зеркало из
https://github.com/iharh/notes.git
synced 2025-11-04 07:36:08 +02:00
27 строки
885 B
Plaintext
27 строки
885 B
Plaintext
https://github.com/monsta-hd/boltzmann-machines
|
|
|
|
from tensorflow.examples.tutorials.mnist import input_data
|
|
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
|
|
|
|
28*28 = 784 pixels (55000 samples)
|
|
mnist.train
|
|
mnist.train.images
|
|
mnist.train.labels
|
|
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0] - one-hot vector for number 3
|
|
mnist.test
|
|
mnist.validate
|
|
|
|
import tensorflow as tf
|
|
|
|
x = tf.placeholder(tf.float32, [None, 784]) # use arbitrary number of 784-dim vectors for learning
|
|
|
|
W = tf.Variable(tf.zeros[784, 10]) # matrix for mult-n
|
|
b = tf.Variable(tf.zeros[10]) # bias vector (to add)
|
|
|
|
y = tf.nn.softmax(tf.matmul(x, W) + b)
|
|
|
|
y_ = tf.placeholder(tf.float32, [None, 10]) # stub-function
|
|
|
|
cross_entropy = tf.reduce_mean(
|
|
-tf.reduce_sum(y_ * tf.log(y), reduction_idices=[1]))
|