modify :Pretraining _VGG from scrtach.rst

delete GENERATED

Author: woongjoonchoi

beginner_source/Pretraining_Vgg_from_scratch.rst

@@ -90,11 +90,6 @@ First, let's import the required dependencies:
 
     albumentations are already installed
 
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 VGG Configuration
 -----------------
 
@@ -103,8 +98,6 @@ We use the CIFAR100 dataset. The authors of the VGG paper scale images ``isotrop
 which means increasing the size of an image while maintaining its proportions,
 preventing distortion and maintaining the consistency of the object.
 
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 .. code-block:: default
 
 
@@ -146,17 +139,12 @@ preventing distortion and maintaining the consistency of the object.
 
     model_layers =None
 
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 .. note:: In the code above, we have defined the batch size as 32,
    which is recommended for Google Colab. However, if you are
    running this code on a machine with 24GB of GPU memory,
    you can set the batch size to 128. You can modify the batch
    size according to your preference and hardware capabilities.
 
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 Defining the dataset
 --------------------
 
@@ -182,9 +170,6 @@ thus improving its performance on both test data and in real-world applications.
 To apply preprocessing, we need to override the CIFAR100 class that we have imported from the
 ``torchvision.datasets`` with a custom class:
 
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 .. code-block:: default
 
 
@@ -240,9 +225,6 @@ To apply preprocessing, we need to override the CIFAR100 class that we have impo
             img=img.transpose((2,0,1))
             return img, target
 
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 Define Model
 ------------
 
@@ -254,8 +236,6 @@ We will use two main components to define the model:
 * ``Config_channels``: This refers to the number of output channels for each layer.
 * ``Config_kernels``: This refers to the kernel size (or filter size) for each layer.
 
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@@ -285,13 +265,8 @@ We will use two main components to define the model:
     "E":[3,3,2,3,3,2,3,3,3,3,2,3,3,3,3,2,3,3,3,3,2],
     }
 
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 Next, we define a model class that generates a model with a choice of six versions.
 
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 .. code-block:: default
 
 
@@ -300,7 +275,6 @@ Next, we define a model class that generates a model with a choice of six versio
         in_channels = 3
         i = 1
         for  out_channels , kernel in zip(cfg_c,cfg_k) :
-            # print(f"{i} th layer {out_channels} processing")
             if out_channels == "M" :
                 feature_extract += [nn.MaxPool2d(kernel,2) ]
             elif out_channels == "LRN":
@@ -372,8 +346,6 @@ Next, we define a model class that generates a model with a choice of six versio
                     self.last_xavier+=1
                 nn.init.constant_(m.bias, 0)
 
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 Initializing Model Weights
 ----------------------------
 
@@ -388,8 +360,6 @@ to initialize the model weights. Specifically, we will apply Xavier
 initialization to the first few layers and the last few layers, while using
 random initialization for the remaining layers.
 
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 .. code-block:: default
 
     # .. note::
@@ -413,15 +383,11 @@ random initialization for the remaining layers.
     # 
     # These values have been found to work well in practice.
 
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 Training the Model
 ------------------
 
 First, let's define top-k error.
 
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 .. code-block:: default
 
     def accuracy(output, target, topk=(1,)):
@@ -439,13 +405,9 @@ First, let's define top-k error.
             res.append(correct_k)
         return res
 
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 Next, we initiate the model and loss function, optimizer and schedulers. In the VGG model,
 they use a softmax output, Momentum Optimizer, and scheduling based on accuracy.
 
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 .. code-block:: default
 
     model_version='B'