Author: Awet
If you have any questions on the code or README, please feel free to contact me.
python Pytorch Torchvision Numpy Matplotlib
Open terminal and navigate to the directory where the code exist then simply type:
python mnist_cnn_A_1.py
it will automaticaly download the mnist dataset from (http://yann.lecun.com/exdb/mnist/) and start the training
CNN: 99.9% (test accuracy) in 50 epoches Test set: Avg. loss: 3.9577173387715444e-05 [ Accuracy: 9990 / 10000 ( 99.9 %)
The code provides a predefined model. The model is CNN (convolutional neural networks). The traing and testing dataset labels are modified according "1" if the image represents the number 3, and "0" otherwise.
The network is trained and evaluated on MNIST dataset with classification accuracy.
The code use as input a MNIST image (http://yann.lecun.com/exdb/mnist/)
We split training data (60000)
Test data (10000) is already given.
T he data for CNN, is 4-dim tensor.
The CNN model contains two convolutional layers with max pooling and ReLU function. Each convolutional layer has 16 and 32 maps, respectively. Both uses 5x5 kernels. Dropout of keep probability 0.2 is used for regularization. After convolution, the activation is flattened and passes through a fully connected layer. Fully connected layer has 256 dimension.
The traing and testing dataset labels are modified according "1" if the image represents the number 3, and "0" otherwise. For every epoch, we train network by train set . We use SGD optimizer with learning_rate = 0.05, momentum = 0.9 for parameter update.
The CNN model is evaluated with test dataset
Sigmoid function outputs a value in range [0,1] which corresponds to the probability of the given sample belonging to positive class (class 3). Everything below 0.5 is labeled with zero (i.e. class other than 3) and everything above 0.5 is labeled with one. So to find the predicted class, I have used:
output = network(data) pred = output.data > 0.5
