Automated tutorials push

Author: pytorchbot

_downloads/0e4c2becda3dfc54e1816634d49f8e73/introyt1_tutorial.py

@@ -303,22 +303,21 @@ def num_flat_features(self, x):
 #    The values passed to the transform are the means (first tuple) and the
 #    standard deviations (second tuple) of the rgb values of the images in
 #    the dataset. You can calculate these values yourself by running these
-#    few lines of code:
-#          ```
-#           from torch.utils.data import ConcatDataset
-#           transform = transforms.Compose([transforms.ToTensor()])
-#           trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
+#    few lines of code::
+#
+#        from torch.utils.data import ConcatDataset
+#        transform = transforms.Compose([transforms.ToTensor()])
+#        trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
 #                                        download=True, transform=transform)
 #
-#           #stack all train images together into a tensor of shape 
-#           #(50000, 3, 32, 32)
-#           x = torch.stack([sample[0] for sample in ConcatDataset([trainset])])
+#        # stack all train images together into a tensor of shape 
+#        # (50000, 3, 32, 32)
+#        x = torch.stack([sample[0] for sample in ConcatDataset([trainset])])
 #           
-#           #get the mean of each channel            
-#           mean = torch.mean(x, dim=(0,2,3)) #tensor([0.4914, 0.4822, 0.4465])
-#           std = torch.std(x, dim=(0,2,3)) #tensor([0.2470, 0.2435, 0.2616])  
-# 
-#          ```   
+#        # get the mean of each channel            
+#        mean = torch.mean(x, dim=(0,2,3)) # tensor([0.4914, 0.4822, 0.4465])
+#        std = torch.std(x, dim=(0,2,3)) # tensor([0.2470, 0.2435, 0.2616])  
+#    
 # 
 # There are many more transforms available, including cropping, centering,
 # rotation, and reflection.

_downloads/3195443a0ced3cabc0ad643537bdb5cd/introyt1_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "2a47f826",
+   "id": "91ce1b03",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b473eeee",
+   "id": "7e2c89af",
    "metadata": {},
    "source": [
     "\n",
@@ -445,6 +445,7 @@
     "\n",
     "-   `transforms.ToTensor()` converts images loaded by Pillow into\n",
     "    PyTorch tensors.\n",
+    "\n",
     "-   `transforms.Normalize()` adjusts the values of the tensor so that\n",
     "    their average is zero and their standard deviation is 1.0. Most\n",
     "    activation functions have their strongest gradients around x = 0, so\n",
@@ -453,7 +454,19 @@
     "    deviations (second tuple) of the rgb values of the images in the\n",
     "    dataset. You can calculate these values yourself by running these\n",
     "    few lines of code:\n",
-    "    `` `        from torch.utils.data import ConcatDataset        transform = transforms.Compose([transforms.ToTensor()])        trainset = torchvision.datasets.CIFAR10(root='./data', train=True,                                     download=True, transform=transform)         #stack all train images together into a tensor of shape         #(50000, 3, 32, 32)        x = torch.stack([sample[0] for sample in ConcatDataset([trainset])])         #get the mean of each channel                    mean = torch.mean(x, dim=(0,2,3)) #tensor([0.4914, 0.4822, 0.4465])        std = torch.std(x, dim=(0,2,3)) #tensor([0.2470, 0.2435, 0.2616]) ``\\`\n",
+    "\n",
+    "        from torch.utils.data import ConcatDataset\n",
+    "        transform = transforms.Compose([transforms.ToTensor()])\n",
+    "        trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
+    "                                        download=True, transform=transform)\n",
+    "\n",
+    "        # stack all train images together into a tensor of shape \n",
+    "        # (50000, 3, 32, 32)\n",
+    "        x = torch.stack([sample[0] for sample in ConcatDataset([trainset])])\n",
+    "\n",
+    "        # get the mean of each channel            \n",
+    "        mean = torch.mean(x, dim=(0,2,3)) # tensor([0.4914, 0.4822, 0.4465])\n",
+    "        std = torch.std(x, dim=(0,2,3)) # tensor([0.2470, 0.2435, 0.2616])  \n",
     "\n",
     "There are many more transforms available, including cropping, centering,\n",
     "rotation, and reflection.\n",

_downloads/4355e2cef7d17548f1e25f97a62828c4/template_tutorial.ipynb

@@ -31,7 +31,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1cc95491",
+   "id": "26a1f870",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d9e63f5",
+   "id": "2d0e0bdc",
    "metadata": {},
    "source": [
     "\n",

_downloads/63a0f0fc7b3ffb15d3a5ac8db3d521ee/tensors_deeper_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f07dd1e4",
+   "id": "fcf61cdc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0fb1c83e",
+   "id": "33507b4e",
    "metadata": {},
    "source": [
     "\n",

_downloads/770632dd3941d2a51b831c52ded57aa2/trainingyt.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "fffd7390",
+   "id": "e0923e32",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "12eb4b10",
+   "id": "3b2b3c77",
    "metadata": {},
    "source": [
     "\n",

_downloads/c28f42852d456daf9af72da6c6909556/captumyt.ipynb

@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b08647f2",
+   "id": "74c62868",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bb195516",
+   "id": "358826a7",
    "metadata": {},
    "source": [
     "\n",

_downloads/e2e556f6b4693c2cef716dd7f40caaf6/tensorboardyt_tutorial.ipynb

@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0602192d",
+   "id": "be005286",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "299047e0",
+   "id": "c0e01774",
    "metadata": {},
    "source": [
     "\n",

_downloads/ed9d4f94afb79f7dada6742a06c486a5/autogradyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "34c9794a",
+   "id": "0813ff8d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0262f5ab",
+   "id": "79c60e38",
    "metadata": {},
    "source": [
     "\n",

_downloads/fe726e041160526cf828806536922cf6/modelsyt_tutorial.ipynb

@@ -34,7 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f4f0f8be",
+   "id": "129f8492",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c8220900",
+   "id": "814173f7",
    "metadata": {},
    "source": [
     "\n",