VGG_MNIST

This is a project on MNIST dataset classification using tensorflow with the VGG16 network stucture.

Network Stucture

• Resize mnist images to 224*224
• 2 64-hidden_dim convolutional layers
• 1 maxpooling layer
• 2 128-hidden_dim convolutional layers
• 1 maxpooling layer
• 3 258-hidden_dim convolutional layers
• 1 maxpooling layer
• 3 512-hidden_dim convolutional layers
• 1 maxpooling layer
• 3 512-hidden_dim convolutional layers
• 1 maxpooling layer
• 1 flatten layer
• 2 dense layers with dropout
• 1 logits layer

Parameters

• Input_size = [784,], reshape to [28,28]
• Batch_size = 32
• Output_size = 10
• Learning_rate = 1e-3
• Optimizer = GradientDescentOptimizer
• Dropout_rate = 0.1
• Training_size = 55000
• Validation_size = 5000
• Testing_size = 10000

Results

[Epoch 26]

train_loss=0.000219, train_acc=0.994785

valid_loss=0.034767, valid_acc=0.994828

test_loss=0.024464, test_acc=0.994807