How to use the tf.train.batch function of python
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tf.train.batch function tf.train.batch( tensors, batch_size, num_threads=1, capacity=32, enqueue_many=False, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False, shared_name=None, name=None)
Of which:
Tensors: Combinations of data obtained using slice_input_producer.
batch_size: Sets the number of dequeued data to be retrieved from the queue at a time.
num_threads: used to control the number of threads, if its value is not unique, due to the nature of thread execution, data acquisition may become out of order.
capacity: an integer that sets the maximum number of elements in the queue
5. allow_samller_final_batch: When True, if the number of samples in the queue is less than batch_size, the number of samples left behind will be dequeued; when False, samples less than batch_size will not be queued.
6, name: name
Test Code 1, allow_samller_final_batch=Trueimport pandas as pdimport numpy as npimport tensorflow as tf#generate data def generate_data(): num = 18 label = np.arange(num) return label#get data def get_batch_data(): label = generate_data() input_queue = tf.train.slice_input_producer([label], shuffle=False,num_epochs=2) label_batch = tf.train.batch(input_queue, batch_size=5, num_threads=1, capacity=64,allow_smaller_final_batch=True) return label_batch#data group label = get_batch_data()sess = tf.Session()#initialize variables sess.run(tf.global_variables_initializer())sess.run(tf.local_variables_initializer())#initialize batch training parameters coord = tf.train.Coordinator()threads = tf.train.start_queue_runners(sess,coord)try: while not coord.should_stop(): #Get the next set of data automatically l = sess.run(label) print(l)except tf.errors.OutOfRangeError: print('Done training')finally: coord.request_stop()coord.join(threads)sess.close()
The results of the run were:
[0 1 2 3 4]
[5 6 7 8 9]
[10 11 12 13 14]
[15 16 17 0 1]
[2 3 4 5 6]
[ 7 8 9 10 11]
[12 13 14 15 16]
[17]
Done training
2、allow_samller_final_batch=False
Less output than allow_samller_final_batch=True [17]
import pandas as pdimport numpy as npimport tensorflow as tf#generate data def generate_data(): num = 18 label = np.arange(num) return label#get data def get_batch_data(): label = generate_data() input_queue = tf.train.slice_input_producer([label], shuffle=False,num_epochs=2) label_batch = tf.train.batch(input_queue, batch_size=5, num_threads=1, capacity=64,allow_smaller_final_batch=False) return label_batch#data group label = get_batch_data()sess = tf.Session()#initialize variables sess.run(tf.global_variables_initializer())sess.run(tf.local_variables_initializer())#initialize batch training parameters coord = tf.train.Coordinator()threads = tf.train.start_queue_runners(sess,coord)try: while not coord.should_stop(): #Get the next set of data automatically l = sess.run(label) print(l)except tf.errors.OutOfRangeError: print('Done training')finally: coord.request_stop()coord.join(threads)sess.close()
The results of the run were:
[0 1 2 3 4]
[5 6 7 8 9]
[10 11 12 13 14]
[15 16 17 0 1]
[2 3 4 5 6]
[ 7 8 9 10 11]
[12 13 14 15 16]
Done training
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