Update README.md

README.md

@@ -11,3 +11,108 @@ license: apache-2.0
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+This gradio app predicts digits using a convolutive neural network (CNN) that was trained on the MNIST hand-drawn digit data set:
+@article{lecun2010mnist,
+  title={MNIST handwritten digit database},
+  author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
+  journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},
+  volume={2},
+  year={2010}
+}
+
+The PyTorch network architecture:
+CNN(
+  (conv1): Sequential(
+    (0): Conv2d(1, 16, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
+    (1): ReLU()
+    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  )
+  (conv2): Sequential(
+    (0): Conv2d(16, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
+    (1): ReLU()
+    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
+  )
+  (out): Linear(in_features=1568, out_features=10, bias=True)
+)
+
+The model was trained using cross entropy loss function, the Adam stochastic optimizer.  Training was done for 5 epochs, using batch size of 64, and with a learning rate of 0.001.  The loss and training accuracy record was as follows:
+
+Epoch 1
+-------------------------------
+loss: 2.308622  [    0/60000]
+loss: 0.263496  [ 6400/60000]
+loss: 0.153133  [12800/60000]
+loss: 0.174262  [19200/60000]
+loss: 0.065622  [25600/60000]
+loss: 0.139496  [32000/60000]
+loss: 0.064824  [38400/60000]
+loss: 0.040185  [44800/60000]
+loss: 0.147770  [51200/60000]
+loss: 0.132756  [57600/60000]
+Test Error: 
+ Accuracy: 97.8%, Avg loss: 0.068824 
+
+Epoch 2
+-------------------------------
+loss: 0.081690  [    0/60000]
+loss: 0.140447  [ 6400/60000]
+loss: 0.024672  [12800/60000]
+loss: 0.021052  [19200/60000]
+loss: 0.093074  [25600/60000]
+loss: 0.057293  [32000/60000]
+loss: 0.059690  [38400/60000]
+loss: 0.041043  [44800/60000]
+loss: 0.008444  [51200/60000]
+loss: 0.023947  [57600/60000]
+Test Error: 
+ Accuracy: 98.8%, Avg loss: 0.037573 
+
+Epoch 3
+-------------------------------
+loss: 0.026763  [    0/60000]
+loss: 0.008322  [ 6400/60000]
+loss: 0.004648  [12800/60000]
+loss: 0.022180  [19200/60000]
+loss: 0.026884  [25600/60000]
+loss: 0.127719  [32000/60000]
+loss: 0.029921  [38400/60000]
+loss: 0.018033  [44800/60000]
+loss: 0.020504  [51200/60000]
+loss: 0.015732  [57600/60000]
+Test Error: 
+ Accuracy: 98.5%, Avg loss: 0.044061 
+
+Epoch 4
+-------------------------------
+loss: 0.001018  [    0/60000]
+loss: 0.047208  [ 6400/60000]
+loss: 0.015693  [12800/60000]
+loss: 0.025309  [19200/60000]
+loss: 0.008418  [25600/60000]
+loss: 0.047959  [32000/60000]
+loss: 0.003479  [38400/60000]
+loss: 0.049097  [44800/60000]
+loss: 0.009243  [51200/60000]
+loss: 0.033639  [57600/60000]
+Test Error: 
+ Accuracy: 98.8%, Avg loss: 0.036810 
+
+Epoch 5
+-------------------------------
+loss: 0.002880  [    0/60000]
+loss: 0.011252  [ 6400/60000]
+loss: 0.102632  [12800/60000]
+loss: 0.045496  [19200/60000]
+loss: 0.007643  [25600/60000]
+loss: 0.003103  [32000/60000]
+loss: 0.093026  [38400/60000]
+loss: 0.022969  [44800/60000]
+loss: 0.027332  [51200/60000]
+loss: 0.005514  [57600/60000]
+Test Error: 
+ Accuracy: 99.0%, Avg loss: 0.027122 
+
+Done!
+
+