本文整理匯總了Python中tensorflow.ReaderBase方法的典型用法代碼示例。如果您正苦於以下問題：Python tensorflow.ReaderBase方法的具體用法？Python tensorflow.ReaderBase怎麽用？Python tensorflow.ReaderBase使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類tensorflow的用法示例。
在下文中一共展示了tensorflow.ReaderBase方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: __init__
# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import ReaderBase [as 別名]
def __init__(self, config, mode="train", input_reader=None):
"""Basic setup. The actual TensorFlow graph is constructed in build().
Args:
config: Object containing configuration parameters.
mode: "train", "eval" or "encode".
input_reader: Subclass of tf.ReaderBase for reading the input serialized
tf.Example protocol buffers. Defaults to TFRecordReader.
Raises:
ValueError: If mode is invalid.
"""
if mode not in ["train", "eval", "encode"]:
raise ValueError("Unrecognized mode: %s" % mode)
self.config = config
self.mode = mode
self.reader = input_reader if input_reader else tf.TFRecordReader()
# Initializer used for non-recurrent weights.
self.uniform_initializer = tf.random_uniform_initializer(
minval=-self.config.uniform_init_scale,
maxval=self.config.uniform_init_scale)
# Input sentences represented as sequences of word ids. "encode" is the
# source sentence, "decode_pre" is the previous sentence and "decode_post"
# is the next sentence.
# Each is an int64 Tensor with shape [batch_size, padded_length].
self.encode_ids = None
self.decode_pre_ids = None
self.decode_post_ids = None
# Boolean masks distinguishing real words (1) from padded words (0).
# Each is an int32 Tensor with shape [batch_size, padded_length].
self.encode_mask = None
self.decode_pre_mask = None
self.decode_post_mask = None
# Input sentences represented as sequences of word embeddings.
# Each is a float32 Tensor with shape [batch_size, padded_length, emb_dim].
self.encode_emb = None
self.decode_pre_emb = None
self.decode_post_emb = None
# The output from the sentence encoder.
# A float32 Tensor with shape [batch_size, num_gru_units].
self.thought_vectors = None
# The cross entropy losses and corresponding weights of the decoders. Used
# for evaluation.
self.target_cross_entropy_losses = []
self.target_cross_entropy_loss_weights = []
# The total loss to optimize.
self.total_loss = None 示例2: __init__
# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import ReaderBase [as 別名]
def __init__(self, config, mode="train", input_reader=None):
"""Basic setup. The actual TensorFlow graph is constructed in build().
Args:
config: Object containing configuration parameters.
mode: "train", "eval" or "encode".
input_reader: Subclass of tf.ReaderBase for reading the input serialized
tf.Example protocol buffers. Defaults to TFRecordReader.
Raises:
ValueError: If mode is invalid.
"""
if mode not in ["train", "eval", "encode"]:
raise ValueError("Unrecognized mode: %s" % mode)
self.config = config
self.mode = mode
self.reader = input_reader if input_reader else tf.TFRecordReader()
# Initializer used for non-recurrent weights.
self.uniform_initializer = tf.random_uniform_initializer(
minval=-self.config.uniform_init_scale,
maxval=self.config.uniform_init_scale)
# Input sentences represented as sequences of word ids. "encode" is the
# source sentence, "decode_pre" is the previous sentence and
# "decode_post" is the next sentence.
# Each is an int64 Tensor with shape [batch_size, padded_length].
self.encode_ids = None
self.decode_pre_ids = None
self.decode_post_ids = None
# Boolean masks distinguishing real words (1) from padded words (0).
# Each is an int32 Tensor with shape [batch_size, padded_length].
self.encode_mask = None
self.decode_pre_mask = None
self.decode_post_mask = None
# Input sentences represented as sequences of word embeddings.
# Each is a float32 Tensor with shape
# [batch_size, padded_length, emb_dim].
self.encode_emb = None
self.decode_pre_emb = None
self.decode_post_emb = None
# The output from the sentence encoder.
# A float32 Tensor with shape [batch_size, num_gru_units].
self.thought_vectors = None
# The cross entropy losses and corresponding weights of the decoders.
# Used for evaluation.
self.target_cross_entropy_losses = []
self.target_cross_entropy_loss_weights = []
# The total loss to optimize.
self.total_loss = None 示例3: __init__
# 需要導入模塊: import tensorflow [as 別名]
# 或者: from tensorflow import ReaderBase [as 別名]
def __init__(self, config, mode="train", input_reader=None, input_queue=None):
"""Basic setup. The actual TensorFlow graph is constructed in build().
Args:
config: Object containing configuration parameters.
mode: "train", "eval" or "encode".
input_reader: Subclass of tf.ReaderBase for reading the input serialized
tf.Example protocol buffers. Defaults to TFRecordReader.
Raises:
ValueError: If mode is invalid.
"""
if mode not in ["train", "eval", "encode"]:
raise ValueError("Unrecognized mode: %s" % mode)
self.config = config
self.mode = mode
self.reader = input_reader if input_reader else tf.TFRecordReader()
self.input_queue = input_queue
# Initializer used for non-recurrent weights.
self.uniform_initializer = tf.random_uniform_initializer(
minval=-FLAGS.uniform_init_scale,
maxval=FLAGS.uniform_init_scale)
# Input sentences represented as sequences of word ids. "encode" is the
# source sentence, "decode_pre" is the previous sentence and "decode_post"
# is the next sentence.
# Each is an int64 Tensor with shape [batch_size, padded_length].
self.encode_ids = None
# Boolean masks distinguishing real words (1) from padded words (0).
# Each is an int32 Tensor with shape [batch_size, padded_length].
self.encode_mask = None
# Input sentences represented as sequences of word embeddings.
# Each is a float32 Tensor with shape [batch_size, padded_length, emb_dim].
self.encode_emb = None
# The output from the sentence encoder.
# A float32 Tensor with shape [batch_size, num_gru_units].
self.thought_vectors = None
# The total loss to optimize.
self.total_loss = None