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