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tensor flow tutorial

tensor flowにて、tutorialを実施

以下のプログラミングで、実行すると、

認識率84%の結果が出てきた。次のStepにも行きたい。

 

fujimoto@fujimoto-VirtualBox:~$ sudo python ./test2
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
0.8446
fujimoto@fujimoto-VirtualBox:~$

 

 

$ cat test2
import tensorflow.examples.tutorials.mnist.input_data
import tensorflow.examples.tutorials.mnist.input_data as input_data


import tensorflow as tf

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
sess = tf.InteractiveSession()

x = tf.placeholder("float", shape=[None, 784])
y_ = tf.placeholder("float", shape=[None, 10])

W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))

sess.run(tf.initialize_all_variables())
y = tf.nn.softmax(tf.matmul(x,W) + b)
cross_entropy = -tf.reduce_sum(y_*tf.log(y))

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

for i in range(30):
batch = mnist.train.next_batch(50)
train_step.run(feed_dict={x: batch[0], y_: batch[1]})

correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
print accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels})

fujimoto@fujimoto-VirtualBox:~$