tf.slice_input_producer(tensor_list, num_epochs=None, shuffle=True, seed=None,
       capacity=32, shared_name=None, name=None)

input_queue = tf.train.slice_input_producer([data, label], num_epochs=1, shuffle=True, capacity=32 )

tf.train.batch([data, label], batch_size=batch_size, capacity=capacity,num_threads=num_thread,allow_smaller_final_batch= True)
tf.train.shuffle_batch([example, label], batch_size=batch_size, capacity=capacity,num_threads=num_thread,allow_smaller_final_batch=True)

def get_Batch(data, label, batch_size):
 print(data.shape, label.shape)
 input_queue = tf.train.slice_input_producer([data, label], num_epochs=1, shuffle=True, capacity=32 ) 
 x_batch, y_batch = tf.train.batch(input_queue, batch_size=batch_size, num_threads=1, capacity=32, allow_smaller_final_batch=False)
 return x_batch, y_batch

import tensorflow as tf
import scipy.io as sio
import numpy as np
 
 
def get_Batch(data, label, batch_size):
 print(data.shape, label.shape)
 input_queue = tf.train.slice_input_producer([data, label], num_epochs=1, shuffle=True, capacity=32 ) 
 x_batch, y_batch = tf.train.batch(input_queue, batch_size=batch_size, num_threads=1, capacity=32, allow_smaller_final_batch=False)
 return x_batch, y_batch
 
 
data = sio.loadmat('data.mat')
train_x = data['train_x']
train_y = data['train_y']
test_x = data['test_x']
test_y = data['test_y']
 
x = tf.placeholder(tf.float32, [None, 10])
y = tf.placeholder(tf.float32, [None, 2])
 
w = tf.Variable(tf.truncated_normal([10, 2], stddev=0.1))
b = tf.Variable(tf.truncated_normal([2], stddev=0.1))
pred = tf.nn.softmax(tf.matmul(x, w) + b)
 
loss = tf.reduce_mean(-tf.reduce_sum(y * tf.log(pred), reduction_indices=[1]))
optimizer = tf.train.AdamOptimizer(2e-5).minimize(loss)
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(pred, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name='evaluation')
 
x_batch, y_batch = get_Batch(train_x, train_y, 1000)
# 訓(xùn)練
with tf.Session() as sess:
 #初始化參數(shù)
 sess.run(tf.global_variables_initializer())
 sess.run(tf.local_variables_initializer())
 # 開(kāi)啟協(xié)調(diào)器
 coord = tf.train.Coordinator()
 # 使用start_queue_runners 啟動(dòng)隊(duì)列填充
 threads = tf.train.start_queue_runners(sess, coord)
 epoch = 0
 try:
  while not coord.should_stop():
   # 獲取訓(xùn)練用的每一個(gè)batch中batch_size個(gè)樣本和標(biāo)簽
   data, label = sess.run([x_batch, y_batch])
   sess.run(optimizer, feed_dict={x: data, y: label})
   train_accuracy = accuracy.eval({x: data, y: label})
   test_accuracy = accuracy.eval({x: test_x, y: test_y})
   print("Epoch %d, Training accuracy %g, Testing accuracy %g" % (epoch, train_accuracy, test_accuracy))
   epoch = epoch + 1
 except tf.errors.OutOfRangeError: # num_epochs 次數(shù)用完會(huì)拋出此異常
  print("---Train end---")
 finally:
  # 協(xié)調(diào)器coord發(fā)出所有線程終止信號(hào)
  coord.request_stop()
  print('---Programm end---')
 coord.join(threads) # 把開(kāi)啟的線程加入主線程，等待threads結(jié)束

epoch = 150
for i in tqdm(range(epoch)):
 # 在total_train_xs, total_train_ys數(shù)據(jù)集中隨機(jī)抽取batch_size個(gè)樣本出來(lái)
 # 作為本輪迭代的訓(xùn)練數(shù)據(jù)batch_xs, batch_ys
 batch_size = 1000
 sample_idxs = np.random.choice(range(len(all_data)), size=batch_size)
 batch_xs = []
 batch_ys = []
 
 val_sample_idxs = np.random.choice(range(len(all_data)), size=batch_size)
 val_batch_xs = []
 val_batch_ys = []
 
 for j in range(batch_size):
  train_id = sample_idxs[j]
  batch_xs.append(all_data[train_id])
  batch_ys.append(all_label[train_id])
 
  val_id = val_sample_idxs[j]
  val_batch_xs.append(all_data[val_id])
  val_batch_ys.append(all_label[val_id])
 
 batch_xs = np.array(batch_xs)
 batch_ys = np.array(batch_ys)
 val_batch_xs = np.array(val_batch_xs)
 val_batch_ys = np.array(val_batch_ys)
 
 
 # 喂訓(xùn)練數(shù)據(jù)進(jìn)去訓(xùn)練
 sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
 if i % 50 == 0:
  y_train_pred = np.array(sess.run(y, feed_dict={x: batch_xs})).reshape(len(batch_xs))
  y_pred = np.array(sess.run(y, feed_dict={x: val_batch_xs})).reshape(len(val_batch_xs))
  # draw(y_test, y_pred)
  print("Iteration %d, train RMSE %f, val RMSE %f" % (i, calcaulateRMSE(batch_ys, y_train_pred), calcaulateRMSE(val_batch_ys, y_pred)))