Pytorch cs231n

All datasets are subclasses of torch. Dataset i. Hence, they can all be passed to a torch. DataLoader which can load multiple samples parallelly using torch. For example:. All the datasets have almost similar API. If dataset is already downloaded, it is not downloaded again. This argument specifies which one to use.

Default: True. Default: images. Default: 3, Default: Default: 0. MS Coco Captions Dataset. MS Coco Detection Dataset. Tuple image, target. RandomCrop for images. ImageNet Classification Dataset. Accordingly dataset is selected. For training, loads one of the 10 pre-defined folds of 1k samples for the. SVHN Dataset. However, in this Dataset, we assign the label 0 to the digit 0 to be compatible with PyTorch loss functions which expect the class labels to be in the range [0, C-1].

This class needs scipy to load data from. Flickr8k Entities Dataset. Flickr30k Entities Dataset.As the beating heart of deep learning, a solid understanding of backpropagation is required for any deep learning practitioner.

Although there are a lot of good resources that explain backpropagation on the internet already, most of them explain from very different angles and each is good for a certain type of audience.

A good assessment of the understanding of any algorithm is whether you can code it out yourself from scratch. After reading this post, you should have an idea of how to implement your own version of backpropagation in Python. Mathematically, backpropagation is the process of computing gradients for the components of a function by applying the chain rule. In the case of neural networks, the function of interest is the loss function.

I like the interpretation by Andrej Karpathy in CSn: take the compute graph as real-valued circuits with logic gates. The gates are the operations in the function, e.

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This is a great mental model because it means backpropagation is a local process. Every gate in the circuit can compute its output and the local gradient without knowing the big picture. During the backward pass backpropagationthe gate applies the chain rule, i.

The backward pass can be implemented using a recursive approach to traverse back from the output of the circuit back to all the inputs. This is essentially what a gradient is. Another great analogy is a force of correction. The sign of the gradient indicates the direction of correction, and the magnitude indicates the strength. One of the best ways to visualize backprop is to draw the compute graph of the function.

To calculate its gradients, we can decompose it into add, sigmoid, square gates as shown in the animated steps below:. Concretely, the process consists of 3 high-level steps. You can follow along and manually calculate the values. Any kind of differentiable function can act as a gate, and we can group multiple gates into a single gate whenever it is convenient. Since we should never explicitly solve for the gradients analytically, the selection of these function components becomes a problem to consider.

Take the sigmoid function for example:.The torch package contains data structures for multi-dimensional tensors and mathematical operations over these are defined.

Additionally, it provides many utilities for efficient serializing of Tensors and arbitrary types, and other useful utilities. Returns True if the data type of input is a complex data type i. Returns True if the data type of input is a floating point data type i. Sets the default floating point dtype to d. This type will be used as default floating point type for type inference in torch.

The default floating point dtype is initially torch. Get the current default floating point torch. Sets the default torch. Tensor type to floating point tensor type t. This type will also be used as default floating point type for type inference in torch. The default floating point tensor type is initially torch. Returns the total number of elements in the input tensor. Thresholded matrices will ignore this parameter. Can override with any of the above options.

Returns True if your system supports flushing denormal numbers and it successfully configures flush denormal mode. Random sampling creation ops are listed under Random sampling and include: torch. Tensor s with values sampled from a broader range of distributions. Constructs a tensor with data. If you have a Tensor data and want to avoid a copy, use torch. If you have a NumPy ndarray and want to avoid a copy, use torch. When data is a tensor xtorch. Therefore torch.

pytorch cs231n

The equivalents using clone and detach are recommended. Can be a list, tuple, NumPy ndarrayscalar, and other types.

Default: if Noneinfers data type from data. Default: if Noneuses the current device for the default tensor type see torch. Default: False. Works only for CPU tensors. Constructs a sparse tensors in COO rdinate format with non-zero elements at the given indices with the given values. A sparse tensor can be uncoalescedin that case, there are duplicate coordinates in the indices, and the value at that index is the sum of all duplicate value entries: torch.

Will be cast to a torch. LongTensor internally. The indices are the coordinates of the non-zero values in the matrix, and thus should be two-dimensional where the first dimension is the number of tensor dimensions and the second dimension is the number of non-zero values. Sizeoptional — Size of the sparse tensor. If not provided the size will be inferred as the minimum size big enough to hold all non-zero elements.

Default: if None, infers data type from values. Default: if None, uses the current device for the default tensor type see torch. Convert the data into a torch. Similarly, if the data is an ndarray of the corresponding dtype and the device is the cpu, no copy will be performed.In this assignment, you will implement recurrent neural networks and apply them to image captioning on the Microsoft COCO data. You will also explore methods for visualizing the features of a pretrained model on ImageNet, and use this model to implement Style Transfer.

Finally, you will train a Generative Adversarial Network to generate images that look like a training dataset!

CS231n: Convolutional Neural Networks for Visual Recognition

You can work on the assignment in one of two ways: remotely on Google Colaboratory or locally on your own machine. Regardless of the method chosen, ensure you have followed the setup instructions before proceeding. Starter code containing Colab notebooks can be downloaded here.

If you choose to work with Google Colab, please familiarize yourself with the recommended workflow. Starter code containing jupyter notebooks can be downloaded here. Install Packages. Once you have the starter code, activate your environment the one you installed in the Software Setup page and run pip install -r requirements. Download data. Run the following from the assignment3 directory:. Start Jupyter Server. Complete each notebook, then once you are done, go to the submission instructions.

There are two versions of each of these notebooks, one for TensorFlow and one for PyTorch. No extra credit will be awarded if you do a question in both TensorFlow and PyTorch. The notebooks NetworkVisualization-TensorFlow.

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Please complete only one of the notebooks TensorFlow or PyTorch. No extra credit will be awardeded if you complete both notebooks.

pytorch cs231n

In thenotebooks StyleTransfer-TensorFlow. No extra credit will be awarded if you complete both notebooks. Please make sure that the submitted notebooks have been run and the cell outputs are visible. Once you have completed all notebooks and filled out the necessary code, there are two steps you must follow to submit your assignment:.

If you selected Option B and worked on the assignment locally, run the bash script in assignment3 by executing bash collectSubmission. Note for Option B users.

Assignment 3

Please follow these installation instructions to install a and run pip install PyPDF2 to install b. Please submit a3.

Submit the PDF and the zip file to Gradescope. Note for Option A users.

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Remember to download a3.The Convolutional Neural Network in this example is classifying images live in your browser using Javascript, at about 10 milliseconds per image. It takes an input image and transforms it through a series of functions into class probabilities at the end.

The transformed representations in this visualization can be losely thought of as the activations of the neurons along the way. The parameters of this function are learned with backpropagation on a dataset of image, label pairs.

Its exact architecture is [conv-relu-conv-relu-pool]x3-fc-softmax, for a total of 17 layers and parameters. It uses 3x3 convolutions and 2x2 pooling regions. By the end of the class, you will know exactly what all these numbers mean. Course Description Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection.

This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset ImageNet. We will focus on teaching how to set up the problem of image recognition, the learning algorithms e. Much of the background and materials of this course will be drawn from the ImageNet Challenge. Teaching Assistants. Lecture: Tuesday, Thursday 12pmpm. Office Hours You can find a full list of times and locations on the calendar.

Assignment Details See the Assignment Page for more details on how to hand in your assignments. Prerequisites Proficiency in Python All class assignments will be in Python and use numpy we provide a tutorial here for those who aren't as familiar with Python. If you have a lot of programming experience but in a different language e.

CS231n Winter 2016: Lecture1: Introduction and Historical Context

College Calculus, Linear Algebra e. Basic Probability and Statistics e. CS or other stats course You should know basics of probabilities, gaussian distributions, mean, standard deviation, etc. Equivalent knowledge of CS Machine Learning We will be formulating cost functions, taking derivatives and performing optimization with gradient descent.

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FAQ What's the grading policy for Spring ? We are still deciding on the grading details, and we will be updating them soon.

Deep Learning Tabular Data with PyTorch

If you need an academic accommodation based on a disability, you should initiate the request with the Office of Accessible Education OAE. The OAE will evaluate the request, recommend accommodations, and prepare a letter for faculty.

pytorch cs231n

Students should contact the OAE as soon as possible and at any rate in advance of assignment deadlines, since timely notice is needed to coordinate accommodations. Please send your letters to csn-sprstaff lists.

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Out of courtesy, we would appreciate that you first email us or talk to the instructor after the first class you attend. I have a question about the class. What is the best way to reach the course staff?Several approaches for understanding and visualizing Convolutional Networks have been developed in the literature, partly as a response the common criticism that the learned features in a Neural Network are not interpretable.

In this section we briefly survey some of these approaches and related work. Layer Activations. The most straight-forward visualization technique is to show the activations of the network during the forward pass.

For ReLU networks, the activations usually start out looking relatively blobby and dense, but as the training progresses the activations usually become more sparse and localized. One dangerous pitfall that can be easily noticed with this visualization is that some activation maps may be all zero for many different inputs, which can indicate dead filters, and can be a symptom of high learning rates.

The second common strategy is to visualize the weights. These are usually most interpretable on the first CONV layer which is looking directly at the raw pixel data, but it is possible to also show the filter weights deeper in the network.

pytorch cs231n

The weights are useful to visualize because well-trained networks usually display nice and smooth filters without any noisy patterns.

Another visualization technique is to take a large dataset of images, feed them through the network and keep track of which images maximally activate some neuron. We can then visualize the images to get an understanding of what the neuron is looking for in its receptive field.

One such visualization among others is shown in Rich feature hierarchies for accurate object detection and semantic segmentation by Ross Girshick et al. One problem with this approach is that ReLU neurons do not necessarily have any semantic meaning by themselves. Rather, it is more appropriate to think of multiple ReLU neurons as the basis vectors of some space that represents in image patches.

In other words, the visualization is showing the patches at the edge of the cloud of representations, along the arbitrary axes that correspond to the filter weights. This can also be seen by the fact that neurons in a ConvNet operate linearly over the input space, so any arbitrary rotation of that space is a no-op.

This point was further argued in Intriguing properties of neural networks by Szegedy et al. ConvNets can be interpreted as gradually transforming the images into a representation in which the classes are separable by a linear classifier. We can get a rough idea about the topology of this space by embedding images into two dimensions so that their low-dimensional representation has approximately equal distances than their high-dimensional representation.

There are many embedding methods that have been developed with the intuition of embedding high-dimensional vectors in a low-dimensional space while preserving the pairwise distances of the points. Among these, t-SNE is one of the best-known methods that consistently produces visually-pleasing results. We can then plug these into t-SNE and get 2-dimensional vector for each image. The corresponding images can them be visualized in a grid:.

Suppose that a ConvNet classifies an image as a dog. One way of investigating which part of the image some classification prediction is coming from is by plotting the probability of the class of interest e.

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That is, we iterate over regions of the image, set a patch of the image to be all zero, and look at the probability of the class. We can visualize the probability as a 2-dimensional heat map. Visualizing and Understanding Convolutional Networks.

Do ConvNets Learn Correspondence? Visualizing the activations and first-layer weights Layer Activations. Every box shows an activation map corresponding to some filter. Notice that the activations are sparse most values are zero, in this visualization shown in black and mostly local.No, there will not be a midterm this quarter.

Instead, more emphasis will be placed on the project. No, there will not be a poster session this quarter. We are looking into other alternatives for a remote poster session though.

You can watch the lectures live by going to Canvas, and going to the Zoom tab. Recordings will also be posted after lecture onto Canvas. In rare cases you may receive an unofficial grade for example if you previously took the class Incomplete and are re-taking a component this quarter. If this applies to you make a private post on Piazza. That said, you can always consult a TA if you are unsure about any method or problem statement.

In general it is possible to combine your project for CS and another class, but with the following caveats:. We recommend teams of 3 students, while teams sizes of 1 or 2 are also acceptable.

The team size will be taken under consideration when evaluating the scope of the project in breadth and depth, meaning that a three-person team is expected to accomplish more than a one-person team would. The reason we encourage students to form teams of 3 is that, in our experience, this size usually fits best the expectations for the CS projects. In particular, we expect the team to submit a completed project even for team of 1 or 2so keep in mind that all projects require to spend a decent minimum effort towards gathering data, and setting up the infrastructure to reach some form of result.

In a three-person team this can be shared much better, allowing the team to focus a lot more on the interesting stuff, e. In exceptional cases, we can allow a team of 4 people. If you plan to work on a project in a team of 4, please come talk to one of the TAs beforehand so we can ensure that the project has a large enough scope.

The easiest option is to install the Anaconda Python environment manager. This is a tool that allows you to set up multiple Python environments with different packages. Anaconda is compatible with Mac, Windows, and Linux. Here is how to install Tensorflow easily. For PyTorch, follow the above step and also run the command: conda install pytorch torchvision -c pytorch. You can check out previous projects on the projects page of the site.

For a more holistic understanding of machine learning ML is more than deep learning! If you want to go back to the very core mathematical foundations that underpin the history of ML, then take CS Normal office hours should generally be attended if you would like some help on the homework assignments.

These are informal meetings where you can talk about your ideas, concerns, or interests related to the project. You should reach out to your project mentor. You could either sign-up for on of their open office hours or contact them directly to set up an appointment. Try to have as much debugging information available such as error messages, stack-traces, and documentation.

Not officially, but a great resource is The Deep Learning book. See the question above. You might have to learn some core concepts there on your own such as doc2vec or auto-encoders by checking out research papers and other types of content blog posts, courses, videos, etc.

Please ask for help, the TAs often have great content to direct you to. To get access to the course materials including the Coursera private sessionsyou have to enroll in the class or be a Research Scientist, Visiting Scholar, Postdoctoral student, faculty or staff and have a SUNet ID.

Frequently Asked Questions. Instructors Andrew Ng Instructor. Kian Katanforoosh Instructor. What is the grading breakdown?