from network import Model
x_train = ...
y_train = ...
mlp = Model()
mlp.add_layer(784, 200, act_fn='sigmoid')
mlp.add_layer(200, 128, act_fn='sigmoid')
mlp.add_layer(128, 64, act_fn='sigmoid')
mlp.add_layer(64, 10, act_fn='softmax')
mlp.fit(x_train, y_train, epochs=10)
epoch 0 loss = 0.7671066487120393 acc = 0.8809259259259259
epoch 1 loss = 0.4219997210768396 acc = 0.9203703703703704
epoch 2 loss = 0.31908042027747724 acc = 0.9366666666666666
epoch 3 loss = 0.26321430571995635 acc = 0.9472222222222222
epoch 4 loss = 0.2271466179570256 acc = 0.9533333333333334
epoch 5 loss = 0.20133359756502975 acc = 0.9577777777777777
epoch 6 loss = 0.18176775551859337 acc = 0.9618518518518518
epoch 7 loss = 0.1666178338128933 acc = 0.9662962962962963
epoch 8 loss = 0.15475931934489515 acc = 0.9688888888888889
epoch 9 loss = 0.14591911157507992 acc = 0.9718518518518519
This uses cupy instead of numpy if available to have GPU accelerated computations.
An example file demonstrating classification of Iris and MNIST data is included.