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--- cs501r_f2016:lab6v2 [2017/10/10 16:10]
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-====Objective:====
-To build a dense prediction model, to experiment with different
-topologies, and to experiment with different regularization techniques.
-----
-====Deliverable:====
-For this lab, you will turn in a report that describes your efforts at
-creating a tensorflow radiologist.  Your final deliverable is either a
-notebook or PDF writeup that describes your (1) topology, (2) cost
-function, (3) method of calculating accuracy, and (4) results with
-experimenting with regularization.
-Your notebook / writeup should also include at an image that
-shows the dense prediction produced by your network on the
-"pos_test_000072.png" image.  This is an image in the test set that
-your network will not have seen before.
-----
-====Grading standards:====
-Your notebook will be graded on the following:
-  * 30% Proper design, creation and debugging of a dense prediction network
-  * 30% Proper design of a loss function and test set accuracy measure
-  * 20% Proper experimentation with regularization
-  * 20% Tidy visualization of the output of your dense predictor
-----
-====Data set:====
-The data is given as a set of 1024x1024 PNG images.  Each input image
-(in the "inputs" directory) is an RGB image of a section of tissue,
-and there a file with the same name (in the "outputs" directory) that
-has a dense labeling of whether or not a section of tissue is
-cancerous (white pixels mean "cancerous", while black pixels mean "not
-cancerous").
-The data has been pre-split for you into test and training splits.
-Filenames also reflect whether or not the image has any cancer at all
-(files starting with "pos_" have some cancerous pixels, while files
-starting with "neg_" have no cancer anywhere).  All of the data is
-hand-labeled, so the dataset is not very large.  That means that
-overfitting is a real possibility.
-[[http://liftothers.org/cancer_data.tar.gz|The data can be downloaded here.]]
-----
-====Description:====
-For this lab, you will implement a virtual radiologist.  You are given
-images of possibly cancerous tissue samples, and you must build a
-detector that identifies where in the tissue cancer may reside.
-For this lab, there are two primary tasks:
-**Part 1: Implement a dense predictor**
-In previous labs and lectures, we have talked about DNNs that classify an
-entire image as a single class.  Here, however, we are interested in a
-more nuanced classification: given an input image, we would like to
-identify each **pixel** that is possibly cancerous.  That means that
-instead of a single output, your network should output an "image", where
-each output pixel of your network represents the probability that a
-pixel is cancerous.
-**Part 1a: Implement your network topology**
-Like the previous lab, you must choose your topology.  I have had good
-luck implementing the "Deep Convolution U-Net" from this paper:
-[[https://arxiv.org/pdf/1505.04597.pdf|U-Net: Convolutional Networks
-for Biomedical Image Segmentation]]
-(See figure 1).  This should be fairly easy to implement given the
-"conv" and "fc" helper functions that you implemented previously; you
-may also need the tensorflow function "tf.concat".
-**Part 1b: Implement a cost function**
-You should still use cross-entropy as your cost function, but you may
-need to think hard about how exactly to set this up -- your network
-should output cancer/not-cancer probabilities for each pixel, which
-can be viewed as a two-class classification problem.
-**Part 2: Test two regularizers**
-To help combat overfitting, you should implement some sort of
-regularization.  This can be either dropout, or L1 / L2
-regularization.
-First, you must construct a baseline performance measure without
-regularization.  This is straightforward: you must simply test
-classification accuracy on the held-out test set, and compare it to
-the accuracy of the training set.  The difference is the
-"generalization error."
-You must experiment with two different regularizers.  For each, train
-your network, and then report on the following questions:
-  - What is the final classification accuracy on the test set before and after regularization?
-  - Is the generalization error better or worse?
-  - If you used dropout, what dropout probability did you use?
-  - If you used L1/L2, how did you pick lambda?
-----
-====Hints:====
-You are welcome to resize your input images, although don't make them
-so small that the essential details are blurred!  I resized my images
-down to 512x512.
-I used the ``scikit-image`` package to handle all of my image IO and
-resizing.  **NOTE: be careful about data types!** When you first load
-an image using ``skimage.io.imread``, it returns a tensor with uint8
-pixels in the range of [0,255].  However, after using
-``skimage.transform.resize``, the result is an image with float32
-entries in [0,1].
-You are welcome (and encouraged) to use the built-in tensorflow
-dropout layer.

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