神经网络实现（多分类logistic回归模型实现）

二分类 logistic

 2018/11/13   Share

改动

数据集输入
图模型

数据集输入

class CifarData:
    def __init__(self,filenames,need_shuffle):
        all_data = []
        all_labels = []
        for filename in filenames:
            data,labels = load_data(filename)
            all_data.append(data)
            all_labels.append(labels)
        self._data = np.vstack(all_data)
        self._data = self._data / 127.5 -1
        self._labels = np.hstack(all_labels)
        print(self._data.shape)
        print(self._labels.shape)
        
        self._num_examples = self._data.shape[0]
        self._need_shuffle = need_shuffle
        self._indicator = 0
        if self._need_shuffle:
            self._shuffle_data()
    
    def _shuffle_data(self):
        # [0,1,2,3,4,5] - > [5,3,2,4,0,1]
        p = np.random.permutation(self._num_examples)
        self._data = self._data[p]
        self._labels = self._labels[p]
        
    def next_batch(self, batch_size):
        """return batch_size examples as a batch."""
        end_indicator = self._indicator + batch_size
        if end_indicator > self._num_examples:
            if self._need_shuffle:
                self._shuffle_data()
                self._indicator = 0
                end_indicator = batch_size
            else:
                raise Exception("have no more examples")
        if end_indicator > self._num_examples:
            raise Exception("batch size is larger than all examples")
        batch_data = self._data[self._indicator:end_indicator]
        batch_labels = self._labels[self._indicator: end_indicator]
        self._indicator = end_indicator
        return batch_data, batch_labels

图模型

tf.reset_default_graph()

# [None,3072]
x = tf.placeholder(tf.float32,[None,3072])

# [None], eg:[0,5,6,3]
y = tf.placeholder(tf.int64,[None])

# [3072,10]
w = tf.get_variable('w',[x.get_shape()[-1],10],initializer=tf.random_normal_initializer(0,1))

# (10, )
b = tf.get_variable('b',[10],initializer=tf.constant_initializer(0.0))

# [None,3072]*[3072,10] = [None,10]
y_ = tf.matmul(x,w)+b

# mean square loss
"""
# api:e^x / sum(e^x)
# [[0.01, 0.9, ..., 0.03, []]
p_y = tf.nn.softmax(y_)
# 5 - > [0,0,0,0,0,1,0,0,0,0]
y_one_hot = tf.one_hot(y,10,dtype=tf.float32)
loss = tf.reduce_mean(tf.square(y_one_hot - p_y))
"""
loss = tf.losses.sparse_softmax_cross_entropy(labels=y,logits=y_)
# y_ -> softmax
# y -> one_hot
# loss = ylogy_

# indices
predict = tf.argmax(y_,1)
# [1,0,1,1,1,0]
correct_prediction = tf.equal(predict,y)
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float64))

with tf.name_scope('train_op'):
    train_op = tf.train.AdamOptimizer(1e-3).minimize(loss)

总结

机器学习与深度学习认知
神经网络

神经元、二分类logistic回归、多分类logistic回归

神经网络的Tensorflow实现

Tensorflow基础

神经网络的Tensorflow实现

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CATALOG

1. 改动
1. 1.1. 数据集输入
2. 1.2. 图模型
2. 总结



缺失模块。
1、请确保node版本大于6.2
2、在博客根目录（注意不是archer根目录）执行以下命令：
npm i hexo-generator-json-content --save
3、在根目录_config.yml里添加配置：

jsonContent:
  meta: false
  pages: false
  posts:
    title: true
    date: true
    path: true
    text: false
    raw: false
    content: false
    slug: false
    updated: false
    comments: false
    link: false
    permalink: false
    excerpt: false
    categories: true
    tags: true