Automated tutorials push

Author: pytorchbot

_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "08a56b87",
+   "id": "f58659f7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6a1a1ff1",
+   "id": "f682f082",
    "metadata": {},
    "source": [
     "\n",

_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb

@@ -31,7 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fa407add",
+   "id": "62a8d9fa",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "798a5677",
+   "id": "8c389022",
    "metadata": {},
    "source": [
     "\n",

_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "70184db4",
+   "id": "10f283be",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fccaaa0c",
+   "id": "5bd82f1b",
    "metadata": {},
    "source": [
     "\n",

_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "87401fce",
+   "id": "44c5b791",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "30621f77",
+   "id": "bef53218",
    "metadata": {},
    "source": [
     "\n",

_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb

@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f2168b84",
+   "id": "4936e0ae",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "31b0ca73",
+   "id": "e0482226",
    "metadata": {},
    "source": [
     "\n",

_downloads/e2e556f6b4693c2cef716dd7f40caaf6/tensorboardyt_tutorial.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7e4da9f6",
+   "id": "701273da",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bbf7b3e9",
+   "id": "9c992c0a",
    "metadata": {},
    "source": [
     "\n",

_downloads/ed9d4f94afb79f7dada6742a06c486a5/autogradyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6bc61fe1",
+   "id": "a5b7eac5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5a8aecd1",
+   "id": "5cb609e3",
    "metadata": {},
    "source": [
     "\n",

_downloads/fe726e041160526cf828806536922cf6/modelsyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "81372e17",
+   "id": "fb874688",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "65055fea",
+   "id": "4b321f64",
    "metadata": {},
    "source": [
     "\n",

_images/sphx_glr_coding_ddpg_001.png

@@ -1632,26 +1632,26 @@ modules we need.
 
 
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+    reward: -4.87 (r0 = -2.06), reward eval: reward: -3.87, reward normalized=-2.87/4.29, grad norm= 94.53, loss_value= 180.18, loss_actor= 19.29, target value: -19.89: : 10400it [00:24, 258.98it/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  28.395 seconds)
+   **Total running time of the script:** ( 0 minutes  28.224 seconds)
 
 
 .. _sphx_glr_download_advanced_coding_ddpg.py:

_images/sphx_glr_dqn_with_rnn_tutorial_001.png

@@ -516,9 +516,9 @@ models run single threaded.
  .. code-block:: none
 
     loss: 5.167
-    elapsed time (seconds): 207.0
+    elapsed time (seconds): 204.4
     loss: 5.168
-    elapsed time (seconds): 117.5
+    elapsed time (seconds): 119.4
 
 
 
@@ -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  33.228 seconds)
+   **Total running time of the script:** ( 5 minutes  32.552 seconds)
 
 
 .. _sphx_glr_download_advanced_dynamic_quantization_tutorial.py:

_images/sphx_glr_neural_style_tutorial_004.png

@@ -410,38 +410,39 @@ 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 +763,22 @@ Finally, we can run the algorithm.
 
     Optimizing..
     run [50]:
-    Style Loss : 4.149775 Content Loss: 4.157228
+    Style Loss : 4.347193 Content Loss: 4.249933
 
     run [100]:
-    Style Loss : 1.104719 Content Loss: 3.005375
+    Style Loss : 1.185907 Content Loss: 3.055794
 
     run [150]:
-    Style Loss : 0.700091 Content Loss: 2.638469
+    Style Loss : 0.725471 Content Loss: 2.664198
 
     run [200]:
-    Style Loss : 0.471553 Content Loss: 2.486511
+    Style Loss : 0.489452 Content Loss: 2.500590
 
     run [250]:
-    Style Loss : 0.344631 Content Loss: 2.400878
+    Style Loss : 0.353058 Content Loss: 2.408744
 
     run [300]:
-    Style Loss : 0.263820 Content Loss: 2.348750
+    Style Loss : 0.268822 Content Loss: 2.351739
 
 
 
@@ -786,7 +787,7 @@ Finally, we can run the algorithm.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  36.749 seconds)
+   **Total running time of the script:** ( 0 minutes  36.782 seconds)
 
 
 .. _sphx_glr_download_advanced_neural_style_tutorial.py:

_images/sphx_glr_reinforcement_ppo_001.png

@@ -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  0.575 seconds)
+   **Total running time of the script:** ( 0 minutes  0.566 seconds)
 
 
 .. _sphx_glr_download_advanced_numpy_extensions_tutorial.py: