import tensorflow as tf
import numpy as np
import input_data

mnist = input_data.read_data_sets('data/', one_hot=True)
print("MNIST ready")

n_input  = 784 # 28*28的灰度圖，畫素個數784
n_output = 10  # 是10分類問題

# 權重項
weights = {
    # conv1,引數[3, 3, 1, 32]分別指定了filter的h、w、所連線輸入的維度、filter的個數即產生特徵圖個數
    'wc1': tf.Variable(tf.random_normal([3
 
, 3, 1, 32], stddev=0.1)),   
    # conv2，這裡引數3，3同上，32是當前連線的深度是32，即前面特徵圖的個數，64為輸出的特徵圖的個數
    'wc2': tf.Variable(tf.random_normal([3, 3, 32, 64], stddev=0.1)), 
    # fc1，將特徵圖轉換為向量，1024由自己定義
    'wd1': tf.Variable(tf.random_normal([7*7*64, 1024], stddev=0.1)), 
    # fc2，做10分類任務，前面連1024，輸出10分類
    'wd2': tf.Variable(tf.random_normal([1024
 
, n_output], stddev=0.1)) 
}
"""
特徵圖大小計算：
f_w = (w-f+2*pad)/s + 1 = (28-3+2*1)/1 + 1 = 28 # 說明經過卷積層並沒有改變圖片的大小
f_h = (h-f+2*pad)/s + 1 = (28-3+2*1)/1 + 1 = 28
# 特徵圖的大小是經過池化層後改變的
第一次pooling後28*28變為14*14
第二次pooling後14*14變為7*7，即最終是一個7*7*64的特徵圖

"""
# 偏置項
biases = {
    'bc1': tf.Variable(tf.random_normal([32
 
], stddev=0.1)),      # conv1，對應32個特徵圖
    'bc2': tf.Variable(tf.random_normal([64], stddev=0.1)),      # conv2，對應64個特徵圖
    'bd1': tf.Variable(tf.random_normal([1024], stddev=0.1)),    # fc1，對應1024個向量
    'bd2': tf.Variable(tf.random_normal([n_output], stddev=0.1)) # fc2，對應10個輸出
}

def conv_basic(_input, _w, _b, _keep_prob):
    # INPUT
    # 對影象做預處理，轉換為tf支援的格式，即[n, h, w, c],-1是確定好其它3維後，讓tf去推斷剩下的1維
    _input_r = tf.reshape(_input, shape=[-1, 28, 28, 1]) 

    # CONV LAYER 1
    _conv1 = tf.nn.conv2d(_input_r, _w['wc1'], strides=[1, 1, 1, 1], padding='SAME') 
    # [1, 1, 1, 1]分別代表batch_size、h、w、c的stride
    # padding有兩種選擇：'SAME'（視窗滑動時，畫素不夠會自動補0）或'VALID'（不夠就跳過）兩種選擇
    _conv1 = tf.nn.relu(tf.nn.bias_add(_conv1, _b['bc1'])) # 卷積層後連啟用函式
    # 最大值池化，[1, 2, 2, 1]其中1,1對應batch_size和channel，2,2對應2*2的池化
    _pool1 = tf.nn.max_pool(_conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
    # 隨機殺死一些神經元,_keepratio為保留神經元比例，如0.6 
    _pool_dr1 = tf.nn.dropout(_pool1, _keep_prob) 

    # CONV LAYER 2
    _conv2 = tf.nn.conv2d(_pool_dr1, _w['wc2'], strides=[1, 1, 1, 1], padding='SAME')
    _conv2 = tf.nn.relu(tf.nn.bias_add(_conv2, _b['bc2']))
    _pool2 = tf.nn.max_pool(_conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
    _pool_dr2 = tf.nn.dropout(_pool2, _keep_prob) # dropout

    # VECTORIZE向量化
    # 定義全連線層的輸入，把pool2的輸出做一個reshape，變為向量的形式
    _densel = tf.reshape(_pool_dr2, [-1, _w['wd1'].get_shape().as_list()[0]]) 

    # FULLY CONNECTED LAYER 1
    _fc1 = tf.nn.relu(tf.add(tf.matmul(_densel, _w['wd1']), _b['bd1'])) # w*x+b，再通過relu
    _fc_dr1 = tf.nn.dropout(_fc1, _keep_prob) # dropout

    # FULLY CONNECTED LAYER 2
    _out = tf.add(tf.matmul(_fc_dr1, _w['wd2']), _b['bd2']) # w*x+b，得到結果

    # RETURN
    out = {'input_r': _input_r, 'conv1': _conv1, 'pool1': _pool1, 'pool_dr1': _pool_dr1,
           'conv2': _conv2, 'pool2': _pool2, 'pool_dr2': _pool_dr2, 'densel': _densel,
           'fc1': _fc1, 'fc_dr1': _fc_dr1, 'out': _out
           }
    return out
print("CNN READY")


x = tf.placeholder(tf.float32, [None, n_input]) # 用placeholder先佔地方，樣本個數不確定為None
y = tf.placeholder(tf.float32, [None, n_output]) # 用placeholder先佔地方，樣本個數不確定為None
keep_prob = tf.placeholder(tf.float32)

_pred = conv_basic(x, weights, biases, keep_prob)['out'] # 前向傳播的預測值
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(_pred, y)) # 交叉熵損失函式
optm = tf.train.AdamOptimizer(0.001).minimize(cost) # 梯度下降優化器
_corr = tf.equal(tf.argmax(_pred, 1), tf.argmax(y, 1)) # 對比預測值索引和實際label索引，相同返回True，不同返回False
accr = tf.reduce_mean(tf.cast(_corr, tf.float32)) # 將True或False轉換為1或0,並對所有的判斷結果求均值

init = tf.global_variables_initializer()
print("FUNCTIONS READY")

# 上面神經網路結構定義好之後，下面定義一些超引數
training_epochs = 1000 # 所有樣本迭代1000次
batch_size = 100 # 每進行一次迭代選擇100個樣本
display_step = 1
# LAUNCH THE GRAPH
sess = tf.Session() # 定義一個Session
sess.run(init) # 在sess裡run一下初始化操作
# OPTIMIZE
for epoch in range(training_epochs):
    avg_cost = 0.
    total_batch = int(mnist.train.num_examples/batch_size)
    for i in range(total_batch):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size) # 逐個batch的去取資料
        sess.run(optm, feed_dict={x: batch_xs, y: batch_ys, keep_prob:0.5})
        avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob:1.0})/total_batch
    if epoch % display_step == 0:
        train_accuracy = sess.run(accr, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.0})
        test_accuracy = sess.run(accr, feed_dict={x: mnist.test.images, y: mnist.test.labels, keep_prob:1.0})
        print("Epoch: %03d/%03d cost: %.9f TRAIN ACCURACY: %.3f TEST ACCURACY: %.3f"
              % (epoch, training_epochs, avg_cost, train_accuracy, test_accuracy))
print("DONE")

我用的顯示卡是GTX960，在跑這個卷積神經網路的時候，第一次filter分別設的是64和128，結果報蜜汁錯誤了，反正就是我視訊記憶體不足，所以改成了32和64，讓特徵圖少一點。所以，是讓我換1080的意思嘍

I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:885] Found device 0 with properties: 
name: GeForce GTX 960
major: 5 minor: 2 memoryClockRate (GHz) 1.304
pciBusID 0000:01:00.0
Total memory: 4.00GiB
Free memory: 3.33GiB
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:906] DMA: 0 
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:916] 0:   Y 
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 960, pci bus id: 0000:01:00.0)
W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\bfc_allocator.cc:217] Ran out of memory trying to allocate 2.59GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\bfc_allocator.cc:217] Ran out of memory trying to allocate 1.34GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\bfc_allocator.cc:217] Ran out of memory trying to allocate 2.10GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
W c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\bfc_allocator.cc:217] Ran out of memory trying to allocate 3.90GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
Epoch: 000/1000 cost: 0.517761162 TRAIN ACCURACY: 0.970 TEST ACCURACY: 0.967
Epoch: 001/1000 cost: 0.093012387 TRAIN ACCURACY: 0.960 TEST ACCURACY: 0.979
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省略