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
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