Tensorflow实现AlexNet人脸检测代码

下面是一个使用Tensorflow实现的AlexNet人脸检测代码示例：

import tensorflow as tf
import numpy as np
import cv2

# 定义AlexNet网络结构
def alexnet(x, weights, biases, keep_prob):
    conv1 = tf.nn.conv2d(x, weights['wc1'], strides=[1, 4, 4, 1], padding='SAME')
    conv1 = tf.nn.bias_add(conv1, biases['bc1'])
    conv1 = tf.nn.relu(conv1)
    pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')
    norm1 = tf.nn.lrn(pool1, depth_radius=5, bias=2.0, alpha=1e-3, beta=0.75)

    conv2 = tf.nn.conv2d(norm1, weights['wc2'], strides=[1, 1, 1, 1], padding='SAME')
    conv2 = tf.nn.bias_add(conv2, biases['bc2'])
    conv2 = tf.nn.relu(conv2)
    pool2 = tf.nn.max_pool(conv2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')
    norm2 = tf.nn.lrn(pool2, depth_radius=5, bias=2.0, alpha=1e-3, beta=0.75)

    conv3 = tf.nn.conv2d(norm2, weights['wc3'], strides=[1, 1, 1, 1], padding='SAME')
    conv3 = tf.nn.bias_add(conv3, biases['bc3'])
    conv3 = tf.nn.relu(conv3)

    conv4 = tf.nn.conv2d(conv3, weights['wc4'], strides=[1, 1, 1, 1], padding='SAME')
    conv4 = tf.nn.bias_add(conv4, biases['bc4'])
    conv4 = tf.nn.relu(conv4)

    conv5 = tf.nn.conv2d(conv4, weights['wc5'], strides=[1, 1, 1, 1], padding='SAME')
    conv5 = tf.nn.bias_add(conv5, biases['bc5'])
    conv5 = tf.nn.relu(conv5)
    pool5 = tf.nn.max_pool(conv5, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')

    fc6 = tf.reshape(pool5, [-1, weights['wd1'].get_shape().as_list()[0]])
    fc6 = tf.add(tf.matmul(fc6, weights['wd1']), biases['bd1'])
    fc6 = tf.nn.relu(fc6)
    fc6 = tf.nn.dropout(fc6, keep_prob)

    fc7 = tf.add(tf.matmul(fc6, weights['wd2']), biases['bd2'])
    fc7 = tf.nn.relu(fc7)
    fc7 = tf.nn.dropout(fc7, keep_prob)

    out = tf.add(tf.matmul(fc7, weights['out']), biases['out'])
    return out

# 加载训练好的参数
weights = {
    'wc1': tf.Variable(tf.random_normal([11, 11, 3, 96])),
    'wc2': tf.Variable(tf.random_normal([5, 5, 96, 256])),
    'wc3': tf.Variable(tf.random_normal([3, 3, 256, 384])),
    'wc4': tf.Variable(tf.random_normal([3, 3, 384, 384])),
    'wc5': tf.Variable(tf.random_normal([3, 3, 384, 256])),
    'wd1': tf.Variable(tf.random_normal([6 * 6 * 256, 4096])),
    'wd2': tf.Variable(tf.random_normal([4096, 4096])),
    'out': tf.Variable(tf.random_normal([4096, 1]))
}
biases = {
    'bc1': tf.Variable(tf.random_normal([96])),
    'bc2': tf.Variable(tf.random_normal([256])),
    'bc3': tf.Variable(tf.random_normal([384])),
    'bc4': tf.Variable(tf.random_normal([384])),
    'bc5': tf.Variable(tf.random_normal([256])),
    'bd1': tf.Variable(tf.random_normal([4096])),
    'bd2': tf.Variable(tf.random_normal([4096])),
    'out': tf.Variable(tf.random_normal([1]))
}

# 加载测试图片
img = cv2.imread('<path_to_image>')
img = cv2.resize(img, (227, 227))
img = np.array(img, dtype=np.float32)
img = np.expand_dims(img, axis=0)

# 运行模型进行预测
x = tf.placeholder(tf.float32, [None, 227, 227, 3])
keep_prob = tf.placeholder(tf.float32)
pred = alexnet(x, weights, biases, keep_prob)
saver = tf.train.Saver()
with tf.Session() as sess:
    saver.restore(sess, '<path_to_model>')
    output = sess.run(pred, feed_dict={x: img, keep_prob: 1.})
    if output > 0:
        print('Face detected!')
    else:
        print('No face detected!')

需要注意的是，在上述代码中，需要预先训练好AlexNet模型，并将其保存为一个ckpt文件。加载模型时，需要指定ckpt文件的路径。