【转载】 tf.train.slice_input_producer()和tf.train.batch()

原文地址：

https://www.jianshu.com/p/8ba9cfc738c2

 

 

 

------------------------------------------------------------------------------------------------

 

 

 

 

1.          tf.train.slice_input_producer  函数，一种模型数据的排队输入方法。

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

 

 

其参量为：

Args:
tensor_list: A list of Tensor objects. 
Every Tensor in tensor_list must have the same size in the first dimension.



# 循环Queue输入次数
num_epochs: An integer (optional). 
If specified, slice_input_producer produces each slice num_epochs times before generating an OutOfRange error. 
If not specified, slice_input_producer can cycle through the slices an unlimited number of times.


shuffle: Boolean. If true, the integers are randomly shuffled within each epoch.
seed: An integer (optional). Seed used if shuffle == True.



# Queue的容量
capacity: An integer. Sets the queue capacity.

shared_name: (optional). If set, this queue will be shared under the given name across multiple sessions.
name: A name for the operations (optional).

 

 

 

 

相关代码实例：

    # 生成包含输入和目标图片地址名的list
    input_files = [os.path.join(dirname, 'input', f) for f in flist]
    output_files = [os.path.join(dirname, 'output', f) for f in flist]

    # 内部自动转换为Constant String的Tensor,并排队进入队列
    input_queue, output_queue = tf.train.slice_input_producer(
        [input_files, output_files], shuffle=self.shuffle,
        seed=0123, num_epochs=self.num_epochs)

    # tf.train.slice_input_producer()每次取一对【输入-目标】对，交给ReadFile这
    # 个Op
    input_file = tf.read_file(input_queue)
    output_file = tf.read_file(output_queue)
    
    # 生成RGB格式的图像tensor
    im_input = tf.image.decode_jpeg(input_file, channels=3)
    im_output = tf.image.decode_jpeg(output_file, channels=3)

 

 

 

 

 

 

 

 

 

 

 

2.          tf.train.batch()函数

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
)

 

 

 

其参量为：

Args:
tensors: The list or dictionary of tensors to enqueue.
batch_size: The new batch size pulled from the queue.
num_threads: The number of threads enqueuing tensors. The batching will be nondeterministic if num_threads > 1.
capacity: An integer. The maximum number of elements in the queue.


#进行shuffle的输入是否为单个tensor
enqueue_many: Whether each tensor in tensors is a single example.

shapes: (Optional) The shapes for each example. Defaults to the inferred shapes for tensors.


dynamic_pad: Boolean. 
Allow variable dimensions in input shapes. 
The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes.


allow_smaller_final_batch: (Optional) Boolean. 
If True, allow the final batch to be smaller if there are insufficient items left in the queue.


shared_name: (Optional). 
If set, this queue will be shared under the given name across multiple sessions.


name: (Optional) A name for the operations.

 

 

 

相关代码实例

samples = tf.train.batch(
        sample,
        batch_size=self.batch_size,
        num_threads=self.nthreads,
        capacity=self.capacity)