import tensorflow as tf
import numpy as np
import input_data
mnist = input_data.read_data_sets('data/', one_hot=True)
print("MNIST ready")
sess = tf.InteractiveSession()
# 定義好初始化函數(shù)以便重復(fù)使用。給權(quán)重制造一些隨機(jī)噪聲來打破完全對(duì)稱，使用截?cái)嗟恼龖B(tài)分布，標(biāo)準(zhǔn)差設(shè)為0.1，
# 同時(shí)因?yàn)槭褂胷elu，也給偏執(zhí)增加一些小的正值(0.1)用來避免死亡節(jié)點(diǎn)(dead neurons)
def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') # 參數(shù)分別指定了卷積核的尺寸、多少個(gè)channel、filter的個(gè)數(shù)即產(chǎn)生特征圖的個(gè)數(shù)

# 2x2最大池化，即將一個(gè)2x2的像素塊降為1x1的像素。最大池化會(huì)保留原始像素塊中灰度值最高的那一個(gè)像素，即保留最顯著的特征。
def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

n_input  = 784 # 28*28的灰度圖，像素個(gè)數(shù)784
n_output = 10  # 是10分類問題

# 在設(shè)計(jì)網(wǎng)絡(luò)結(jié)構(gòu)前，先定義輸入的placeholder，x是特征，y是真實(shí)的label
x = tf.placeholder(tf.float32, [None, n_input]) 
y = tf.placeholder(tf.float32, [None, n_output]) 
x_image = tf.reshape(x, [-1, 28, 28, 1]) # 對(duì)圖像做預(yù)處理，將1D的輸入向量轉(zhuǎn)為2D的圖片結(jié)構(gòu)，即1*784到28*28的結(jié)構(gòu),-1代表樣本數(shù)量不固定，1代表顏色通道數(shù)量

# 定義第一個(gè)卷積層，使用前面寫好的函數(shù)進(jìn)行參數(shù)初始化，包括weight和bias
W_conv1 = weight_variable([3, 3, 1, 32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

# 定義第二個(gè)卷積層
W_conv2 = weight_variable([3, 3, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

# fc1，將兩次池化后的7*7共128個(gè)特征圖轉(zhuǎn)換為1D向量，隱含節(jié)點(diǎn)1024由自己定義
W_fc1 = weight_variable([7*7*64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

# 為了減輕過擬合，使用Dropout層
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

# Dropout層輸出連接一個(gè)Softmax層,得到最后的概率輸出
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
pred = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) #前向傳播的預(yù)測(cè)值，
print("CNN READY")

# 定義損失函數(shù)為交叉熵?fù)p失函數(shù)
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=[1]))
# 優(yōu)化器
optm = tf.train.AdamOptimizer(0.001).minimize(cost)
# 定義評(píng)測(cè)準(zhǔn)確率的操作
corr = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) # 對(duì)比預(yù)測(cè)值的索引和真實(shí)label的索引是否一樣，一樣返回True，不一樣返回False
accuracy = tf.reduce_mean(tf.cast(corr, tf.float32))
# 初始化所有參數(shù)
tf.global_variables_initializer().run()
print("FUNCTIONS READY")

training_epochs = 1000 # 所有樣本迭代1000次
batch_size = 100 # 每進(jìn)行一次迭代選擇100個(gè)樣本
display_step = 1
for i in range(training_epochs):
    avg_cost = 0.
    total_batch = int(mnist.train.num_examples/batch_size)
    batch = mnist.train.next_batch(batch_size)
    optm.run(feed_dict={x:batch[0], y:batch[1], keep_prob:0.7})
    avg_cost += sess.run(cost, feed_dict={x:batch[0], y:batch[1], keep_prob:1.0})/total_batch
    if i % display_step ==0: # 每10次訓(xùn)練，對(duì)準(zhǔn)確率進(jìn)行一次測(cè)試
        train_accuracy = accuracy.eval(feed_dict={x:batch[0], y:batch[1], keep_prob:1.0})
        test_accuracy = accuracy.eval(feed_dict={x:mnist.test.images, y:mnist.test.labels, keep_prob:1.0})
        print("step: %d  cost: %.9f  TRAIN ACCURACY: %.3f  TEST ACCURACY: %.3f" % (i, avg_cost, train_accuracy, test_accuracy))
print("DONE")