Original Address:
https://www.jianshu.com/p/8ba9cfc738c2
------------------------------------------------------------------------------------------------
1. tf.train.slice_input_producer function, a model that the queuing data input method.
tf.train.slice_input_producer( tensor_list, num_epochs=None, shuffle=True, seed=None, capacity=32, shared_name=None, name=None )
Its parameters are:
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).
The relevant code examples:
# Generates an input image and a target address name List input_files = [the os.path.join (dirname, ' INPUT ' , F) for F in flist] output_files = [the os.path.join (dirname, ' Output ' , F) for F in flist] # internal automatically converted to the Constant String Tensor, and queued into the queue 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 () each take one pair [input - target] right, to which ReadFile # 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()Functions
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 )
Its parameters are:
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.
The relevant code examples
samples = tf.train.batch( sample, batch_size=self.batch_size, num_threads=self.nthreads, capacity=self.capacity)