本文整理汇总了Python中losses.CrossEntropyLoss方法的典型用法代码示例。如果您正苦于以下问题：Python losses.CrossEntropyLoss方法的具体用法？Python losses.CrossEntropyLoss怎么用？Python losses.CrossEntropyLoss使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类losses的用法示例。
在下文中一共展示了losses.CrossEntropyLoss方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: build_graph
# 需要导入模块: import losses [as 别名]
# 或者: from losses import CrossEntropyLoss [as 别名]
def build_graph(reader,
model,
input_data_pattern,
label_loss_fn=losses.CrossEntropyLoss(),
batch_size=1000,
transformer_class=feature_transform.DefaultTransformer):
video_id, model_input_raw, labels_batch, num_frames = (
get_input_data_tensors(
reader,
input_data_pattern,
batch_size=batch_size,
num_readers=FLAGS.num_readers))
feature_transformer = transformer_class()
model_input, num_frames = feature_transformer.transform(model_input_raw, num_frames=num_frames)
with tf.name_scope("model"):
if FLAGS.noise_level > 0:
noise_level_tensor = tf.placeholder_with_default(0.0, shape=[], name="noise_level")
else:
noise_level_tensor = None
if FLAGS.dropout:
keep_prob_tensor = tf.placeholder_with_default(1.0, shape=[], name="keep_prob")
result = model.create_model(
model_input,
num_frames=num_frames,
vocab_size=reader.num_classes,
labels=labels_batch,
dropout=FLAGS.dropout,
keep_prob=keep_prob_tensor,
noise_level=noise_level_tensor)
else:
result = model.create_model(
model_input,
num_frames=num_frames,
vocab_size=reader.num_classes,
labels=labels_batch,
noise_level=noise_level_tensor)
print "result", result
predictions = result["predictions"]
tf.add_to_collection("predictions", predictions)
tf.add_to_collection("video_id_batch", video_id)
tf.add_to_collection("input_batch_raw", model_input_raw)
tf.add_to_collection("input_batch", model_input)
tf.add_to_collection("num_frames", num_frames)
tf.add_to_collection("labels", tf.cast(labels_batch, tf.float32))
if FLAGS.dropout:
tf.add_to_collection("keep_prob", keep_prob_tensor)
if FLAGS.noise_level > 0:
tf.add_to_collection("noise_level", noise_level_tensor)