【机器学习】AlexNet 的tensorflow 实现

AlexNet 的tensorflow 实现

# 输入数据  import input_data  mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)    import tensorflow as tf    # 定义网络超参数  learning_rate = 0.001  training_iters = 200000  batch_size = 64  display_step = 20    # 定义网络参数  n_input = 784 # 输入的维度  n_classes = 10 # 标签的维度  dropout = 0.8 # Dropout 的概率    # 占位符输入  x = tf.placeholder(tf.types.float32, [None, n_input])  y = tf.placeholder(tf.types.float32, [None, n_classes])  keep_prob = tf.placeholder(tf.types.float32)    # 卷积操作  def conv2d(name, l_input, w, b):      return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(l_input, w, strides=[1, 1, 1, 1], padding='SAME'),b), name=name)    # 最大下采样操作  def max_pool(name, l_input, k):      return tf.nn.max_pool(l_input, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME', name=name)    # 归一化操作  def norm(name, l_input, lsize=4):      return tf.nn.lrn(l_input, lsize, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name=name)    # 定义整个网络   def alex_net(_X, _weights, _biases, _dropout):      # 向量转为矩阵      _X = tf.reshape(_X, shape=[-1, 28, 28, 1])        # 卷积层      conv1 = conv2d('conv1', _X, _weights['wc1'], _biases['bc1'])      # 下采样层      pool1 = max_pool('pool1', conv1, k=2)      # 归一化层      norm1 = norm('norm1', pool1, lsize=4)      # Dropout      norm1 = tf.nn.dropout(norm1, _dropout)        # 卷积      conv2 = conv2d('conv2', norm1, _weights['wc2'], _biases['bc2'])      # 下采样      pool2 = max_pool('pool2', conv2, k=2)      # 归一化      norm2 = norm('norm2', pool2, lsize=4)      # Dropout      norm2 = tf.nn.dropout(norm2, _dropout)        # 卷积      conv3 = conv2d('conv3', norm2, _weights['wc3'], _biases['bc3'])      # 下采样      pool3 = max_pool('pool3', conv3, k=2)      # 归一化      norm3 = norm('norm3', pool3, lsize=4)      # Dropout      norm3 = tf.nn.dropout(norm3, _dropout)        # 全连接层，先把特征图转为向量      dense1 = tf.reshape(norm3, [-1, _weights['wd1'].get_shape().as_list()[0]])       dense1 = tf.nn.relu(tf.matmul(dense1, _weights['wd1']) + _biases['bd1'], name='fc1')       # 全连接层      dense2 = tf.nn.relu(tf.matmul(dense1, _weights['wd2']) + _biases['bd2'], name='fc2') # Relu activation        # 网络输出层      out = tf.matmul(dense2, _weights['out']) + _biases['out']      return out    # 存储所有的网络参数  weights = {      'wc1': tf.Variable(tf.random_normal([3, 3, 1, 64])),      'wc2': tf.Variable(tf.random_normal([3, 3, 64, 128])),      'wc3': tf.Variable(tf.random_normal([3, 3, 128, 256])),      'wd1': tf.Variable(tf.random_normal([4*4*256, 1024])),      'wd2': tf.Variable(tf.random_normal([1024, 1024])),      'out': tf.Variable(tf.random_normal([1024, 10]))  }  biases = {      'bc1': tf.Variable(tf.random_normal([64])),      'bc2': tf.Variable(tf.random_normal([128])),      'bc3': tf.Variable(tf.random_normal([256])),      'bd1': tf.Variable(tf.random_normal([1024])),      'bd2': tf.Variable(tf.random_normal([1024])),      'out': tf.Variable(tf.random_normal([n_classes]))  }    # 构建模型  pred = alex_net(x, weights, biases, keep_prob)    # 定义损失函数和学习步骤  cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))  optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)    # 测试网络  correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))  accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))    # 初始化所有的共享变量  init = tf.initialize_all_variables()    # 开启一个训练  with tf.Session() as sess:      sess.run(init)      step = 1      # Keep training until reach max iterations      while step * batch_size < training_iters:          batch_xs, batch_ys = mnist.train.next_batch(batch_size)          # 获取批数据          sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout})          if step % display_step == 0:              # 计算精度              acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})              # 计算损失值              loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})              print "Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc)          step += 1      print "Optimization Finished!"      # 计算测试精度      print "Testing Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.})

参考：[https://github.com/aymericdamien/TensorFlow-Examples/tree/master/examples/3%20-%20Neural%20Networks]