cifar10.py 文件源码

def basic_conv(n=3, epochs=60):
    nets = []  # list of networks (for ensemble, if desired)
    for j in range(n):
        net = Network([
            ConvPoolLayer(image_shape=(mini_batch_size, 3, 32, 32), 
                          filter_shape=(32, 3, 3, 3), stride=(1, 1),
                          poolsize=(2, 2), activation_fn=relu),
            ConvPoolLayer(image_shape=(mini_batch_size, 32, 16, 16), 
                          filter_shape=(80, 32, 3, 3), stride=(1, 1),
                          poolsize=(2, 2), activation_fn=relu),
            ConvPoolLayer(image_shape=(mini_batch_size, 80, 8, 8), 
                          filter_shape=(128, 80, 3, 3), stride=(1, 1),
                          poolsize=(2, 2), activation_fn=relu),
            FullyConnectedLayer(n_in=128*4*4, n_out=100),
            SoftmaxLayer(n_in=100, n_out=10)], mini_batch_size)

        net.SGD(train_data, epochs, mini_batch_size, 0.01,
                validation_data, test_data)

        nets.append(net)  # Add current network to list
    return nets