diff --git a/keras.py b/keras.py
index b13a8aff9a8cbb12fb2fa0f5a9dc8726780dc5f9..f2ed438af67bc92250a768baab3b6b67ea7ad4ef 100644
--- a/keras.py
+++ b/keras.py
@@ -1,11 +1,11 @@
# from itertools import izip
import itertools
+from functools import cached_property
import gc
from collections import OrderedDict, defaultdict
import fnmatch
import math
import datetime
-import nvidia_smi
import numpy as np
import tensorflow as tf
@@ -40,14 +40,14 @@ from .plotting import (
def kVar(*args, **kwargs):
- """ produce a keras-tracked tf.Variable from all given parameters """
+ """produce a keras-tracked tf.Variable from all given parameters"""
var = tf.Variable(*args, **kwargs)
track_variable(var)
return var
def kOpt(opt, **kwargs):
- """ instanciate a keras Optimizer with all applicable **kwargs """
+ """instanciate a keras Optimizer with all applicable **kwargs"""
if not callable(opt):
opt = getattr(tf.keras.optimizers, opt)
assert issubclass(opt, tf.keras.optimizers.Optimizer)
@@ -56,14 +56,14 @@ def kOpt(opt, **kwargs):
def kInput(ref, **kwargs):
- """ produce a keras.Input with shape & dtype according to ref """
+ """produce a keras.Input with shape & dtype according to ref"""
kwargs.setdefault("shape", ref.shape[1:])
kwargs.setdefault("dtype", ref.dtype)
return tf.keras.Input(**kwargs)
def keras_register_custom_object(obj):
- """ decorator for globally registering a custom object with keras """
+ """decorator for globally registering a custom object with keras"""
tf.keras.utils.get_custom_objects()[obj.__name__] = obj
return obj
@@ -219,7 +219,7 @@ def Onehot(index, n, name=None):
class Moment(tf.keras.metrics.Mean):
def __init__(self, order, label=False, **kwargs):
- """ Metric calculating the order-th moment """
+ """Metric calculating the order-th moment"""
assert order == int(order)
assert label == bool(label)
kwargs.setdefault("name", "%smom%d" % ("l" if label else "", order))
@@ -255,24 +255,30 @@ def _patfilter(pattern, items):
return filter(re.compile(pattern).search, items)
-class GPUStats(tf.keras.callbacks.Callback):
- """
- conda: conda install -c fastai nvidia-ml-py3
- pip : pip install nvidia-ml-py3
- """
+try:
+ import nvidia_smi
+except ImportError:
+ pass
+else:
+
+ class GPUStats(tf.keras.callbacks.Callback):
+ """
+ conda: conda install -c fastai nvidia-ml-py3
+ pip : pip install nvidia-ml-py3
+ """
- def __init__(self, idx=0):
- nvidia_smi.nvmlInit()
- self.handle = nvidia_smi.nvmlDeviceGetHandleByIndex(idx)
+ def __init__(self, idx=0):
+ nvidia_smi.nvmlInit()
+ self.handle = nvidia_smi.nvmlDeviceGetHandleByIndex(idx)
- def on_x_end(self, x, logs=None):
- self.mem = nvidia_smi.nvmlDeviceGetMemoryInfo(self.handle)
- self.res = nvidia_smi.nvmlDeviceGetUtilizationRates(self.handle)
- logs["GPU-Usage [%]"] = self.res.gpu
- logs["GPU-vRAM [%]"] = 100 * self.mem.used / self.mem.total
- logs["GPU-vRAM [MiB]"] = self.mem.used / (1024 ** 2)
+ def on_x_end(self, x, logs=None):
+ self.mem = nvidia_smi.nvmlDeviceGetMemoryInfo(self.handle)
+ self.res = nvidia_smi.nvmlDeviceGetUtilizationRates(self.handle)
+ logs["GPU-Usage [%]"] = self.res.gpu
+ logs["GPU-vRAM [%]"] = 100 * self.mem.used / self.mem.total
+ logs["GPU-vRAM [MiB]"] = self.mem.used / (1024 ** 2)
- on_batch_end = on_x_end
+ on_batch_end = on_x_end
class Moment2Std(tf.keras.callbacks.Callback):
@@ -447,7 +453,7 @@ class PlotMulticlass(PlottingCallback, TFSummaryCallback):
self,
x,
y,
- sample_weight=None,
+ sample_weight=(None,),
class_names=["signal", "background"],
to_file=False,
columns=None,
@@ -521,13 +527,16 @@ class PlotMulticlass(PlottingCallback, TFSummaryCallback):
plt.close("all")
gc.collect()
+ @cached_property
+ def prediction(self):
+ return self.model.predict(self.x, batch_size=4096)
+
def make_eval_plots(self, epoch=0):
- prediction = self.model.predict(self.x, batch_size=4096)
imgs = {}
fig = figure_roc_curve(
self.y,
- prediction,
+ self.prediction,
class_names=self.class_names,
sample_weight=self.sample_weight_flat,
)
@@ -536,7 +545,7 @@ class PlotMulticlass(PlottingCallback, TFSummaryCallback):
fig = figure_confusion_matrix(
self.y,
- prediction,
+ self.prediction,
class_names=self.class_names,
sample_weight=self.sample_weight_flat,
normalize="true",
@@ -546,7 +555,7 @@ class PlotMulticlass(PlottingCallback, TFSummaryCallback):
fig = figure_confusion_matrix(
self.y,
- prediction,
+ self.prediction,
class_names=self.class_names,
sample_weight=self.sample_weight_flat,
normalize="pred",
@@ -556,7 +565,7 @@ class PlotMulticlass(PlottingCallback, TFSummaryCallback):
if self.plot_activations:
fig = figure_node_activations(
- prediction,
+ self.prediction,
self.y,
class_names=self.class_names,
disjoint=True,
@@ -610,6 +619,24 @@ class PlotMulticlassEval(PlotMulticlass):
self.make_eval_plots(0)
+class PlotAssignment(PlotMulticlass):
+ def __init__(
+ self,
+ x,
+ y,
+ **kwargs,
+ ):
+ super().__init__(x, y, **kwargs)
+ n_objects = self.y.shape[1]
+ n_flavours = 3
+ self.y = self.y.reshape(-1, n_flavours)
+ self.sample_weight_flat = (self.y * np.array([1 / 0.81, 1 / 0.15, 1 / 0.037])).sum(axis=-1)
+
+ @cached_property
+ def prediction(self):
+ return self.model.predict(self.x, batch_size=4096).reshape(-1, 3)
+
+
class PlotLBN(PlottingCallback, TFSummaryCallback):
def __init__(self, lbn_layer=None, inp_particle_names=None, *args, **kwargs):
self.lbn = lbn_layer
diff --git a/numpy.py b/numpy.py
index 356f2a3b0a61f351f5b7f0abbc19f217021ec1ca..dcad6f2bd6da140f09874bb468a15b031d8ad9b4 100644
--- a/numpy.py
+++ b/numpy.py
@@ -3,6 +3,12 @@
import numpy as np
+def set_thresh(values, thresh=1e-5):
+ val = values.copy()
+ val[val < thresh] = 0
+ return val
+
+
def one_hot(a, n=None):
if n is None:
n = a.max() + 1