Tensorflow | 读取csv文件

#加载包
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
import numpy as np

# 数据集名称，数据集要放在你的工作目录下
IRIS_TRAINING = "iris_training.csv"
IRIS_TEST = "iris_test.csv"

# 数据集读取，训练集和测试集
training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
    filename=IRIS_TRAINING,
    target_dtype=np.int,
    features_dtype=np.float32)
test_set = tf.contrib.learn.datasets.base.load_csv_with_header(
    filename=IRIS_TEST,
    target_dtype=np.int,
    features_dtype=np.float32)

# 特征
feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]

# 构建DNN网络，3层，每层分别为10,20,10个节点
classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
                                            hidden_units=[10, 20, 10],
                                            n_classes=3,
                                            model_dir="/tmp/iris_model")

# 拟合模型，迭代2000步
classifier.fit(x=training_set.data,
               y=training_set.target,
               steps=2000)

# 计算精度
accuracy_score = classifier.evaluate(x=test_set.data,y=test_set.target)["accuracy"]

print('Accuracy: {0:f}'.format(accuracy_score))

# 预测新样本的类别
new_samples = np.array([[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]], dtype=float)
y = list(classifier.predict(new_samples, as_iterable=True))
print('Predictions: {}'.format(str(y)))

training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
    filename=IRIS_TRAINING,
    target_dtype=np.int,
    features_dtype=np.float32)
test_set = tf.contrib.learn.datasets.base.load_csv_with_header(
    filename=IRIS_TEST,
    target_dtype=np.int,
    features_dtype=np.float32)

# 特征
feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]

#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)