#硬體配置

*本人用的是 lenovo小新銳7000筆記本，cpu是intel -core -i5-7300Q 四核，記憶體4G，機械硬碟320G，雙顯示卡，整合intel® HD Graphics630 獨顯 GeForce GTX 1050，這塊顯示卡位寬128bit, 視訊記憶體2G,有640個cuda核心。 ##系統環境： win7下，安裝Anaconda3, 之後安裝Python3.6 , Cuda9.0,cudnn9.0 tensorflow1.14.0, Pycharm專業版 ，訓練該資料集用了兩層的卷積神經網路，訓練2萬步，在不開啟GPU加速的情況下用時59分06秒;切換到GPU虛擬環境下用時5分12秒。看來沒有GPU，實踐深度學習確實有難度呀。 附程式碼如下： *

//import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

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')

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                          strides=[1, 2, 2, 1], padding='SAME')

if __name__ == '__main__':
    # 讀入資料
    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
    # x為訓練影象的佔位符、y_為訓練影象標籤的佔位符
    x = tf.placeholder(tf.float32, [None, 784])
    y_ = tf.placeholder(tf.float32, [None, 10])

    # 將單張圖片從784維向量重新還原為28x28的矩陣圖片
    x_image = tf.reshape(x, [-1, 28, 28, 1])

    # 第一層卷積層
    W_conv1 = weight_variable([5, 5, 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)

    # 第二層卷積層
    W_conv2 = weight_variable([5, 5, 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)

    # 全連線層，輸出為1024維的向量
    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)
    # 使用Dropout，keep_prob是一個佔位符，訓練時為0.5，測試時為1
    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    # 把1024維的向量轉換成10維，對應10個類別
    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])
    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2

    # 我們不採用先Softmax再計算交叉熵的方法，而是直接用tf.nn.softmax_cross_entropy_with_logits直接計算
    cross_entropy = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
    # 同樣定義train_step
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

    # 定義測試的準確率
    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    # 建立Session和變數初始化
    sess = tf.InteractiveSession()
    sess.run(tf.global_variables_initializer())

    # 訓練20000步
    for i in range(20000):
        batch = mnist.train.next_batch(50)
        # 每100步報告一次在驗證集上的準確度
        if i % 100 == 0:
            train_accuracy = accuracy.eval(feed_dict={
                x: batch[0], y_: batch[1], keep_prob: 1.0})
            print("step %d, training accuracy %g" % (i, train_accuracy))
        train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

    # 訓練結束後報告在測試集上的準確度
    print("test accuracy %g" % accuracy.eval(feed_dict={
        x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))