[MDT-01] 必要な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(20160703)
tf.set_random_seed(20160703)
[MDT-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)
[MDT-03] 畳込みフィルターが1層のCNNを表現するクラスを定義します。
In [3]:
class SingleCNN:
def __init__(self, num_filters, num_units):
with tf.Graph().as_default():
self.prepare_model(num_filters, num_units)
self.prepare_session()
def prepare_model(self, num_filters, num_units):
num_units1 = 14*14*num_filters
num_units2 = num_units
with tf.name_scope('input'):
x = tf.placeholder(tf.float32, [None, 784], name='input')
x_image = tf.reshape(x, [-1,28,28,1])
with tf.name_scope('convolution'):
W_conv = tf.Variable(
tf.truncated_normal([5,5,1,num_filters], stddev=0.1),
name='conv-filter')
h_conv = tf.nn.conv2d(
x_image, W_conv, strides=[1,1,1,1], padding='SAME',
name='filter-output')
with tf.name_scope('pooling'):
h_pool =tf.nn.max_pool(h_conv, ksize=[1,2,2,1],
strides=[1,2,2,1], padding='SAME',
name='max-pool')
h_pool_flat = tf.reshape(h_pool, [-1, 14*14*num_filters],
name='pool-output')
with tf.name_scope('fully-connected'):
w2 = tf.Variable(tf.truncated_normal([num_units1, num_units2]))
b2 = tf.Variable(tf.zeros([num_units2]))
hidden2 = tf.nn.relu(tf.matmul(h_pool_flat, w2) + b2,
name='fc-output')
with tf.name_scope('softmax'):
w0 = tf.Variable(tf.zeros([num_units2, 10]))
b0 = tf.Variable(tf.zeros([10]))
p = tf.nn.softmax(tf.matmul(hidden2, w0) + b0,
name='softmax-output')
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(0.0005).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("convolution_filters", W_conv)
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_df_logs", sess.graph)
self.sess = sess
self.summary = summary
self.writer = writer
[MDT-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_df_logs
[MDT-05] パラメーターの最適化を4000回繰り返します。テストセットに対して約98%の正解率が得られます。
In [5]:
cnn = SingleCNN(16, 1024)
i = 0
for _ in range(4000):
i += 1
batch_xs, batch_ts = mnist.train.next_batch(100)
cnn.sess.run(cnn.train_step, feed_dict={cnn.x:batch_xs, cnn.t:batch_ts})
if i % 50 == 0:
summary, loss_val, acc_val = cnn.sess.run(
[cnn.summary, cnn.loss, cnn.accuracy],
feed_dict={cnn.x:mnist.test.images, cnn.t:mnist.test.labels})
print ('Step: %d, Loss: %f, Accuracy: %f'
% (i, loss_val, acc_val))
cnn.writer.add_summary(summary, i)