用cnn做行人分类

爱被打了一巴掌 2022-07-24 04:13 138阅读 0赞

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机器学习数据库是关键，自己搜搜吧，规模太小训练不出来，正样本和负样本。

训练之前要处理训练文件，这个我在之前的[python图像操作][python]这篇博文里写过，并有完整代码。

也可以用我处理好的数据，稍后我会上传

input\_data.py

"""Functions for downloading and reading MNIST data."""
    from __future__ import print_function
    import gzip
    import os
    import numpy
    
    
    def extract_images(filename):
      """Extract the images into a 4D uint8 numpy array [index, y, x, depth]."""
      print('Extracting', filename)
      rows = 128
      cols = 64
      data = numpy.fromfile(filename, dtype=numpy.uint8)
      data = data.reshape(-1, rows, cols, 1)
      #print numpy.shape(data)
      return data
    
    
    def dense_to_one_hot(labels_dense, num_classes=2):
      """Convert class labels from scalars to one-hot vectors."""
      num_labels = labels_dense.shape[0]
      index_offset = numpy.arange(num_labels) * num_classes
      labels_one_hot = numpy.zeros((num_labels, num_classes))
      labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1
      return labels_one_hot
    
    
    def extract_labels(filename, one_hot=False):
      """Extract the labels into a 1D uint8 numpy array [index]."""
      print('Extracting', filename)
      labels = numpy.fromfile(filename, dtype=numpy.uint8)
      if one_hot:
         return dense_to_one_hot(labels)
      return labels
    
    
    class DataSet(object):
    
      def __init__(self, images, labels, fake_data=False):
        if fake_data:
          self._num_examples = 10000
        else:
          assert images.shape[0] == labels.shape[0], (
              "images.shape: %s labels.shape: %s" % (images.shape,
                                                     labels.shape))
          self._num_examples = images.shape[0]
    
          # Convert shape from [num examples, rows, columns, depth]
          # to [num examples, rows*columns] (assuming depth == 1)
          assert images.shape[3] == 1
          images = images.reshape(images.shape[0],
                                  images.shape[1] * images.shape[2])
          # Convert from [0, 255] -> [0.0, 1.0].
          images = images.astype(numpy.float32)
          images = numpy.multiply(images, 1.0 / 255.0)
        self._images = images
        self._labels = labels
        self._epochs_completed = 0
        self._index_in_epoch = 0
    
      @property
      def images(self):
        return self._images
    
      @property
      def labels(self):
        return self._labels
    
      @property
      def num_examples(self):
        return self._num_examples
    
      @property
      def epochs_completed(self):
        return self._epochs_completed
    
      def next_batch(self, batch_size, fake_data=False):
        """Return the next `batch_size` examples from this data set."""
        if fake_data:
          fake_image = [1.0 for _ in xrange(784)]
          fake_label = 0
          return [fake_image for _ in xrange(batch_size)], [
              fake_label for _ in xrange(batch_size)]
        start = self._index_in_epoch
        self._index_in_epoch += batch_size
        if self._index_in_epoch > self._num_examples:
          # Finished epoch
          self._epochs_completed += 1
          # Shuffle the data
          perm = numpy.arange(self._num_examples)
          numpy.random.shuffle(perm)
          self._images = self._images[perm]
          self._labels = self._labels[perm]
          # Start next epoch
          start = 0
          self._index_in_epoch = batch_size
          assert batch_size <= self._num_examples
        end = self._index_in_epoch
        return self._images[start:end], self._labels[start:end]
    
    
    def read_data_sets(train_dir, fake_data=False, one_hot=False):
      class DataSets(object):
        pass
      data_sets = DataSets()
    
      if fake_data:
        data_sets.train = DataSet([], [], fake_data=True)
        data_sets.validation = DataSet([], [], fake_data=True)
        data_sets.test = DataSet([], [], fake_data=True)
        return data_sets
    
      TRAIN_IMAGES = 'train_data.bin'
      TRAIN_LABELS = 'train_label.bin'
      TEST_IMAGES = 'test_data.bin'
      TEST_LABELS = 'test_label.bin'
      VALIDATION_SIZE = 500
    
      local_file =os.path.join(train_dir, TRAIN_IMAGES)
      train_images = extract_images(local_file)
      
      local_file =os.path.join(train_dir, TRAIN_LABELS)
      train_labels = extract_labels(local_file, one_hot=one_hot)
    
      local_file = os.path.join(train_dir, TEST_IMAGES)
      test_images = extract_images(local_file)
      
      local_file =os.path.join(train_dir, TEST_LABELS)
      test_labels = extract_labels(local_file, one_hot=one_hot)
    
      validation_images = train_images[:VALIDATION_SIZE]
      validation_labels = train_labels[:VALIDATION_SIZE]
      train_images = train_images[VALIDATION_SIZE:]
      train_labels = train_labels[VALIDATION_SIZE:]
    
      data_sets.train = DataSet(train_images, train_labels)
      data_sets.validation = DataSet(validation_images, validation_labels)
      data_sets.test = DataSet(test_images, test_labels)
    
      return data_sets

conv\_net.py

import input_data
    mnist = input_data.read_data_sets('dataset', one_hot=True)
    import tensorflow as tf
    
    # Parameters
    learning_rate = 0.001
    training_iters = 100000
    batch_size = 128
    display_step = 10
    
    # Network Parameters
    n_input = 128*64 #  data input (img shape: 128*64)
    n_classes = 2 # total classes (0-1)
    dropout = 0.50 # Dropout, probability to keep units
    
    # tf Graph input
    x = tf.placeholder(tf.float32, [None, n_input])
    y = tf.placeholder(tf.float32, [None, n_classes])
    keep_prob = tf.placeholder(tf.float32) #dropout (keep probability)
    
    # Create model
    def conv2d(img, w, b):
        return tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(img, w, strides=[1, 1, 1, 1], padding='SAME'),b))
    
    def max_pool(img, k):
        return tf.nn.max_pool(img, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME')
    
    def conv_net(_X, _weights, _biases, _dropout):
        # Reshape input picture
        _X = tf.reshape(_X, shape=[-1, 128, 64, 1])
    
        # Convolution Layer
        conv1 = conv2d(_X, _weights['wc1'], _biases['bc1'])
        # Max Pooling (down-sampling)
        conv1 = max_pool(conv1, k=2)
        # Apply Dropout
        conv1 = tf.nn.dropout(conv1, _dropout)
    
        # Convolution Layer
        conv2 = conv2d(conv1, _weights['wc2'], _biases['bc2'])
        # Max Pooling (down-sampling)
        conv2 = max_pool(conv2, k=2)
        # Apply Dropout
        conv2 = tf.nn.dropout(conv2, _dropout)
    
        # Fully connected layer
        dense1 = tf.reshape(conv2, [-1, _weights['wd1'].get_shape().as_list()[0]]) # Reshape conv2 output to fit dense layer input
        dense1 = tf.nn.relu(tf.add(tf.matmul(dense1, _weights['wd1']), _biases['bd1'])) # Relu activation
        dense1 = tf.nn.dropout(dense1, _dropout) # Apply Dropout
    
        # Output, class prediction
        out = tf.add(tf.matmul(dense1, _weights['out']), _biases['out'])
        return out
    
    # Store layers weight & bias
    weights = {
        'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])), # 5x5 conv, 1 input, 32 outputs
        'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])), # 5x5 conv, 32 inputs, 64 outputs
        'wd1': tf.Variable(tf.random_normal([32*16*64, 1024])), # fully connected, 7*7*64 inputs, 1024 outputs
        'out': tf.Variable(tf.random_normal([1024, n_classes])) # 1024 inputs, 10 outputs (class prediction)
    }
    
    biases = {
        'bc1': tf.Variable(tf.random_normal([32])),
        'bc2': tf.Variable(tf.random_normal([64])),
        'bd1': tf.Variable(tf.random_normal([1024])),
        'out': tf.Variable(tf.random_normal([n_classes]))
    }
    
    # Construct model
    pred = conv_net(x, weights, biases, keep_prob)
    
    # Define loss and optimizer
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
    
    # Evaluate model
    correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
    
    # Initializing the variables
    init = tf.initialize_all_variables()
    
    # Launch the graph
    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)
            # Fit training using batch data
            sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout})
            if step % display_step == 0:
                # Calculate batch accuracy
                acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
                # Calculate batch loss
                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!"
        # Calculate accuracy for 256 mnist test images
        print "Testing Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.})

下面是训练结果

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[python]: http://blog.csdn.net/u014568921/article/details/51324448
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