Automated tutorials push

Author: pytorchbot

_downloads/0662a149d54bd776924742c96eb6282d/optimization_tutorial.py

@@ -76,7 +76,7 @@ def forward(self, x):
 # (`read more <https://pytorch.org/tutorials/beginner/hyperparameter_tuning_tutorial.html>`__ about hyperparameter tuning)
 #
 # We define the following hyperparameters for training:
-#  - **Number of Epochs** - the number times to iterate over the dataset
+#  - **Number of Epochs** - the number of times to iterate over the dataset
 #  - **Batch Size** - the number of data samples propagated through the network before the parameters are updated
 #  - **Learning Rate** - how much to update models parameters at each batch/epoch. Smaller values yield slow learning speed, while large values may result in unpredictable behavior during training.
 #

_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4779a3f6",
+   "id": "78f59b54",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c5f807f2",
+   "id": "02dbf817",
    "metadata": {},
    "source": [
     "\n",

_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb

@@ -31,7 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "26c612f6",
+   "id": "06c28189",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9fce16f1",
+   "id": "53fa7317",
    "metadata": {},
    "source": [
     "\n",

_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c3439dbe",
+   "id": "6ac57dab",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cdc0c835",
+   "id": "3d76bbc3",
    "metadata": {},
    "source": [
     "\n",

_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "14152759",
+   "id": "cf6a15f8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55289e43",
+   "id": "68a2315c",
    "metadata": {},
    "source": [
     "\n",

_downloads/91d72708edab956d7293bb263e2ab53f/optimization_tutorial.ipynb

@@ -113,7 +113,7 @@
     "\n",
     "We define the following hyperparameters for training:\n",
     "\n",
-    ":   -   **Number of Epochs** - the number times to iterate over the\n",
+    ":   -   **Number of Epochs** - the number of times to iterate over the\n",
     "        dataset\n",
     "    -   **Batch Size** - the number of data samples propagated through\n",
     "        the network before the parameters are updated\n",

_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb

@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "78ac4491",
+   "id": "eb2db942",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9aa1599c",
+   "id": "72aa86af",
    "metadata": {},
    "source": [
     "\n",

_downloads/e2e556f6b4693c2cef716dd7f40caaf6/tensorboardyt_tutorial.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "744039b5",
+   "id": "dd5a3d85",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5974d4a8",
+   "id": "12b57a78",
    "metadata": {},
    "source": [
     "\n",

_downloads/ed9d4f94afb79f7dada6742a06c486a5/autogradyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7d278b80",
+   "id": "2c6f13a9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "93f682a5",
+   "id": "4c605f6a",
    "metadata": {},
    "source": [
     "\n",

_downloads/fe726e041160526cf828806536922cf6/modelsyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "58c85a0a",
+   "id": "3ef8ad90",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e275cb61",
+   "id": "cbbd2394",
    "metadata": {},
    "source": [
     "\n",

_images/sphx_glr_char_rnn_classification_tutorial_001.png

@@ -517,9 +517,9 @@ models run single threaded.
  .. code-block:: none
 
     loss: 5.167
-    elapsed time (seconds): 197.8
+    elapsed time (seconds): 197.4
     loss: 5.168
-    elapsed time (seconds): 118.4
+    elapsed time (seconds): 117.8
 
 
 
@@ -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  25.477 seconds)
+   **Total running time of the script:** ( 5 minutes  24.577 seconds)
 
 
 .. _sphx_glr_download_advanced_dynamic_quantization_tutorial.py:

_images/sphx_glr_char_rnn_classification_tutorial_002.png

@@ -410,22 +410,21 @@ 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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@@ -746,22 +745,22 @@ Finally, we can run the algorithm.
 
     Optimizing..
     run [50]:
-    Style Loss : 4.286120 Content Loss: 4.196964
+    Style Loss : 3.947716 Content Loss: 4.056622
 
     run [100]:
-    Style Loss : 1.156173 Content Loss: 3.044555
+    Style Loss : 1.137567 Content Loss: 2.997336
 
     run [150]:
-    Style Loss : 0.709581 Content Loss: 2.651251
+    Style Loss : 0.708885 Content Loss: 2.645293
 
     run [200]:
-    Style Loss : 0.473631 Content Loss: 2.488267
+    Style Loss : 0.476111 Content Loss: 2.481911
 
     run [250]:
-    Style Loss : 0.346411 Content Loss: 2.402201
+    Style Loss : 0.344080 Content Loss: 2.399487
 
     run [300]:
-    Style Loss : 0.264236 Content Loss: 2.346521
+    Style Loss : 0.262263 Content Loss: 2.346738
 
 
 
@@ -770,7 +769,7 @@ Finally, we can run the algorithm.
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  12.000 seconds)
+   **Total running time of the script:** ( 0 minutes  11.571 seconds)
 
 
 .. _sphx_glr_download_advanced_neural_style_tutorial.py:

_images/sphx_glr_neural_style_tutorial_004.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  0.371 seconds)
+   **Total running time of the script:** ( 0 minutes  0.374 seconds)
 
 
 .. _sphx_glr_download_advanced_numpy_extensions_tutorial.py:

_images/sphx_glr_reinforcement_q_learning_001.png

@@ -487,7 +487,7 @@ For more detailed information, see:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  2.370 seconds)
+   **Total running time of the script:** ( 0 minutes  2.395 seconds)
 
 
 .. _sphx_glr_download_advanced_python_custom_ops.py:

_images/sphx_glr_spatial_transformer_tutorial_001.png

@@ -70,7 +70,7 @@ model from C++.
 
     2.7.0+cu126
 
-    <torch._C.Generator object at 0x7f3bbbd9a470>
+    <torch._C.Generator object at 0x7fec1599a470>
 
 
 

_images/sphx_glr_torchvision_tutorial_002.png

@@ -113,8 +113,8 @@ documentation <https://pytorch.org/docs/stable/autograd.html#function>`__.
 
  .. code-block:: none
 
-    Gradient function for z = <AddBackward0 object at 0x7f4c13fa22c0>
-    Gradient function for loss = <BinaryCrossEntropyWithLogitsBackward0 object at 0x7f4c13fa2f80>
+    Gradient function for z = <AddBackward0 object at 0x7f6f3ac75270>
+    Gradient function for loss = <BinaryCrossEntropyWithLogitsBackward0 object at 0x7f6f3ac74dc0>
 
 
 
@@ -395,7 +395,7 @@ Further Reading
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.008 seconds)
+   **Total running time of the script:** ( 0 minutes  0.009 seconds)
 
 
 .. _sphx_glr_download_beginner_basics_autogradqs_tutorial.py:

_sources/advanced/dynamic_quantization_tutorial.rst.txt

@@ -475,7 +475,7 @@ Further Reading
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  0.514 seconds)
+   **Total running time of the script:** ( 0 minutes  0.492 seconds)
 
 
 .. _sphx_glr_download_beginner_basics_buildmodel_tutorial.py:

_sources/advanced/neural_style_tutorial.rst.txt

@@ -101,30 +101,29 @@ We load the `FashionMNIST Dataset <https://pytorch.org/vision/stable/datasets.ht
 
 
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@@ -423,7 +422,7 @@ Further Reading
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  4.918 seconds)
+   **Total running time of the script:** ( 0 minutes  4.908 seconds)
 
 
 .. _sphx_glr_download_beginner_basics_data_tutorial.py: