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兩層FC層做分類：MNIST

在本教程中，我們來實現一個非常簡單的兩層全連線網路來完成MNIST資料的分類問題。
輸入[-1,28*28], FC1 有 1024 個neurons， FC2 有 10 個neurons。這麼簡單的一個全連線網路，結果測試準確率達到了 0.98。還是非常棒的！！！

import numpy as np
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)

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
 

print 'training data shape ', mnist.train.images.shape
print 'training label shape ', mnist.train.labels.shape

training data shape  (55000, 784)
training label shape  (55000, 10)

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)

step 200, training acc 0.937364
step 400, training acc 0.965818
step 600, training acc 0.973364
step 800, training acc 0.977709
step 1000, training acc 0.981528
= = = = = = = = = =  step 1000, testing acc 0.9688
step 1200, training acc 0.988437
step 1400, training acc 0.988728
step 1600, training acc 0.987491
step 1800, training acc 0.993873
step 2000, training acc 0.992527
= = = = = = = = = =  step 2000, testing acc 0.9789
step 2200, training acc 0.995309
step 2400, training acc 0.995455
step 2600, training acc 0.9952
step 2800, training acc 0.996073
step 3000, training acc 0.9964
= = = = = = = = = =  step 3000, testing acc 0.9778
step 3200, training acc 0.996709
step 3400, training acc 0.998109
step 3600, training acc 0.997455
step 3800, training acc 0.995055
step 4000, training acc 0.997291
= = = = = = = = = =  step 4000, testing acc 0.9808
step 4200, training acc 0.997746
step 4400, training acc 0.996073
step 4600, training acc 0.998564
step 4800, training acc 0.997946
step 5000, training acc 0.998673
= = = = = = = = = =  step 5000, testing acc 0.98