直接上程式碼：
# -*- coding:utf-8 -*- import tensorflow as tf def read_data(file_queue): reader = tf.TextLineReader(skip_header_lines=1) key, value = reader.read(file_queue) defaults = [[0], [0.], [0.], [0.], [0.], ['']] Id,SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species = tf.decode_csv(value, defaults) #因為使用的是鳶尾花資料集，這裡需要對y值做轉換 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) 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) init_op = tf.global_variables_initializer() local_init_op = tf.local_variables_initializer() # local variables like epoch_num, batch_size with tf.Session() as sess: sess.run(init_op) sess.run(local_init_op) # Start populating the filename queue. coord = tf.train.Coordinator() threads = tf.train.start_queue_runners(coord=coord) # Retrieve a single instance: try: #while not coord.should_stop(): while True: example, label = sess.run([x_train_batch, y_train_batch]) print (example) print (label) except tf.errors.OutOfRangeError: print ('Done reading') finally: coord.request_stop() coord.join(threads) sess.close()
資料集是鳶尾花資料集，大家自行下載吧，下面給個示例：
Id,SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species 21,5.4,3.4,1.7,0.2,Iris-setosa 22,5.1,3.7,1.5,0.4,Iris-setosa 23,4.6,3.6,1.0,0.2,Iris-setosa 24,5.1,3.3,1.7,0.5,Iris-setosa 25,4.8,3.4,1.9,0.2,Iris-setosa 26,5.0,3.0,1.6,0.2,Iris-setosa 27,5.0,3.4,1.6,0.4,Iris-setosa 28,5.2,3.5,1.5,0.2,Iris-setosa 29,5.2,3.4,1.4,0.2,Iris-setosa 30,4.7,3.2,1.6,0.2,Iris-setosa 31,4.8,3.1,1.6,0.2,Iris-setosa 32,5.4,3.4,1.5,0.4,Iris-setosa 33,5.2,4.1,1.5,0.1,Iris-setosa 34,5.5,4.2,1.4,0.2,Iris-setosa 35,4.9,3.1,1.5,0.1,Iris-setosa 36,5.0,3.2,1.2,0.2,Iris-setosa 37,5.5,3.5,1.3,0.2,Iris-setosa