communications Security, Y. Ming, S. Cao, R. Zhang, Z. Li, Y. Chen, Y. From the graph, we can see that no matter which coverage metric is considered, in contrast to the last 200 cells, the first 300 cells are less active and obviously “lazy” in trying to be asserted. website: https://www.cse.unsw ... Back to top . ∙ we set thresholds αξ∈{3,6,9}, αf∈{0.71,0.78,0.85} and test the LSTM-2 layer. The test generation terminates when certain level of coverage rate has been satisfied or certain number of test cases have been generated. This indicates that our designed test metrics have few interactions in terms of test requirements. In the above diagram, a chunk of neural network, A, looks at some input Xt and outputs a value ht. Let J(f,x,y) be a cost function over the model f, with input x, and output y, and z⊆⋃kNk be a set of variables (hidden or not).
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