Convolution mnist

Convolution neuron network mnist example.

from miml import datasets
from miml.preprocessing import MinMaxScaler
from miml.model_selection import train_test_split
from miml.deep_learning import Network, InputType, Optimizer, Adam
from miml.deep_learning import Activation, LossFunction, WeightInit
from miml.deep_learning import DenseLayer, OutputLayer, ConvolutionLayer,\
    SubsamplingLayer

print('Read data...')
fn = 'D:/Temp/machine_learning/mnist_784.arff'
mnist = datasets.load_arff(fn, ridx=784)
X = mnist.x
y = mnist.y

print('Train/Test split...')
X_train, X_test, y_train, y_test = train_test_split(X,
    y, test_size=0.4, random_state=0)

print(X_train.shape, y_train.shape)
print(X_test.shape, y_test.shape)

print('MinMax scaling...')
scaler = MinMaxScaler(feature_range=(0, 1))
X_train = scaler.fit_transform(X_train.flatten()).reshape(X_train.shape)
X_test = scaler.fit_transform(X_test.flatten()).reshape(X_test.shape)

print('Build neural network...')
model = Network(seed=123, l2=0.0005, weight_init=WeightInit.XAVIER,
    updater=Adam(1e-3), input_type=InputType.convolutionalFlat(28,28,1))
model.add(ConvolutionLayer(kernel_size=(5,5), nin=1, stride=(1,1), nout=20,
    activation=Activation.IDENTITY))
model.add(SubsamplingLayer(pooling_type='max', kernel_size=(2,2), stride=(2,2)))
model.add(ConvolutionLayer(kernel_size=(5,5), stride=(1,1), nout=50,
    activation=Activation.IDENTITY))
model.add(SubsamplingLayer(pooling_type='max', kernel_size=(2,2), stride=(2,2)))
model.add(DenseLayer(nout=500, activation=Activation.RELU))
model.add(OutputLayer(loss=LossFunction.NEGATIVELOGLIKELIHOOD, nin=500, nout=10,
    activation=Activation.SOFTMAX))
model.compile()

print('Fit model...')
nepochs = 1
model.fit(X_train, y_train, epochs=nepochs, batchsize=64)

print('Model evaluation...')
meval = model.eval(X_test, y_test, batchsize=64)
print(meval)