From c9ecbfbe2cf2cbfaaecbe1e5c93c6ca7710754a6 Mon Sep 17 00:00:00 2001 From: holl- Date: Sat, 3 Aug 2024 18:53:48 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20tum-pbs/?= =?UTF-8?q?PhiML@5d9d6602c39e9cb2d97e2907fe0b7be937f06793=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Advantages_Data_Types.html | 6 ++-- Convert.html | 2 +- Examples.html | 2 +- Introduction.html | 2 +- Linear_Solves.html | 14 ++++---- N_Dimensional.html | 18 +++++----- Networks.html | 16 ++++----- Performance.html | 34 +++++++++--------- Shapes.html | 6 ++-- Tensors.html | 70 +++++++++++++++++++------------------- 10 files changed, 85 insertions(+), 85 deletions(-) diff --git a/Advantages_Data_Types.html b/Advantages_Data_Types.html index eb4c3fc..039f266 100644 --- a/Advantages_Data_Types.html +++ b/Advantages_Data_Types.html @@ -15151,10 +15151,10 @@

Why ΦML has Preci diff --git a/Convert.html b/Convert.html index efcd1f9..468689d 100644 --- a/Convert.html +++ b/Convert.html @@ -15617,7 +15617,7 @@

Converting Tensors -
2024-08-03 18:17:37.023984: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+
2024-08-03 18:51:32.753942: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 

 
 
diff --git a/Examples.html b/Examples.html
index bf5ab80..cd0385f 100644
--- a/Examples.html
+++ b/Examples.html
@@ -15215,7 +15215,7 @@ 
Training an MLP
-
2024-08-03 18:17:52.619976: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+
2024-08-03 18:51:48.070782: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 

 
 
diff --git a/Introduction.html b/Introduction.html
index bbd1e65..3e644bd 100644
--- a/Introduction.html
+++ b/Introduction.html
@@ -15349,7 +15349,7 @@ 
Usage without ΦML's Te
 
 
 
 
diff --git a/Linear_Solves.html b/Linear_Solves.html
index 3720ac2..4382b3d 100644
--- a/Linear_Solves.html
+++ b/Linear_Solves.html
@@ -16021,13 +16021,13 @@ 
Obtaining Additional Inf
 
 
 
@@ -16155,11 +16155,11 @@ 
Linear Solves with Native Tensors
Grids
 
 
 

 
 
@@ -15289,7 +15289,7 @@ 
Grids
Grids
 
 
 

 
 
@@ -15328,7 +15328,7 @@ 
Grids
Grids
 
 
 

 
 
@@ -15479,7 +15479,7 @@ 
Grids
Grids
 
 
 

 
 
@@ -15518,7 +15518,7 @@ 
Grids
Grids
 
 
 

 
 
@@ -15557,7 +15557,7 @@ 
Grids
Grids
 
 
 

 
 
@@ -15672,7 +15672,7 @@ 
Dimensions as Components
-
(othersⁱ=4, pointsⁱ=4) 0.327 ± 0.303 (0e+00...9e-01)

+
(othersⁱ=4, pointsⁱ=4) 0.376 ± 0.323 (0e+00...8e-01)

 
 
 
@@ -15711,7 +15711,7 @@ 
Dimensions as Components
-
(othersⁱ=4, pointsⁱ=4) 0.307 ± 0.219 (0e+00...7e-01)

+
(othersⁱ=4, pointsⁱ=4) 0.293 ± 0.239 (0e+00...7e-01)

 
 
 
@@ -15750,7 +15750,7 @@ 
Dimensions as Components
-
(othersⁱ=4, pointsⁱ=4) 0.535 ± 0.350 (0e+00...1e+00)

+
(othersⁱ=4, pointsⁱ=4) 0.544 ± 0.347 (0e+00...1e+00)

 
 
 
diff --git a/Networks.html b/Networks.html
index 69e59ae..5f31dec 100644
--- a/Networks.html
+++ b/Networks.html
@@ -15300,7 +15300,7 @@ 
Training an MLP
-
<matplotlib.collections.PathCollection at 0x7f67703d27c0>

+
<matplotlib.collections.PathCollection at 0x7f25765d2850>

 
 
 
@@ -15312,7 +15312,7 @@ 
Training an MLP
-
@@ -15467,7 +15467,7 @@ 
Training an MLP
-
<matplotlib.collections.PathCollection at 0x7f676cb17d90>

+
<matplotlib.collections.PathCollection at 0x7f2572a96e20>

 
 
 
@@ -15479,7 +15479,7 @@ 
Training an MLP
-
@@ -15569,7 +15569,7 @@ 
Training a U-Net
-
<matplotlib.image.AxesImage at 0x7f676ca516d0>

+
<matplotlib.image.AxesImage at 0x7f25729d4760>

 
 
 
@@ -15581,7 +15581,7 @@ 
Training a U-Net
-
@@ -15642,7 +15642,7 @@ 
Training a U-Net
-
<matplotlib.image.AxesImage at 0x7f676c9b9190>

+
<matplotlib.image.AxesImage at 0x7f257208d220>

 
 
 
@@ -15654,7 +15654,7 @@ 
Training a U-Net
-
diff --git a/Performance.html b/Performance.html
index c7bdadc..7e54d09 100644
--- a/Performance.html
+++ b/Performance.html
@@ -15212,8 +15212,8 @@ 
Performance and JIT-compilation
-
Φ-ML + torch JIT compilation: float64 0.21197349
-Φ-ML + torch execution average: 0.03549956902861595 +- 0.004727165214717388
+
Φ-ML + torch JIT compilation: float64 0.20342584
+Φ-ML + torch execution average: 0.03442099690437317 +- 0.0051096719689667225
 

 
 
@@ -15233,8 +15233,8 @@ 
Performance and JIT-compilation
-
Φ-ML + jax JIT compilation: float64 0.1617278
-Φ-ML + jax execution average: 0.012110414914786816 +- 0.0008309065597131848
+
Φ-ML + jax JIT compilation: float64 0.1580026
+Φ-ML + jax execution average: 0.011942562647163868 +- 0.0009103829506784678
 

 
 
@@ -15244,7 +15244,7 @@ 
Performance and JIT-compilation
-
2024-08-03 18:19:04.275435: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
+
2024-08-03 18:52:57.862299: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
 

 
 
@@ -15254,8 +15254,8 @@ 
Performance and JIT-compilation
-
Φ-ML + tensorflow JIT compilation: float64 13.906091
-Φ-ML + tensorflow execution average: 0.053461670875549316 +- 0.00282722688280046
+
Φ-ML + tensorflow JIT compilation: float64 13.694691
+Φ-ML + tensorflow execution average: 0.05257116258144379 +- 0.0016889951657503843
 

 
 
@@ -15361,8 +15361,8 @@ 
Native Implementations
-
jax JIT compilation: 0.1335396100000139
-jax execution average: 0.010147904414141366
+
jax JIT compilation: 0.13146938599999203
+jax execution average: 0.010196452373737267
 

 
 
@@ -15443,11 +15443,11 @@ 
Native Implementations
-
/tmp/ipykernel_2746/3571425526.py:12: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+
/tmp/ipykernel_2692/3571425526.py:12: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
   dist = torch.sqrt(torch.maximum(torch.sum(deltas ** 2, -1), torch.tensor(1e-4)))  # eps=1e-4 to avoid NaN during backprop of sqrt
-/tmp/ipykernel_2746/3571425526.py:20: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+/tmp/ipykernel_2692/3571425526.py:20: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
   x_inc_contrib = torch.sum(torch.where(has_impact.unsqueeze(-1), torch.minimum(impact_time.unsqueeze(-1) - dt, torch.tensor(0.0)) * impulse, torch.tensor(0.0)), -2)
-/tmp/ipykernel_2746/3571425526.py:22: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
+/tmp/ipykernel_2692/3571425526.py:22: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.
   v += torch.sum(torch.where(has_impact.unsqueeze(-1), impulse, torch.tensor(0.0)), -2)
 

 
@@ -15458,8 +15458,8 @@ 
Native Implementations
-
torch JIT compilation: 0.036969639360904694
-torch execution average: 0.03337471932172775
+
torch JIT compilation: 0.036141786724328995
+torch execution average: 0.03381171450018883
 

 
 
@@ -15469,7 +15469,7 @@ 
Native Implementations
-
/tmp/ipykernel_2746/3571425526.py:45: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
+
/tmp/ipykernel_2692/3571425526.py:45: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
   print(f"torch execution average: {torch.mean(torch.tensor(dt_torch[2:]))}")
 

 
@@ -15545,8 +15545,8 @@ 
Native Implementations
-
tensorflow JIT compilation: 0.37863877415657043
-tensorflow execution average: 0.03856977820396423
+
tensorflow JIT compilation: 0.20972128212451935
+tensorflow execution average: 0.03771407902240753
 

 
 
diff --git a/Shapes.html b/Shapes.html
index 615cb79..0d3ebe2 100644
--- a/Shapes.html
+++ b/Shapes.html
@@ -15453,7 +15453,7 @@ 
Dimension Types
-
(xˢ=28, yˢ=28) 0.517 ± 0.296 (5e-05...1e+00)

+
(xˢ=28, yˢ=28) 0.520 ± 0.289 (4e-04...1e+00)

 
 
 
@@ -15534,7 +15534,7 @@ 
Automatic Reshaping
-
(-0.322, -0.443, 1.164, -0.047, 0.237, -0.144) (aᶜ=2, bᶜ=3)

+
(-0.246, -1.220, 0.321, 1.947, -0.171, 2.163) (aᶜ=2, bᶜ=3)

 
 
 
@@ -15590,7 +15590,7 @@ 
Automatic Reshaping
-
(0.872, 0.512, 1.843, 0.377, 0.017, 1.348) (aᶜ=2, bᶜ=3)

+
(0.130, 0.421, 0.520, -0.325, -0.034, 0.064) (aᶜ=2, bᶜ=3)

 
 
 
diff --git a/Tensors.html b/Tensors.html
index 5b657dd..57e46d6 100644
--- a/Tensors.html
+++ b/Tensors.html
@@ -15232,7 +15232,7 @@ 
Named Dimensions
-
(examplesᵇ=2, xˢ=4, yˢ=3) 0.542 ± 0.271 (3e-03...1e+00)

+
(examplesᵇ=2, xˢ=4, yˢ=3) 0.531 ± 0.272 (9e-02...9e-01)

 
 
 
@@ -15283,7 +15283,7 @@ 
Printing Options
-
(examplesᵇ=2, xˢ=4, yˢ=3) float32 5.42119e-01 ± 2.70566e-01 (3.34590e-03...9.97822e-01)
+
(examplesᵇ=2, xˢ=4, yˢ=3) float32 5.31045e-01 ± 2.72271e-01 (8.78806e-02...9.12761e-01)
 

 
 
@@ -15321,13 +15321,13 @@ 
Printing Options
 
examples=0  
- 0.022, 0.638, 0.590, 0.712,
- 0.908, 0.003, 0.998, 0.348,
- 0.491, 0.290, 0.589, 0.727  along (xˢ=4, yˢ=3)
+ 0.705, 0.365, 0.913, 0.900,
+ 0.424, 0.322, 0.774, 0.779,
+ 0.243, 0.148, 0.890, 0.257  along (xˢ=4, yˢ=3)
 examples=1  
- 0.409, 0.262, 0.654, 0.329,
- 0.815, 0.524, 0.204, 0.686,
- 0.334, 0.929, 0.845, 0.703  along (xˢ=4, yˢ=3)
+ 0.450, 0.671, 0.314, 0.167,
+ 0.847, 0.202, 0.088, 0.632,
+ 0.374, 0.890, 0.665, 0.727  along (xˢ=4, yˢ=3)
 

 
 
@@ -15364,15 +15364,15 @@ 
Printing Options
-
[[[0.49 0.91 0.02]
-  [0.29 0.00 0.64]
-  [0.59 1.00 0.59]
-  [0.73 0.35 0.71]]
-
- [[0.33 0.82 0.41]
-  [0.93 0.52 0.26]
-  [0.85 0.20 0.65]
-  [0.70 0.69 0.33]]]
+
[[[0.24 0.42 0.70]
+  [0.15 0.32 0.36]
+  [0.89 0.77 0.91]
+  [0.26 0.78 0.90]]
+
+ [[0.37 0.85 0.45]
+  [0.89 0.20 0.67]
+  [0.66 0.09 0.31]
+  [0.73 0.63 0.17]]]
 

 
 
@@ -15506,15 +15506,15 @@ 
Wrapping and Unwrapping
-
array([[[0.49054566, 0.9077075 , 0.02222426],
-        [0.2904574 , 0.0033459 , 0.637612  ],
-        [0.5891476 , 0.997822  , 0.5904411 ],
-        [0.7265696 , 0.3479879 , 0.71155703]],
-
-       [[0.33403102, 0.81500655, 0.40922892],
-        [0.92898035, 0.5241156 , 0.261785  ],
-        [0.84538066, 0.2041101 , 0.65405846],
-        [0.70342946, 0.68630224, 0.32901454]]], dtype=float32)

+
array([[[0.24287069, 0.42406175, 0.70494646],
+        [0.14764948, 0.32196626, 0.36493638],
+        [0.88996863, 0.7735132 , 0.9127609 ],
+        [0.2568987 , 0.7792125 , 0.8997674 ]],
+
+       [[0.374492  , 0.8470331 , 0.4503718 ],
+        [0.88990706, 0.20194583, 0.67085844],
+        [0.6649014 , 0.08788061, 0.3135259 ],
+        [0.72699827, 0.6315394 , 0.16706485]]], dtype=float32)

 
 
 
@@ -15565,15 +15565,15 @@ 
Wrapping and Unwrapping
-
array([[[0.49054566, 0.9077075 , 0.02222426],
-        [0.2904574 , 0.0033459 , 0.637612  ],
-        [0.5891476 , 0.997822  , 0.5904411 ],
-        [0.7265696 , 0.3479879 , 0.71155703]],
-
-       [[0.33403102, 0.81500655, 0.40922892],
-        [0.92898035, 0.5241156 , 0.261785  ],
-        [0.84538066, 0.2041101 , 0.65405846],
-        [0.70342946, 0.68630224, 0.32901454]]], dtype=float32)

+
array([[[0.24287069, 0.42406175, 0.70494646],
+        [0.14764948, 0.32196626, 0.36493638],
+        [0.88996863, 0.7735132 , 0.9127609 ],
+        [0.2568987 , 0.7792125 , 0.8997674 ]],
+
+       [[0.374492  , 0.8470331 , 0.4503718 ],
+        [0.88990706, 0.20194583, 0.67085844],
+        [0.6649014 , 0.08788061, 0.3135259 ],
+        [0.72699827, 0.6315394 , 0.16706485]]], dtype=float32)