It’s March and no new wars! Hooray!
7:00 – 8:00 Research
- Added an excerpt (the last half or so) from Home to Roost to the paper. Disturbingly appropriate.
- Continuing conference/journal spreadsheet. Here’s the sorted list:
- Next step is to find submission formats. The obvious targets are Phys Rev E and IEEE TAC. I’m tempted by the JofPP, since that was where the original group polarization paper was published. And I need to understand what a Nature letter is.
8:30 – 4:30 BRC
- More TensorFlow
- MNIST tutorial – clear, but a LOT of stuff
- Neural Networks and Deep Learning is an online book referenced in the TF documentation (at least the softmax chapter)
- A one-hot vector is a vector which is 0 in most dimensions, and 1 in a single dimension. In this case, the nth digit will be represented as a vector which is 1 in the nth dimension. For example, 3 would be [0,0,0,1,0,0,0,0,0,0]. Consequently, mnist.train.labels is a [55000, 10] array of floats.
- If you want to assign probabilities to an object being one of several different things, softmax is the thing to do, because softmax gives us a list of values between 0 and 1 that add up to 1. Even later on, when we train more sophisticated models, the final step will be a layer of softmax.
-
x = tf.placeholder(tf.float32, [None, 784])
We represent this as a 2-D tensor of floating-point numbers, with a shape [None, 784]. (Here None means that a dimension can be of any length.)
- A good explanation of cross-entropy, apparently.
- tf.reduce_mean
- Success!!! Here’s the code:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float32, [None, 10]) #note that y_ means 'y prme'
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
sess = tf.InteractiveSession()
tf.global_variables_initializer().run()
for _ in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
- And here are the results:
C:\Users\philip.feldman\AppData\Local\Programs\Python\Python35\python.exe C:/Development/Sandboxes/TensorflowPlayground/HelloPackage/MNIST_tutorial.py
Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
0.9192
- Working on the advanced tutorial. Fixed fully_connected_feed.py to work with local data.
- And then my brain died
viztales 