Automated tutorials push

Author: pytorchbot

_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "aa160397",
+   "id": "03d00eb0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "190f9950",
+   "id": "a2258b92",
    "metadata": {},
    "source": [
     "\n",

_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb

@@ -31,7 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "48d2c3c7",
+   "id": "4e1876e7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "21d0685d",
+   "id": "57340e2a",
    "metadata": {},
    "source": [
     "\n",

_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c1aebcb0",
+   "id": "c82232a1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0e2aaa9c",
+   "id": "aafac980",
    "metadata": {},
    "source": [
     "\n",

_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c38505ba",
+   "id": "5e949b91",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "78c5656f",
+   "id": "3593a657",
    "metadata": {},
    "source": [
     "\n",

_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb

@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6a17d7ee",
+   "id": "241f94be",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a17e29f",
+   "id": "eb62a5d6",
    "metadata": {},
    "source": [
     "\n",

_downloads/e2e556f6b4693c2cef716dd7f40caaf6/tensorboardyt_tutorial.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "84ecc7cc",
+   "id": "6881b39d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "580590a2",
+   "id": "4cb84077",
    "metadata": {},
    "source": [
     "\n",

_downloads/ed9d4f94afb79f7dada6742a06c486a5/autogradyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c18fa265",
+   "id": "5a85e8ec",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "77d9b723",
+   "id": "01153738",
    "metadata": {},
    "source": [
     "\n",

_downloads/fe726e041160526cf828806536922cf6/modelsyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "277d217e",
+   "id": "a6756670",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c51520d0",
+   "id": "52962b28",
    "metadata": {},
    "source": [
     "\n",

_images/sphx_glr_char_rnn_classification_tutorial_001.png

@@ -1634,26 +1634,26 @@ modules we need.
 
 
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-    reward: -0.12 (r0 = -2.61), reward eval: reward: -0.01, reward normalized=-2.23/5.55, grad norm= 59.75, loss_value= 249.44, loss_actor= 13.52, target value: -14.12:  64%|######4   | 6400/10000 [00:08<00:04, 813.08it/s]
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+    reward: -4.79 (r0 = -3.32), reward eval: reward: -5.77, reward normalized=-4.03/4.05, grad norm= 168.97, loss_value= 157.23, loss_actor= 23.69, target value: -28.33: : 10400it [00:20, 348.57it/s]
 
 
 
@@ -1723,7 +1723,7 @@ To iterate further on this loss module we might consider:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  31.807 seconds)
+   **Total running time of the script:** ( 0 minutes  31.669 seconds)
 
 
 .. _sphx_glr_download_advanced_coding_ddpg.py:

_images/sphx_glr_char_rnn_classification_tutorial_002.png

@@ -517,9 +517,9 @@ models run single threaded.
  .. code-block:: none
 
     loss: 5.167
-    elapsed time (seconds): 207.8
+    elapsed time (seconds): 204.3
     loss: 5.168
-    elapsed time (seconds): 118.5
+    elapsed time (seconds): 115.2
 
 
 
@@ -541,7 +541,7 @@ 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  34.789 seconds)
+   **Total running time of the script:** ( 5 minutes  27.712 seconds)
 
 
 .. _sphx_glr_download_advanced_dynamic_quantization_tutorial.py:

_images/sphx_glr_coding_ddpg_001.png

@@ -410,32 +410,20 @@ 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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@@ -756,22 +744,22 @@ Finally, we can run the algorithm.
 
     Optimizing..
     run [50]:
-    Style Loss : 4.072629 Content Loss: 4.158342
+    Style Loss : 4.094805 Content Loss: 4.188426
 
     run [100]:
-    Style Loss : 1.157763 Content Loss: 3.050233
+    Style Loss : 1.117979 Content Loss: 3.008747
 
     run [150]:
-    Style Loss : 0.714200 Content Loss: 2.651302
+    Style Loss : 0.712450 Content Loss: 2.650858
 
     run [200]:
-    Style Loss : 0.489686 Content Loss: 2.496655
+    Style Loss : 0.476656 Content Loss: 2.489839
 
     run [250]:
-    Style Loss : 0.354790 Content Loss: 2.407404
+    Style Loss : 0.344013 Content Loss: 2.401936
 
     run [300]:
-    Style Loss : 0.268439 Content Loss: 2.352382
+    Style Loss : 0.264354 Content Loss: 2.349211
 
 
 
@@ -780,7 +768,7 @@ Finally, we can run the algorithm.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  36.042 seconds)
+   **Total running time of the script:** ( 0 minutes  11.342 seconds)
 
 
 .. _sphx_glr_download_advanced_neural_style_tutorial.py:

_images/sphx_glr_dqn_with_rnn_tutorial_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  0.591 seconds)
+   **Total running time of the script:** ( 0 minutes  0.377 seconds)
 
 
 .. _sphx_glr_download_advanced_numpy_extensions_tutorial.py: