本文整理匯總了Python中differential_privacy.multiple_teachers.utils.batch_indices方法的典型用法代碼示例。如果您正苦於以下問題：Python utils.batch_indices方法的具體用法？Python utils.batch_indices怎麽用？Python utils.batch_indices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: softmax_preds
# 需要導入模塊: from differential_privacy.multiple_teachers import utils [as 別名]
# 或者: from differential_privacy.multiple_teachers.utils import batch_indices [as 別名]
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