Automated tutorials push

pytorch · Jun 24, 2024 · c86323e · c86323e
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diff --git a/_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb b/_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb
@@ -34,7 +34,7 @@
   {
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    "execution_count": null,
-   "id": "1bb88423",
+   "id": "9ba8ee83",
    "metadata": {},
    "outputs": [],
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@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "30e88c00",
+   "id": "05cc4f1b",
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    "source": [
     "\n",

diff --git a/_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb b/_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb
@@ -31,7 +31,7 @@
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-   "id": "928c31a0",
+   "id": "4ffc4dc0",
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    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "886238f6",
+   "id": "146701e4",
    "metadata": {},
    "source": [
     "\n",

diff --git a/_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb b/_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb
@@ -34,7 +34,7 @@
   {
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-   "id": "80a8bc41",
+   "id": "7ddb23c2",
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@@ -50,7 +50,7 @@
   },
   {
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+   "id": "35c87e1d",
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     "\n",

diff --git a/_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb b/_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb
@@ -35,7 +35,7 @@
   {
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-   "id": "c960d609",
+   "id": "f165baaf",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
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   {
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+   "id": "db68e714",
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    "source": [
     "\n",

diff --git a/_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb b/_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb
@@ -37,7 +37,7 @@
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-   "id": "47b6d22d",
+   "id": "0c9495e6",
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@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72922c26",
+   "id": "765def5b",
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     "\n",

diff --git a/_downloads/e2e556f6b4693c2cef716dd7f40caaf6/tensorboardyt_tutorial.ipynb b/_downloads/e2e556f6b4693c2cef716dd7f40caaf6/tensorboardyt_tutorial.ipynb
@@ -35,7 +35,7 @@
   {
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@@ -51,7 +51,7 @@
   },
   {
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-   "id": "5e469891",
+   "id": "ba7c212e",
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diff --git a/_downloads/ed9d4f94afb79f7dada6742a06c486a5/autogradyt_tutorial.ipynb b/_downloads/ed9d4f94afb79f7dada6742a06c486a5/autogradyt_tutorial.ipynb
@@ -34,7 +34,7 @@
   {
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+   "id": "2424e425",
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@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4dd7fc5b",
+   "id": "ec6ff4d4",
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     "\n",

diff --git a/_downloads/fe726e041160526cf828806536922cf6/modelsyt_tutorial.ipynb b/_downloads/fe726e041160526cf828806536922cf6/modelsyt_tutorial.ipynb
@@ -34,7 +34,7 @@
   {
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@@ -50,7 +50,7 @@
   },
   {
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-   "id": "9efd889d",
+   "id": "54aab864",
    "metadata": {},
    "source": [
     "\n",

diff --git a/_images/sphx_glr_coding_ddpg_001.png b/_images/sphx_glr_coding_ddpg_001.png
diff --git a/_images/sphx_glr_dqn_with_rnn_tutorial_001.png b/_images/sphx_glr_dqn_with_rnn_tutorial_001.png
diff --git a/_images/sphx_glr_neural_style_tutorial_004.png b/_images/sphx_glr_neural_style_tutorial_004.png
diff --git a/_images/sphx_glr_reinforcement_ppo_001.png b/_images/sphx_glr_reinforcement_ppo_001.png
diff --git a/_images/sphx_glr_reinforcement_q_learning_001.png b/_images/sphx_glr_reinforcement_q_learning_001.png
diff --git a/_images/sphx_glr_spatial_transformer_tutorial_001.png b/_images/sphx_glr_spatial_transformer_tutorial_001.png
diff --git a/_images/sphx_glr_torchvision_tutorial_002.png b/_images/sphx_glr_torchvision_tutorial_002.png
diff --git a/_sources/advanced/coding_ddpg.rst.txt b/_sources/advanced/coding_ddpg.rst.txt
@@ -1632,26 +1632,26 @@ modules we need.
 
 
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-    reward: -2.15 (r0 = -2.88), reward eval: reward:  0.01, reward normalized=-3.05/6.49, grad norm= 208.83, loss_value= 391.54, loss_actor= 15.33, target value: -18.46:  56%|#####6    | 5600/10000 [00:08<00:05, 869.39it/s]
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+    reward: -4.58 (r0 = -3.21), reward eval: reward: -2.90, reward normalized=-2.86/5.12, grad norm= 213.90, loss_value= 296.15, loss_actor= 13.14, target value: -20.54:  96%|#########6| 9600/10000 [00:22<00:01, 285.28it/s]
+    reward: -4.58 (r0 = -3.21), reward eval: reward: -2.90, reward normalized=-2.86/5.12, grad norm= 213.90, loss_value= 296.15, loss_actor= 13.14, target value: -20.54: : 10400it [00:25, 264.79it/s]                        
+    reward: -3.55 (r0 = -3.21), reward eval: reward: -2.90, reward normalized=-3.59/4.43, grad norm= 57.50, loss_value= 177.22, loss_actor= 20.78, target value: -23.86: : 10400it [00:25, 264.79it/s] 
 
 
 
@@ -1721,7 +1721,7 @@ To iterate further on this loss module we might consider:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  40.986 seconds)
+   **Total running time of the script:** ( 0 minutes  41.406 seconds)
 
 
 .. _sphx_glr_download_advanced_coding_ddpg.py:

diff --git a/_sources/advanced/dynamic_quantization_tutorial.rst.txt b/_sources/advanced/dynamic_quantization_tutorial.rst.txt
@@ -516,9 +516,9 @@ models run single threaded.
  .. code-block:: none
 
     loss: 5.167
-    elapsed time (seconds): 204.6
+    elapsed time (seconds): 205.4
     loss: 5.168
-    elapsed time (seconds): 116.3
+    elapsed time (seconds): 113.9
 
 
 
@@ -540,7 +540,7 @@ Thanks for reading! As always, we welcome any feedback, so please create an issu
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 5 minutes  29.155 seconds)
+   **Total running time of the script:** ( 5 minutes  27.914 seconds)
 
 
 .. _sphx_glr_download_advanced_dynamic_quantization_tutorial.py:

diff --git a/_sources/advanced/neural_style_tutorial.rst.txt b/_sources/advanced/neural_style_tutorial.rst.txt
@@ -410,37 +410,37 @@ network to evaluation mode using ``.eval()``.
     Downloading: "https://download.pytorch.org/models/vgg19-dcbb9e9d.pth" to /var/lib/ci-user/.cache/torch/hub/checkpoints/vgg19-dcbb9e9d.pth
 
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@@ -762,22 +762,22 @@ Finally, we can run the algorithm.
 
     Optimizing..
     run [50]:
-    Style Loss : 4.106364 Content Loss: 4.173377
+    Style Loss : 4.076185 Content Loss: 4.141464
 
     run [100]:
-    Style Loss : 1.120980 Content Loss: 3.016389
+    Style Loss : 1.117196 Content Loss: 3.007679
 
     run [150]:
-    Style Loss : 0.707181 Content Loss: 2.649149
+    Style Loss : 0.697977 Content Loss: 2.643763
 
     run [200]:
-    Style Loss : 0.471671 Content Loss: 2.487833
+    Style Loss : 0.472661 Content Loss: 2.485559
 
     run [250]:
-    Style Loss : 0.341961 Content Loss: 2.401103
+    Style Loss : 0.342390 Content Loss: 2.399799
 
     run [300]:
-    Style Loss : 0.261578 Content Loss: 2.348049
+    Style Loss : 0.261053 Content Loss: 2.347213
 
 
 
@@ -786,7 +786,7 @@ Finally, we can run the algorithm.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  37.243 seconds)
+   **Total running time of the script:** ( 0 minutes  37.280 seconds)
 
 
 .. _sphx_glr_download_advanced_neural_style_tutorial.py:

diff --git a/_sources/advanced/numpy_extensions_tutorial.rst.txt b/_sources/advanced/numpy_extensions_tutorial.rst.txt
@@ -303,7 +303,7 @@ The backward pass computes the gradient ``wrt`` the input and the gradient ``wrt
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.597 seconds)
+   **Total running time of the script:** ( 0 minutes  0.583 seconds)
 
 
 .. _sphx_glr_download_advanced_numpy_extensions_tutorial.py: