有无GPU运行深度学习mnist数据集时间对比

#硬件配置

*本人用的是 lenovo小新锐7000笔记本，cpu是intel -core -i5-7300Q
四核，内存4G，机械硬盘320G，双显卡，集成intel® HD Graphics630
独显 GeForce GTX 1050，这块显卡位宽128bit, 显存2G,有640个cuda核心。
##系统环境：
win7下，安装Anaconda3, 之后安装Python3.6 , Cuda9.0,cudnn9.0 tensorflow1.14.0, Pycharm专业版 ，训练该数据集用了两层的卷积神经网络，训练2万步，在不开启GPU加速的情况下用时59分06秒;切换到GPU虚拟环境下用时5分12秒。看来没有GPU，实践深度学习确实有难度呀。
附代码如下：
*

//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__':
    # 读入数据
    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
    # x为训练图像的占位符、y_为训练图像标签的占位符
    x = tf.placeholder(tf.float32, [None, 784])
    y_ = tf.placeholder(tf.float32, [None, 10])

    # 将单张图片从784维向量重新还原为28x28的矩阵图片
    x_image = tf.reshape(x, [-1, 28, 28, 1])

    # 第一层卷积层
    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)

    # 第二层卷积层
    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)

    # 全连接层，输出为1024维的向量
    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是一个占位符，训练时为0.5，测试时为1
    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    # 把1024维的向量转换成10维，对应10个类别
    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])
    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2

    # 我们不采用先Softmax再计算交叉熵的方法，而是直接用tf.nn.softmax_cross_entropy_with_logits直接计算
    cross_entropy = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
    # 同样定义train_step
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

    # 定义测试的准确率
    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    # 创建Session和变量初始化
    sess = tf.InteractiveSession()
    sess.run(tf.global_variables_initializer())

    # 训练20000步
    for i in range(20000):
        batch = mnist.train.next_batch(50)
        # 每100步报告一次在验证集上的准确度
        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})

    # 训练结束后报告在测试集上的准确度
    print("test accuracy %g" % accuracy.eval(feed_dict={
        x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))