[MST-01] モジュールをインポートして、乱数のシードを設定します。
In [1]:
%matplotlib inline
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
np.random.seed(20160612)
tf.set_random_seed(20160612)
[MST-02] MNISTのデータセットを用意します。
In [2]:
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
[MST-03] 単層ニューラルネットワークを表現するクラスを定義します。
In [3]:
class SingleLayerNetwork:
def __init__(self, num_units):
with tf.Graph().as_default():
self.prepare_model(num_units)
self.prepare_session()
def prepare_model(self, num_units):
with tf.name_scope('input'):
x = tf.placeholder(tf.float32, [None, 784], name='input')
with tf.name_scope('hidden'):
w1 = tf.Variable(tf.truncated_normal([784, num_units]),
name='weights')
b1 = tf.Variable(tf.zeros([num_units]), name='biases')
hidden1 = tf.nn.relu(tf.matmul(x, w1) + b1, name='hidden1')
with tf.name_scope('output'):
w0 = tf.Variable(tf.zeros([num_units, 10]), name='weights')
b0 = tf.Variable(tf.zeros([10]), name='biases')
p = tf.nn.softmax(tf.matmul(hidden1, w0) + b0, name='softmax')
with tf.name_scope('optimizer'):
t = tf.placeholder(tf.float32, [None, 10], name='labels')
loss = -tf.reduce_sum(t * tf.log(p), name='loss')
train_step = tf.train.AdamOptimizer().minimize(loss)
with tf.name_scope('evaluator'):
correct_prediction = tf.equal(tf.argmax(p, 1), tf.argmax(t, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,
tf.float32), name='accuracy')
tf.summary.scalar("loss", loss)
tf.summary.scalar("accuracy", accuracy)
tf.summary.histogram("weights_hidden", w1)
tf.summary.histogram("biases_hidden", b1)
tf.summary.histogram("weights_output", w0)
tf.summary.histogram("biases_output", b0)
self.x, self.t, self.p = x, t, p
self.train_step = train_step
self.loss = loss
self.accuracy = accuracy
def prepare_session(self):
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
summary = tf.summary.merge_all()
writer = tf.summary.FileWriter("/tmp/mnist_sl_logs", sess.graph)
self.sess = sess
self.summary = summary
self.writer = writer
[MST-04] TensorBoard用のデータ出力ディレクトリーを削除して初期化しておきます。
In [4]:
!rm -rf /tmp/mnist_sl_logs
[MST-05] パラメーターの最適化を2000回繰り返します。テストセットに対して、約97%の正解率が得られます。
In [5]:
nn = SingleLayerNetwork(1024)
i = 0
for _ in range(2000):
i += 1
batch_xs, batch_ts = mnist.train.next_batch(100)
nn.sess.run(nn.train_step, feed_dict={nn.x: batch_xs, nn.t: batch_ts})
if i % 100 == 0:
summary, loss_val, acc_val = nn.sess.run(
[nn.summary, nn.loss, nn.accuracy],
feed_dict={nn.x:mnist.test.images, nn.t: mnist.test.labels})
print ('Step: %d, Loss: %f, Accuracy: %f'
% (i, loss_val, acc_val))
nn.writer.add_summary(summary, i)