基于Tensorflow的英文评论二分类CNN模型

基于Tensorflow的英文评论二分类模型

---

前言

经过机器学习生成的模型,可以判断英语的肯定或否定含义,减轻了人的工作量,使得对大量意见进行归集,判断成为可能
==>源代码Github下载

---

导读

你可以逐行阅读源码和我的注释,然后运行感兴趣的Python文件,就可以让模型工作起来

源码列表

性质	内容	详细信息
源码	Python文件4篇	data_helpers.py
-	-	train.py
-	-	eval.py
-	-	text_cnn.py
数据集	训练测试数据	rt-polarity.neg
-	-	rt-polarity.pos

data_helpers.py 源码及分析

import numpy as np
import re
import itertools
from collections import Counter


def clean_str(string):
    #清理数据替换掉无词义的符号,这些符号,无法向量化
    """
    Tokenization/string cleaning for all datasets except for SST.
    Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py
    """
    string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
    string = re.sub(r"\'s", " \'s", string)
    string = re.sub(r"\'ve", " \'ve", string)
    string = re.sub(r"n\'t", " n\'t", string)
    string = re.sub(r"\'re", " \'re", string)
    string = re.sub(r"\'d", " \'d", string)
    string = re.sub(r"\'ll", " \'ll", string)
    string = re.sub(r",", " , ", string)
    string = re.sub(r"!", " ! ", string)
    string = re.sub(r"\(", " \( ", string)
    string = re.sub(r"\)", " \) ", string)
    string = re.sub(r"\?", " \? ", string)
    string = re.sub(r"\s{2,}", " ", string)
    return string.strip().lower()


def load_data_and_labels(positive_data_file, negative_data_file):
    # 将 词 和 标签,组成一个向量,维度是词,深度是2 positive和negative
    # 正样本语料库词标签为[0,1]
    # 负样本语料库词标签为[1,0]
    """
    Loads MR polarity data from files, splits the data into words and generates labels.
    Returns split sentences and labels.
    """
    # Load data from files
    positive_examples = list(open(positive_data_file, "r").readlines())
    positive_examples = [s.strip() for s in positive_examples]
    negative_examples = list(open(negative_data_file, "r").readlines())
    negative_examples = [s.strip() for s in negative_examples]
    # Split by words
    x_text = positive_examples + negative_examples
    x_text = [clean_str(sent) for sent in x_text]
    # Generate labels
    positive_labels = [[0, 1] for _ in positive_examples]
    negative_labels = [[1, 0] for _ in negative_examples]
    y = np.concatenate([positive_labels, negative_labels], 0)
    return [x_text, y]


def batch_iter(data, batch_size, num_epochs, shuffle=True):
    # 主要功能
    # 1 选择每次迭代,是否洗数据,像洗牌意义
    # 2 用生成器,每次只输出shuffled_data[start_index:end_index]这么多
    """
    Generates a batch iterator for a dataset.
    """
    data = np.array(data)
    data_size = len(data)
    num_batches_per_epoch = int((len(data)-1)/batch_size) + 1
    for epoch in range(num_epochs):
        # Shuffle the data at each epoch
        if shuffle:
            shuffle_indices = np.random.permutation(np.arange(data_size))
            shuffled_data = data[shuffle_indices]
        else:
            shuffled_data = data
        for batch_num in range(num_batches_per_epoch):
            start_index = batch_num * batch_size
            end_index = min((batch_num + 1) * batch_size, data_size)
            yield shuffled_data[start_index:end_index]

train.py 源码及分析

#! /usr/bin/env python

import tensorflow as tf
import numpy as np
import os
import time
import datetime
import data_helpers
from text_cnn import TextCNN
from tensorflow.contrib import learn

# Parameters
# ==================================================
# 语料文件路径定义
# Data loading params
tf.flags.DEFINE_float("dev_sample_percentage", .1, "Percentage of the training data to use for validation")
tf.flags.DEFINE_string("positive_data_file", "./data/rt-polaritydata/rt-polarity.pos", "Data source for the positive data.")
tf.flags.DEFINE_string("negative_data_file", "./data/rt-polaritydata/rt-polarity.neg", "Data source for the negative data.")

# 模型关键参数定义,卷积想象成这样
#[1] [0] [0]
#[0] [1] [0]
#[0] [0] [1] 
# 如上,类推3/4/5 ==>filter_sizes 
# Model Hyperparameters
tf.flags.DEFINE_integer("embedding_dim", 128, "Dimensionality of character embedding (default: 128)")
tf.flags.DEFINE_string("filter_sizes", "3,4,5", "Comma-separated filter sizes (default: '3,4,5')")
tf.flags.DEFINE_integer("num_filters", 128, "Number of filters per filter size (default: 128)")
tf.flags.DEFINE_float("dropout_keep_prob", 0.5, "Dropout keep probability (default: 0.5)")
tf.flags.DEFINE_float("l2_reg_lambda", 0.0, "L2 regularization lambda (default: 0.0)")

# Training parameters
#每个数据集64个数据,就是一批数据
tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
#200个数据集
tf.flags.DEFINE_integer("num_epochs", 200, "Number of training epochs (default: 200)")
#每100个,测试一个数据集(batch)
tf.flags.DEFINE_integer("evaluate_every", 100, "Evaluate model on dev set after this many steps (default: 100)")
tf.flags.DEFINE_integer("checkpoint_every", 100, "Save model after this many steps (default: 100)")
tf.flags.DEFINE_integer("num_checkpoints", 5, "Number of checkpoints to store (default: 5)")
# Misc Parameters
tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")

FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
    print("{}={}".format(attr.upper(), value))
print("")


# Data Preparation
# ==================================================

# Load data
print("Loading data...")
x_text, y = data_helpers.load_data_and_labels(FLAGS.positive_data_file, FLAGS.negative_data_file)

# Build vocabulary
max_document_length = max([len(x.split(" ")) for x in x_text])
vocab_processor = learn.preprocessing.VocabularyProcessor(max_document_length)
x = np.array(list(vocab_processor.fit_transform(x_text)))

# Randomly shuffle data
# 打散数据,seed(10),生成固定随机数 0.57140259469
np.random.seed(10)
# np.arange 生成随机序列
shuffle_indices = np.random.permutation(np.arange(len(y)))
x_shuffled = x[shuffle_indices]
y_shuffled = y[shuffle_indices]

# Split train/test set
# TODO: This is very crude, should use cross-validation
# 直接把dev_sample_index前用于训练,dev_sample_index后的用于训练;
# 交叉验证更好,从测试结果看,准确率差不多;

dev_sample_index = -1 * int(FLAGS.dev_sample_percentage * float(len(y)))
x_train, x_dev = x_shuffled[:dev_sample_index], x_shuffled[dev_sample_index:]
y_train, y_dev = y_shuffled[:dev_sample_index], y_shuffled[dev_sample_index:]
print("Vocabulary Size: {:d}".format(len(vocab_processor.vocabulary_)))
print("Train/Dev split: {:d}/{:d}".format(len(y_train), len(y_dev)))

#前面都在准备数据
# Training
# ==================================================
#开始构造图
with tf.Graph().as_default():
    session_conf = tf.ConfigProto(
      allow_soft_placement=FLAGS.allow_soft_placement,
      log_device_placement=FLAGS.log_device_placement)
    sess = tf.Session(config=session_conf)
    with sess.as_default():
        #卷积池化网络导入
        cnn = TextCNN(
            sequence_length=x_train.shape[1],
            num_classes=y_train.shape[1],
            vocab_size=len(vocab_processor.vocabulary_),
            embedding_size=FLAGS.embedding_dim,
            filter_sizes=list(map(int, FLAGS.filter_sizes.split(","))),
            num_filters=FLAGS.num_filters,
            l2_reg_lambda=FLAGS.l2_reg_lambda)
            #l2_reg_lambda 中文'拉姆那',正则化参数,这里用0

        # Define Training procedure
        global_step = tf.Variable(0, name="global_step", trainable=False)
        # 目标函数小于时optimizer,训练结束
        optimizer = tf.train.AdamOptimizer(1e-3)
        # 导入cnn.loss求偏导的差(也就是结果的变化量),反过来就知道,上次的变化,对结果影响
        # 从而知道是否到全局最优解
        grads_and_vars = optimizer.compute_gradients(cnn.loss)
        # 填入训练序列,后面会步进迭代
        train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)

        # Keep track of gradient values and sparsity (optional)
        grad_summaries = []
        # 每一步都存参数,tensorboard可以看
        for g, v in grads_and_vars:
            if g is not None:
                grad_hist_summary = tf.histogram_summary("{}/grad/hist".format(v.name), g)
                sparsity_summary = tf.scalar_summary("{}/grad/sparsity".format(v.name), tf.nn.zero_fraction(g))
                grad_summaries.append(grad_hist_summary)
                grad_summaries.append(sparsity_summary)
        grad_summaries_merged = tf.merge_summary(grad_summaries)

        # Output directory for models and summaries
        timestamp = str(int(time.time()))
        # 文件名为时间

        out_dir = os.path.abspath(os.path.join(os.path.curdir, "runs", timestamp))
        print("Writing to {}\n".format(out_dir))

        # Summaries for loss and accuracy
        # 目标函数 和 准确率的参数保存
        loss_summary = tf.scalar_summary("loss", cnn.loss)
        acc_summary = tf.scalar_summary("accuracy", cnn.accuracy)

        # Train Summaries
        # 训练数据保存
        train_summary_op = tf.merge_summary([loss_summary, acc_summary, grad_summaries_merged])
        train_summary_dir = os.path.join(out_dir, "summaries", "train")
        train_summary_writer = tf.train.SummaryWriter(train_summary_dir, sess.graph)

        # Dev summaries
        # 测试数据保存
        dev_summary_op = tf.merge_summary([loss_summary, acc_summary])
        dev_summary_dir = os.path.join(out_dir, "summaries", "dev")
        dev_summary_writer = tf.train.SummaryWriter(dev_summary_dir, sess.graph)

        # Checkpoint directory. Tensorflow assumes this directory already exists so we need to create it
        checkpoint_dir = os.path.abspath(os.path.join(out_dir, "checkpoints"))
        checkpoint_prefix = os.path.join(checkpoint_dir, "model")
        if not os.path.exists(checkpoint_dir):
            os.makedirs(checkpoint_dir)
        saver = tf.train.Saver(tf.global_variables(), max_to_keep=FLAGS.num_checkpoints)

        # Write vocabulary
        vocab_processor.save(os.path.join(out_dir, "vocab"))

        # Initialize all variables
        sess.run(tf.global_variables_initializer())

        def train_step(x_batch, y_batch):
            # 训练每一步,填入数据
            """
            A single training step
            """
            feed_dict = {
              cnn.input_x: x_batch,
              cnn.input_y: y_batch,
              cnn.dropout_keep_prob: FLAGS.dropout_keep_prob
            }
            _, step, summaries, loss, accuracy = sess.run(
                [train_op, global_step, train_summary_op, cnn.loss, cnn.accuracy],
                feed_dict)
            time_str = datetime.datetime.now().isoformat()
            print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
            train_summary_writer.add_summary(summaries, step)

        def dev_step(x_batch, y_batch, writer=None):
            # 测试每一步,填入数据
            """
            Evaluates model on a dev set
            """
            feed_dict = {
              cnn.input_x: x_batch,
              cnn.input_y: y_batch,
              cnn.dropout_keep_prob: 1.0
            }
            step, summaries, loss, accuracy = sess.run(
                [global_step, dev_summary_op, cnn.loss, cnn.accuracy],
                feed_dict)
            time_str = datetime.datetime.now().isoformat()
            print("{}: step {}, loss {:g}, acc {:g}".format(time_str, step, loss, accuracy))
            if writer:
                writer.add_summary(summaries, step)

        # Generate batches
        batches = data_helpers.batch_iter(
            list(zip(x_train, y_train)), FLAGS.batch_size, FLAGS.num_epochs)
        # Training loop. For each batch...
        for batch in batches:
            x_batch, y_batch = zip(*batch)
            train_step(x_batch, y_batch)
            current_step = tf.train.global_step(sess, global_step)
            if current_step % FLAGS.evaluate_every == 0:
                # 每隔100次,测试一次
                print("\nEvaluation:")
                dev_step(x_dev, y_dev, writer=dev_summary_writer)
                print("")
            if current_step % FLAGS.checkpoint_every == 0:
                # 每隔100次,存档一次
                path = saver.save(sess, checkpoint_prefix, global_step=current_step)
                print("Saved model checkpoint to {}\n".format(path))

eval.py 源码及分析

#! /usr/bin/env python

import tensorflow as tf
import numpy as np
import os
import time
import datetime
import data_helpers
from text_cnn import TextCNN
from tensorflow.contrib import learn
import csv

# Parameters
# ==================================================
#数据路径,注意,相对路径是相对当前Python环境的运行路径而言
# Data Parameters
tf.flags.DEFINE_string("positive_data_file", "./data/rt-polaritydata/rt-polarity.pos", "Data source for the positive data.")
tf.flags.DEFINE_string("negative_data_file", "./data/rt-polaritydata/rt-polarity.neg", "Data source for the positive data.")

# Eval Parameters
# batch_size=一批数据有多少  checkpoint_dir=存档路径 eval_train=开关是评价还是训练
tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)")
tf.flags.DEFINE_string("checkpoint_dir", "", "Checkpoint directory from training run")
tf.flags.DEFINE_boolean("eval_train", False, "Evaluate on all training data")

# Misc Parameters
# allow_soft_placement=软约束,就是运算设备不存在系统指定一个
# log_device_placement=记录设备信息,被指派另一个,要不要记录下来信息
tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices")


FLAGS = tf.flags.FLAGS
FLAGS._parse_flags()
print("\nParameters:")
for attr, value in sorted(FLAGS.__flags.items()):
    print("{}={}".format(attr.upper(), value))
print("")

# CHANGE THIS: Load data. Load your own data here
# eval_train上面开关设置了false走else,测试一条,一正一负,如果为真,走自己的测试样本
if FLAGS.eval_train:
    x_raw, y_test = data_helpers.load_data_and_labels(FLAGS.positive_data_file, FLAGS.negative_data_file)
    y_test = np.argmax(y_test, axis=1)
else:
    x_raw = ["a masterpiece four years in the making", "everything is off."]
    y_test = [1, 0]

# Map data into vocabulary
# 词向量存放路径并取出,词向量就是打开模型这台车的钥匙
vocab_path = os.path.join(FLAGS.checkpoint_dir, "..", "vocab")
vocab_processor = learn.preprocessing.VocabularyProcessor.restore(vocab_path)
# 测试语料,写入一个array,依次串行写入
x_test = np.array(list(vocab_processor.transform(x_raw)))

print("\nEvaluating...\n")

# Evaluation
# ==================================================
# 找到最新的时间点,应用模型
checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)
# 构造图,就是运算结构,构造好了,填入数据,连续迭代
graph = tf.Graph()
with graph.as_default():
    session_conf = tf.ConfigProto(
      allow_soft_placement=FLAGS.allow_soft_placement,
      log_device_placement=FLAGS.log_device_placement)
    sess = tf.Session(config=session_conf)
    # 打开会话,准备传入构造好的graph,交给后台运算
    with sess.as_default():
        # Load the saved meta graph and restore variables
        saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file))
        saver.restore(sess, checkpoint_file)

        # Get the placeholders from the graph by name
        input_x = graph.get_operation_by_name("input_x").outputs[0]
        # input_y = graph.get_operation_by_name("input_y").outputs[0]
        dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0]

        # Tensors we want to evaluate
        predictions = graph.get_operation_by_name("output/predictions").outputs[0]

        # 以上分别为保存,输出 输入占位符 的设置

        # Generate batches for one epoch
        # 取batches,每FLAGS.batch_size个list(x_test),作一个bastch
        batches = data_helpers.batch_iter(list(x_test), FLAGS.batch_size, 1, shuffle=False)

        # Collect the predictions here
        # 判断输出,用来和标签比对,即知道正确率,见下
        all_predictions = []

        for x_test_batch in batches:
            batch_predictions = sess.run(predictions, {input_x: x_test_batch, dropout_keep_prob: 1.0})
            all_predictions = np.concatenate([all_predictions, batch_predictions])
            # 循环相连接,把所有的x_test_batch跑出来的结果x_test_batch,首尾相连
            # dropout_keep_prob=1.0 不损失

# Print accuracy if y_test is defined
if y_test is not None:
    correct_predictions = float(sum(all_predictions == y_test))
    print("Total number of test examples: {}".format(len(y_test)))
    print("Accuracy: {:g}".format(correct_predictions/float(len(y_test))))
    # y_test=所有的标签,correct_predictions=正确率求和,两者求商,为正确率

# Save the evaluation to a csv
# 准备数据,存入该路径下的prediction.csv文件
predictions_human_readable = np.column_stack((np.array(x_raw), all_predictions))
out_path = os.path.join(FLAGS.checkpoint_dir, "..", "prediction.csv")
print("Saving evaluation to {0}".format(out_path))
with open(out_path, 'w') as f:
    csv.writer(f).writerows(predictions_human_readable)
    #excel打开可见

text_cnn.py 源码及分析

import tensorflow as tf
import numpy as np

# 本类是CNN网络实现
class TextCNN(object):
    """
    A CNN for text classification.
    Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.
    """
    def __init__(
      # 参数,写记名字,可以理解部分意思;
      self, sequence_length, num_classes, vocab_size,
      embedding_size, filter_sizes, num_filters, l2_reg_lambda=0.0):

        # Placeholders for input, output and dropout
        # 数据入口
        # input_x 输入语料,待训练的内容,维度是sequence_length,"N个词构成的N维向量";
        # input_y 输入语料,待训练的内容标签,维度是num_classes,"正面 || 负面";
        # dropout_keep_prob dropout参数,防止过拟合,仅在训练时用;
        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)
        # 损失函数,就是训练的目标函数,初始化,训练的目的,就是偏导求解概率向量矩阵,趋近损失函数最小值;
        # 交叉熵
        l2_loss = tf.constant(0.0)

        # Embedding layer
        # 指定运算结构的运行位置在cpu非gpu,因为"embedding"无法运行在gpu.原因为深入理解
        # 通过tf.name_scope指定"embedding"
        with tf.device('/cpu:0'), tf.name_scope("embedding"):
            # 表达"词向量宽度 和 嵌入深度"的概率矩阵 W,在-1~1间,线性随机赋初值;
            # random_uniform 随机赋值
            W = tf.Variable(
                tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0),
                name="W")
            # embedding_lookup=以input_x为索引,提取出W向量中的第(m=input_x)个维度
            self.embedded_chars = tf.nn.embedding_lookup(W, self.input_x)
            # expand_dims=增加维度,-1=插入一列,embedded_chars作为一列,插入embedded_chars_expanded
            self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)

        # Create a convolution + maxpool layer for each filter size
        # pooled_outputs=输出
        pooled_outputs = []
        # filter_sizes卷积核尺寸,枚举后遍历;
        for i, filter_size in enumerate(filter_sizes):
            # 命名空间,conv-maxpool-%s,卷积池化空间,也是运算结构的第二层
            with tf.name_scope("conv-maxpool-%s" % filter_size):
                # Convolution Layer
                # 卷积滤波参数配置,不同的卷积核尺寸,提取不同的上下文特征,越宽=提取的上下文越长
                filter_shape = [filter_size, embedding_size, 1, num_filters]
                # 初始化W为方差0.1的正态分布
                # 注意embedding层的W概率矩阵初始化为线性
                # 原因在于,运算后反向传递的误差参数,符合正态分布,可以更好地趋近特征;
                # 理解:类似于两个霰弹枪对射,弹丸容易对撞而发现重合区域,步枪对射,很难发现;
                W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")
                # 补偿系数b赋常量
                b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name="b")
                # 开始卷积
                conv = tf.nn.conv2d(
                    # 运算数据
                    self.embedded_chars_expanded,
                    # 概率矩阵
                    W,
                    # 卷积核参数
                    strides=[1, 1, 1, 1],
                    padding="VALID",
                    name="conv")
                # Apply nonlinearity
                # 激活函数,用的Relu,让显著地越显著
                # 可以理解为,正面或者负面评价有一些标志词汇,这些词汇概率被增强\
                # 即一旦出现这些词汇,倾向性分类进正或负面评价,该激励函数可加快学习进度\
                # 增加稀疏性,因为让确定的事情更确定,噪声的影响就降到了最低,举一个例子\
                # 语料样本中含有多次出现的"否定"意义的词,则其他词语误导判断的机会下降
                h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")
                # Maxpooling over the outputs
                #继续池化
                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
        num_filters_total = num_filters * len(filter_sizes)
        self.h_pool = tf.concat(3, pooled_outputs)
        # 池化后的输出,维度为[hpool, num_filters_total]
        # 最开始扁平的数据,体积上变成了细长的数据,有更多的特征和联系
        # 由词的判定,引入上下文的判定,越多的特征,越多维度的概率矩阵,意味着越准确
        self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])

        # Add dropout
        with tf.name_scope("dropout"):
            # drop层,防止过拟合,参数之前给出dropout_keep_prob
            # 互联网级别的数据无需过拟合,也就是大数据,因为过拟合的本质是采样失真,噪声权重影响了判断\
            # 如果采样足够多,足够充分,噪声的影响可以被量化到趋近事实,也就无从过拟合\
            # 举一个例子,历史课上爱发言的孩子,给历史老师留下印象爱发言,这就容易造成过拟合.\
            # 如果班主任,天天观察孩子,记录下每一次发言的频度和持续时间,就能得出准确的结论,因为\
            # 观察偏差样本偏差这些噪声都无法影响海量的准确采样\
            # 结论:数据越大,drop和正则化就越不需要;
            self.h_drop = tf.nn.dropout(self.h_pool_flat, self.dropout_keep_prob)

        # Final (unnormalized) scores and predictions
        # 输出层
        with tf.name_scope("output"):
            # 提取出所有节点的w和b
            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")
            # 损失函数导入
            l2_loss += tf.nn.l2_loss(W)
            l2_loss += tf.nn.l2_loss(b)
            # 简单求和
            self.scores = tf.nn.xw_plus_b(self.h_drop, W, b, name="scores")
            # 归一化,把180:20的总得分,转化成90%和10%的概率
            self.predictions = tf.argmax(self.scores, 1, name="predictions")

        # CalculateMean cross-entropy loss
        # 目标/损失函数,用的最小交叉熵
        with tf.name_scope("loss"):
            losses = tf.nn.softmax_cross_entropy_with_logits(self.scores, self.input_y)
            self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss

        # Accuracy
        with tf.name_scope("accuracy"):
            # 准确率,简单求和算数平均值
            correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
            self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, "float"), name="accuracy")

---

输出表格文件

为方便打开,转成txt文件
link

---

打印输出

感谢您的耐心

我是秦海宁,我在长虹

欢迎和我交流

也欢迎加入我们

[email protected]