Skip to content

Instantly share code, notes, and snippets.

Embed
What would you like to do?
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
# Mnistに使うデータセットをインポートする
mnist = input_data.read_data_sets('/tmp/tensorflow/mnist/input_data',
one_hot=True)
# セッションの作成と初期化
sess = tf.InteractiveSession()
# test_layerスコープ
with tf.name_scope('test_layer'):
x = tf.placeholder(tf.float32, [None, 784], name='x') # 入力するPlaceholder
W = tf.Variable(tf.zeros([784, 10]), name='W') # 重み
b = tf.Variable(tf.zeros([10]), name='b') # バイアス
y = tf.matmul(x, W) + b # 内積計算とバイアスの加算
y_ = tf.placeholder(tf.float32, [None, 10], name='y_') # 正解
# optimizerスコープ
with tf.name_scope('optimizer'):
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
# evaluatorスコープ
with tf.name_scope('evaluator'):
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# Tensorboardのサマリスコープ
with tf.name_scope('summary'):
writer = tf.summary.FileWriter("./tensorboard_log", sess.graph) # ログを残すフォルダの指定とセッショングラフを可視化
tf.summary.scalar('cross_entropy', cross_entropy) # cross_entropyを可視化
tf.summary.scalar('accuracy', accuracy) # accuracyを可視化
merged = tf.summary.merge_all()
# 学習部(1000回学習)
tf.global_variables_initializer().run() # 重みの初期化
for step in range(1000):
batch_xs, batch_ys = mnist.train.next_batch(100)
sess.run(train_step, feed_dict={x: batch_xs,y_: batch_ys})
summary_str = sess.run(merged, feed_dict={x: batch_xs,y_: batch_ys})
if step % 100 == 0:
writer.add_summary(summary_str, step)
print(sess.run(accuracy, feed_dict={x: mnist.test.images,
y_: mnist.test.labels}))
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment