tensorflow mnist 2

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF


mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
x = tf.placeholder("float", [None, 784])
y_ = tf.placeholder("float", [None,10])

W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x,W) + b)

#cross_entropy = -tf.reduce_sum(y_*tf.log(y), reduction_indices=[1])
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

config = tf.ConfigProto()
config.gpu_options.allow_growth = True  # 不全部佔滿顯存, 按需分配
config.gpu_options.per_process_gpu_memory_fraction = 0.6  #限制GPU內存佔用率

train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
init = tf.initialize_all_variables()
sess = tf.Session(config=config)
KTF.set_session(sess)  # 設置session
sess.run(init)

for i in range(1000):
	batch_xs, batch_ys = mnist.train.next_batch(100)
	sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
	if(i % 100 == 0):
		print ("epoch:", i, ",accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))