removed batchnorm2d

arun477 · Sep 15, 2023 · 338bbe8 · 338bbe8
1 parent 6c5990e
commit 338bbe8
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Showing 7 changed files with 256 additions and 76 deletions.
diff --git a/README.md b/README.md
@@ -1,7 +1,7 @@
 # <img src="/static/favicon.png" alt="Logo" style="float: left; margin-right: 10px; border-radius:100%;margin-top:5px" />  MNIST CLASSIFIER
 MNIST classifier from scratch
 * Model: CNN
-* Accuracy: 98%
+* Accuracy: 94%
 
 * Training Notebook: mnist_classifier.ipynb
 * Cleaned Python Inference Version: mnist_classifier.py

diff --git a/__pycache__/mnist_classifier.cpython-39.pyc b/__pycache__/mnist_classifier.cpython-39.pyc
diff --git a/__pycache__/server.cpython-39.pyc b/__pycache__/server.cpython-39.pyc
diff --git a/classifier.pth b/classifier.pth
diff --git a/mnist_classifier.ipynb b/mnist_classifier.ipynb
diff --git a/mnist_classifier.py b/mnist_classifier.py
@@ -11,7 +11,6 @@
 import torchvision.transforms.functional as TF
 from torch.utils.data import default_collate, DataLoader
 import torch.optim as optim
-import pickle
 
 
 def transform_ds(b):
@@ -23,7 +22,7 @@ def transform_ds(b):
 class DataLoaders:
     def __init__(self, train_ds, valid_ds, bs, collate_fn, **kwargs):
         self.train = DataLoader(train_ds, batch_size=bs, shuffle=True, collate_fn=collate_fn, **kwargs)
-        self.valid = DataLoader(valid_ds, batch_size=bs*2, shuffle=False, collate_fn=collate_fn, **kwargs)
+        self.valid = DataLoader(valid_ds, batch_size=bs, shuffle=False, collate_fn=collate_fn, **kwargs)
 
 def collate_fn(b):
     collate = default_collate(b)
@@ -65,13 +64,24 @@ def forward(self, x):
         return self.act(self.convs(x) + self.idconv(self.pool(x)))
 
 
+# def cnn_classifier():
+#     return nn.Sequential(
+#         ResBlock(1, 8, norm=nn.BatchNorm2d(8)),
+#         ResBlock(8, 16, norm=nn.BatchNorm2d(16)),
+#         ResBlock(16, 32, norm=nn.BatchNorm2d(32)),
+#         ResBlock(32, 64, norm=nn.BatchNorm2d(64)),
+#         ResBlock(64, 64, norm=nn.BatchNorm2d(64)),
+#         conv(64, 10, act=False),
+#         nn.Flatten(),
+#     )
+
 def cnn_classifier():
     return nn.Sequential(
-        ResBlock(1, 8, norm=nn.BatchNorm2d(8)),
-        ResBlock(8, 16, norm=nn.BatchNorm2d(16)),
-        ResBlock(16, 32, norm=nn.BatchNorm2d(32)),
-        ResBlock(32, 64, norm=nn.BatchNorm2d(64)),
-        ResBlock(64, 64, norm=nn.BatchNorm2d(64)),
+        ResBlock(1, 8,),
+        ResBlock(8, 16, ),
+        ResBlock(16, 32,),
+        ResBlock(32, 64, ),
+        ResBlock(64, 64,),
         conv(64, 10, act=False),
         nn.Flatten(),
     )
@@ -83,7 +93,7 @@ def kaiming_init(m):
 
 
 loaded_model = cnn_classifier()
-loaded_model.load_state_dict(torch.load('classifier.pth'))
+loaded_model.load_state_dict(torch.load('classifier.pth'));
 loaded_model.eval();
 
 

diff --git a/server.py b/server.py
@@ -6,6 +6,7 @@
 from pathlib import Path
 import torchvision.transforms as transforms
 import mnist_classifier
+import torch
 
 app = FastAPI()