Commit 71d1c382 authored by Marcel Rieger's avatar Marcel Rieger

Fix some deprecations.

parent 9218607b
......@@ -42,10 +42,10 @@ Tests should be run for Python 2 and 3. The following commands assume you are ro
python -m unittest test
# or via docker, python 2
docker run --rm -v `pwd`:/root/lbn -w /root/lbn tensorflow/tensorflow:latest python -m unittest test
docker run --rm -v `pwd`:/root/lbn -w /root/lbn tensorflow/tensorflow:1.13.1 python -m unittest test
# or via docker, python 3
docker run --rm -v `pwd`:/root/lbn -w /root/lbn tensorflow/tensorflow:latest-py3 python -m unittest test
docker run --rm -v `pwd`:/root/lbn -w /root/lbn tensorflow/tensorflow:1.13.1-py3 python -m unittest test
......@@ -57,7 +57,7 @@ class LBN(object):
*particle_weights* and *restframe_weights* can refer to externally defined variables with custom
initialized weights. If set, their shape must match the number of combinations and inputs. For
simple initialization tests, *weight_init* can be a tuple containing the Gaussian mean and
standard deviation that is passed to ``tf.random_normal``. When *None*, and the weight tensors
standard deviation that is passed to ``tf.random.normal``. When *None*, and the weight tensors
are created internally, mean and standard deviation default to *0* and *1 / combinations*. When
*abs_particle_weights* (*abs_restframe_weights*) is *True*, ``tf.abs`` is applied to the
particle (rest frame) weights. When *clip_particle_weights* (*clip_restframe_weights*) is
......@@ -253,33 +253,33 @@ class LBN(object):
Builds the LBN structure layer by layer within dedicated variable scopes. *input* must be a
tensor of the input four-vectors. All *kwargs* are forwarded to :py:meth:`build_features`.
with tf.variable_scope(
with tf.variable_scope("placeholders"):
with tf.name_scope(
with tf.name_scope("placeholders"):
with tf.variable_scope("inputs"):
with tf.name_scope("inputs"):
with tf.variable_scope("constants"):
with tf.name_scope("constants"):
with tf.variable_scope("particles"):
with tf.name_scope("particles"):
self.build_combinations("particle", self.n_particles)
# rest frames are not built for COMBINATIONS boost mode
if self.boost_mode != self.COMBINATIONS:
with tf.variable_scope("restframes"):
with tf.name_scope("restframes"):
self.build_combinations("restframe", self.n_restframes)
with tf.variable_scope("boost"):
with tf.name_scope("boost"):
with tf.variable_scope("features"):
with tf.name_scope("features"):
self.features = self._raw_features
if self.batch_norm_center or self.batch_norm_scale:
with tf.variable_scope("norm"):
with tf.name_scope("norm"):
self.features = self._norm_features
......@@ -346,7 +346,7 @@ class LBN(object):
mean, stddev = 0., 1. / m
W = tf.Variable(tf.random_normal(weight_shape, mean, stddev, dtype=tf.float32))
W = tf.Variable(tf.random.normal(weight_shape, mean, stddev, dtype=tf.float32))
# W is set externally, check the shape, consider batching
......@@ -427,15 +427,15 @@ class LBN(object):
pvec = tf.reshape(restframes_pvec, [-1, 3])
# determine the beta vectors
betavec = tf.div(pvec, E)
betavec = pvec / E
# determine the scalar beta and gamma values
beta = tf.div(tf.sqrt(tf.reduce_sum(tf.square(pvec), axis=1)), tf.squeeze(E, axis=-1))
gamma = tf.div(1., tf.sqrt(1. - tf.square(beta) + self.epsilon))
beta = tf.sqrt(tf.reduce_sum(tf.square(pvec), axis=1)) / tf.squeeze(E, axis=-1)
gamma = 1. / tf.sqrt(1. - tf.square(beta) + self.epsilon)
# the e vector, (1, -betavec / beta)^T
beta = tf.expand_dims(beta, axis=-1)
e = tf.expand_dims(tf.concat([tf.ones_like(E), -tf.div(betavec, beta)], axis=-1), axis=-1)
e = tf.expand_dims(tf.concat([tf.ones_like(E), -betavec / beta], axis=-1), axis=-1)
e_T = tf.transpose(e, perm=[0, 2, 1])
# finally, the boost matrix
......@@ -15,7 +15,7 @@ import tensorflow as tf
from lbn import LBN, FeatureFactory
# enable eager execution
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