7:00 – 8:00 Research
- Finished formats and determine requirements for journals. Here’s the blog entry with all the information
8:30 – 4:00 BRC
- CS231n: Convolutional Neural Networks for Visual Recognition (Stanford)
- Assignments on Github This is basically the textbook, going from Python Tutorial through deep NNs
- So this is going to seem very newbie, but I’ve finally figured out how to populate a dictionary of arrays:
import numpy as np dict = {'doc1':[], 'doc2':[], 'doc3':[]} for doc in dict: dict[doc] = np.random.rand(5) for doc in dict: print("{0}: {1}".format(doc, dict[doc]))- It turns out that you HAVE to set the array relationship when the key is defined. Here’s how you do it programmatically
import numpy as np dict = {} for i in range(5): name = 'doc_{0}'.format(i) dict[name] = np.random.rand(5) for doc in dict: print("{0}: {1}".format(doc, dict[doc])) - Which gives the following results
doc_0: [ 0.53396248 0.10014123 0.40849079 0.76243954 0.29396581] doc_2: [ 0.21438903 0.68745032 0.1640486 0.51779412 0.05844617] doc_1: [ 0.36181216 0.78839326 0.90174006 0.29013203 0.76752794] doc_3: [ 0.44230569 0.63054045 0.80872794 0.83048027 0.87243106] doc_4: [ 0.08283319 0.72717925 0.29242797 0.90089588 0.34012144]
- It turns out that you HAVE to set the array relationship when the key is defined. Here’s how you do it programmatically
- Continuing to walk through fully_connected.py along with the tutorial
- math_ops.py – TF doc looks very handy
- gen_nn_ops.py – TF doc looks like the rest of the coed we’ll need
- ReLU. The Rectified Linear Unit has become very popular in the last few years. It computes the function f(x)=max(0,x)”>f(x)=max(0,x)f(x)=max(0,x). In other words, the activation is simply thresholded at zero (see image above on the left). There are several pros and cons to using the ReLUs: (Def from here)
- Discovered the Large-Scale Linear Model tutorial. It looks similar-ish to clustering. These are some of the features in tf.contrib.learn, which is also the home of the kmeans clusterer
-
Feature columns and transformations
Much of the work of designing a linear model consists of transforming raw data into suitable input features. tf.learn uses the
FeatureColumnabstraction to enable these transformations.A
FeatureColumnrepresents a single feature in your data. AFeatureColumnmay represent a quantity like ‘height’, or it may represent a category like ‘eye_color’ where the value is drawn from a set of discrete possibilities like {‘blue’, ‘brown’, ‘green’}.In the case of both continuous features like ‘height’ and categorical features like ‘eye_color’, a single value in the data might get transformed into a sequence of numbers before it is input into the model. The
FeatureColumnabstraction lets you manipulate the feature as a single semantic unit in spite of this fact. You can specify transformations and select features to include without dealing with specific indices in the tensors you feed into the model. - WOOHOO! Found what I was looking for!
- The input function must return a dictionary of tensors. Each key corresponds to the name of a
FeatureColumn. Each key’s value is a tensor containing the values of that feature for all data instances. See Building Input Functions with tf.contrib.learn for a more comprehensive look at input functions, andinput_fnin the linear models tutorial code for an example implementation of an input function. - So, working with that assumption, here’s a dictionary of tensors.
import numpy as np import tensorflow as tf; sess = tf.Session() dict = {} for i in range(5): name = 'doc_{0}'.format(i) var = tf.Variable(np.random.rand(5), tf.float32) dict[name] = var init = tf.global_variables_initializer() sess.run(init) print("{0}".format(sess.run(dict)).replace("]),", "])\n")) - Which, remarkably enough, runs and produces the following!
{'doc_2': array([ 0.17515295, 0.93597391, 0.38829954, 0.49664442, 0.07601639]) 'doc_0': array([ 0.40410072, 0.24565424, 0.9089159 , 0.02825472, 0.28945943]) 'doc_1': array([ 0.060302 , 0.58108026, 0.21500697, 0.40784728, 0.89955796]) 'doc_4': array([ 0.42359652, 0.0212912 , 0.38216499, 0.5089103 , 0.5616441 ]) 'doc_3': array([ 0.41851737, 0.76488499, 0.63983758, 0.17332712, 0.07856653])}
- The input function must return a dictionary of tensors. Each key corresponds to the name of a
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viztales 
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