import tensorflow as tf

设置按需使用GPU

config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.InteractiveSession(config=config)
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

权值初始化

def weight_variable(shape):
# 用正态分布来初始化权值
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)

def bias_variable(shape):
# 本例中用relu激活函数，所以用一个很小的正偏置较好
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)

input_layer

X_ = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])

FC1

W_fc1 = weight_variable([784, 1024])
b_fc1 = bias_variable([1024])
h_fc1 = tf.nn.relu(tf.matmul(X_, W_fc1) + b_fc1)

FC2

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_pre = tf.nn.softmax(tf.matmul(h_fc1, W_fc2) + b_fc2)

1.损失函数：cross_entropy

cross_entropy = -tf.reduce_sum(y_ * tf.log(y_pre))

2.优化函数：AdamOptimizer, 优化速度要比 GradientOptimizer 快很多

train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)

3.预测结果评估

#　预测值中最大值（１）即分类结果，是否等于原始标签中的（１）的位置。argmax()取最大值所在的下标
correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

开始运行

sess.run(tf.global_variables_initializer())

这大概迭代了不到 10 个 epoch， 训练准确率已经达到了0.98

for i in range(5000):
X_batch, y_batch = mnist.train.next_batch(batch_size=100)
train_step.run(feed_dict={X_: X_batch, y_: y_batch})#一次用100个数据进行训练
if (i+1) % 200 == 0:
train_accuracy = accuracy.eval(feed_dict={X_: mnist.train.images, y_: mnist.train.labels})
print ("step %d, training acc %g" % (i+1, train_accuracy))
if (i+1) % 1000 == 0:
test_accuracy = accuracy.eval(feed_dict={X_: mnist.test.images, y_: mnist.test.labels})
print ("= " * 10, "step %d, testing acc %g" % (i+1, test_accuracy))