本文整理汇总了Python中differential_privacy.multiple_teachers.utils.batch_indices方法的典型用法代码示例。如果您正苦于以下问题：Python utils.batch_indices方法的具体用法？Python utils.batch_indices怎么用？Python utils.batch_indices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differential_privacy.multiple_teachers.utils的用法示例。
在下文中一共展示了utils.batch_indices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: softmax_preds
# 需要导入模块: from differential_privacy.multiple_teachers import utils [as 别名]
# 或者: from differential_privacy.multiple_teachers.utils import batch_indices [as 别名]
def softmax_preds(images, ckpt_path, return_logits=False):
"""
Compute softmax activations (probabilities) with the model saved in the path
specified as an argument
:param images: a np array of images
:param ckpt_path: a TF model checkpoint
:param logits: if set to True, return logits instead of probabilities
:return: probabilities (or logits if logits is set to True)
"""
# Compute nb samples and deduce nb of batches
data_length = len(images)
nb_batches = math.ceil(len(images) / FLAGS.batch_size)
# Declare data placeholder
train_data_node = _input_placeholder()
# Build a Graph that computes the logits predictions from the placeholder
if FLAGS.deeper:
logits = inference_deeper(train_data_node)
else:
logits = inference(train_data_node)
if return_logits:
# We are returning the logits directly (no need to apply softmax)
output = logits
else:
# Add softmax predictions to graph: will return probabilities
output = tf.nn.softmax(logits)
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
# Will hold the result
preds = np.zeros((data_length, FLAGS.nb_labels), dtype=np.float32)
# Create TF session
with tf.Session() as sess:
# Restore TF session from checkpoint file
saver.restore(sess, ckpt_path)
# Parse data by batch
for batch_nb in xrange(0, int(nb_batches+1)):
# Compute batch start and end indices
start, end = utils.batch_indices(batch_nb, data_length, FLAGS.batch_size)
# Prepare feed dictionary
feed_dict = {train_data_node: images[start:end]}
# Run session ([0] because run returns a batch with len 1st dim == 1)
preds[start:end, :] = sess.run([output], feed_dict=feed_dict)[0]
# Reset graph to allow multiple calls
tf.reset_default_graph()
return preds