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