import tensorflow as tf

# 設定按需使用GPU
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.InteractiveSession(config=config)
# 1. 匯入資料    用tensorflow 匯入資料
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
print
 
('training data shape ', mnist.train.images.shape)
print('training label shape ', mnist.train.labels.shape)
#2. 構建網路
# 權值初始化
def weight_variable(shape):
# 用正態分佈來初始化權值
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
# 本例中用relu啟用函式，所以用一個很小的正偏置較好
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)
# 3. 訓練和評估
# 1.損失函式：cross_entropy
cross_entropy = -tf.reduce_sum(y_ * tf.log(y_pre))
# 2.優化函式：AdamOptimizer, 優化速度要比 GradientOptimizer 快很多
train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)
# 3.預測結果評估
#　預測值中最大值（１）即分類結果，是否等於原始標籤中的（１）的位置。argmax()取最大值所在的下標
correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.arg_max(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# 開始執行
sess.run(tf.global_variables_initializer())
# 這大概迭代了不到 10 個 epoch， 訓練準確率已經達到了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})
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))