Tensorflow读取csv文件（转）

常用的直接读取方法实例：
#加载包

import tensorflow as tf

import os

#设置工作目录

os.chdir("你自己的目录")

#查看目录

print(os.getcwd())

#读取函数定义

def read_data(file_queue):

    reader = tf.TextLineReader(skip_header_lines=1)

    key, value = reader.read(file_queue)

    #定义列

    defaults = [[0], [0.], [0.], [0.], [0.], ['']]

 #编码   Id,SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species = tf.decode_csv(value, defaults)

    #处理

    preprocess_op = tf.case({

        tf.equal(Species, tf.constant('Iris-setosa')): lambda: tf.constant(0),

        tf.equal(Species, tf.constant('Iris-versicolor')): lambda: tf.constant(1),

        tf.equal(Species, tf.constant('Iris-virginica')): lambda: tf.constant(2),

    }, lambda: tf.constant(-1), exclusive=True)

    #栈

    return tf.stack([SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm]), preprocess_op

def create_pipeline(filename, batch_size, num_epochs=None):

    file_queue = tf.train.string_input_producer([filename], num_epochs=num_epochs)

    example, label = read_data(file_queue)

    min_after_dequeue = 1000

    capacity = min_after_dequeue + batch_size

    example_batch, label_batch = tf.train.shuffle_batch(

        [example, label], batch_size=batch_size, capacity=capacity,

        min_after_dequeue=min_after_dequeue

    )

    return example_batch, label_batch

x_train_batch, y_train_batch = create_pipeline('Iris-train.csv', 50, num_epochs=1000)

x_test, y_test = create_pipeline('Iris-test.csv', 60)

print(x_train_batch,y_train_batch)

结果：
Tensor(“shuffle_batch_2:0”, shape=(50, 4), dtype=float32) Tensor(“shuffle_batch_2:1”, shape=(50,), dtype=int32)

从它的数据维度可知，数据已经读入。

一个完整的例子见github：https://github.com/zhangdm/machine-learning-summary/tree/master/tensorflow/tensorflow_iris_nn