textcnn自己的理解

import tensorflow as tfimport numpy as npclass TextCNN(object):    """    A CNN for text classification.    Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.    """        # sequence_length-最长词汇数    # num_classes-分类数    # vocab_size-总词汇数    # embedding_size-词向量长度    # filter_sizes-卷积核尺寸3，4，5    # num_filters-卷积核数量    # l2_reg_lambda-l2正则化系数    def __init__(      self, sequence_length, num_classes, vocab_size,      embedding_size, filter_sizes, num_filters, l2_reg_lambda=0.0):        # Placeholders for input, output and dropout        self.input_x = tf.placeholder(tf.int32, [None, sequence_length], name="input_x")        self.input_y = tf.placeholder(tf.float32, [None, num_classes], name="input_y")        self.dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob")        # Keeping track of l2 regularization loss (optional)        l2_loss = tf.constant(0.0)        # Embedding layer        with tf.device('/cpu:0'), tf.name_scope("embedding"):            self.W = tf.Variable(                    #19758   128                tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0),                name="W")            #input_x  %len(w)              self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x)            #add one vector            # 添加一个维度，[batch_size, sequence_length, embedding_size, 1]            self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)        # Create a convolution + maxpool layer for each filter size        pooled_outputs = []        #3 4 5        for i, filter_size in enumerate(filter_sizes):            with tf.name_scope("conv-maxpool-%s" % filter_size):                # Convolution Layer   3,128,1,2                filter_shape = [filter_size, embedding_size, 1, num_filters]                #随机生成正太分布                W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")                #                #   2                b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name="b")                conv = tf.nn.conv2d(                    self.embedded_chars_expanded,                    W,                    strides=[1, 1, 1, 1],                    padding="VALID",                    name="conv")                # Apply nonlinearity                h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")                # Maxpooling over the outputs                # 56 -(3,4,5) + 1                pooled = tf.nn.max_pool(                    h,                    ksize=[1, sequence_length - filter_size + 1, 1, 1],                    strides=[1, 1, 1, 1],                    padding='VALID',                    name="pool")                pooled_outputs.append(pooled)        # Combine all the pooled features                #将pooled_outputs中的值全部取出来然后reshape成[len(input_x),num_filters*len(filters_size)]，然后进行了dropout层防止过拟合，        #最后再添加了一层全连接层与softmax层将特征映射成不同类别上的概率        #2 3   把池化层输出变成一维向量        num_filters_total = num_filters * len(filter_sizes)        self.h_pool = tf.concat(pooled_outputs, 3)        #    , 6        self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])        # Add dropout        with tf.name_scope("dropout"):            self.h_drop = tf.nn.dropout(self.h_pool_flat, self.dropout_keep_prob)        # Final (unnormalized) scores and predictions        with tf.name_scope("output"):            #6,2            W = tf.get_variable(                "W",                shape=[num_filters_total, num_classes],                initializer=tf.contrib.layers.xavier_initializer())            b = tf.Variable(tf.constant(0.1, shape=[num_classes]), name="b")            #l2 way            l2_loss += tf.nn.l2_loss(W)            l2_loss += tf.nn.l2_loss(b)            #computes matmul(x, weights) + biases.            self.scores = tf.nn.xw_plus_b(self.h_drop, W, b, name="scores")            self.predictions = tf.argmax(self.scores, 1, name="predictions")        # CalculateMean cross-entropy loss        with tf.name_scope("loss"):            #Computes softmax cross entropy between `logits` and `labels`.            losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.scores, labels=self.input_y)            #计算张量的尺寸的元素平均值。            self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss        # Accuracy        with tf.name_scope("accuracy"):            correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))            self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, "float"), name="accuracy")

(一) flag：

分测试和验证集：0.1

文件路径

model:

embedding128

fileter 3,4,5

num_filters 128

dropout 0.5

L20.0

trainpaameters:

batch_size 64

num_epochs 200

evalute_every 100

checkpoint（保存模型用的）100

num_checkpoints number of checkpoints to save 5

misc parameters

soft_placement true

log_device false

加载数据。

建造词库。

一句话最长的句子 56

vocab_processor (相当于wordembedding)

打乱数据。

分测试和验证集

获得最长的 句子 56

y有两种结果

vocab_size 18758

设置global_step

optimizer

gradients去计算loss

设置变量去跟踪梯度。

把x,y合在一起，然后去分批

在分解。

给train模型

需要x,y,dropout

调用textcnn模型

x：1066,56

y: 1066 ,2

dropout: 0.5

input_x:

 w:19758,128

为什么embedded-chars：106656 128

W=[19758,128]属于-1,1之间。

Embedded_chars:1066 56 128

embedded_chars_expanded 1066 56 128 1

conv2d函数：

embedded_charss_expanded1066 56 128 1

w: 3,128,1,2

得到（ ，2）

传递调优和loss梯度，global，记录梯度，loss，准确率