add minimal example to readme

README.md

@@ -37,6 +37,51 @@ python 3.5+
 
 numpy scipy h5py sklearn dotmap
 
+## minimal example
+
+```python
+#!/usr/bin/env python3
+from optim_nn_core import *
+bs = 500
+lr = 0.0005 * np.sqrt(bs)
+reg = L1L2(3.2e-5, 3.2e-4)
+final_reg = L1L2(3.2e-5, 1e-3)
+
+def get_mnist(fn='mnist.npz'):
+    with np.load(fn) as f:
+        return f['X_train'], f['Y_train'], f['X_test'], f['Y_test']
+inputs, outputs, valid_inputs, valid_outputs = get_mnist()
+
+x = Input(shape=inputs.shape[1:])
+y = x
+y = y.feed(Flatten())
+y = y.feed(Dense(y.output_shape[0], init=init_he_normal, reg_w=reg, reg_b=reg))
+y = y.feed(Relu())
+y = y.feed(Dense(y.output_shape[0], init=init_he_normal, reg_w=reg, reg_b=reg))
+y = y.feed(Dropout(0.05))
+y = y.feed(Relu())
+y = y.feed(Dense(10, init=init_glorot_uniform, reg_w=final_reg, reg_b=final_reg))
+y = y.feed(Softmax())
+model = Model(x, y, unsafe=True)
+
+optim = Adam()
+learner = SGDR(optim, epochs=20, rate=lr, restarts=2)
+ritual = Ritual(learner=learner, loss=CategoricalCrossentropy(), mloss=Accuracy())
+ritual.prepare(model)
+while learner.next():
+    print("epoch", learner.epoch)
+    mloss, _ = ritual.train_batched(inputs, outputs, batch_size=bs, return_losses=True)
+    print("train accuracy", "{:6.2f}%".format(mloss * 100))
+
+def print_error(name, inputs, outputs):
+    loss, mloss, _, _ = ritual.test_batched(inputs, outputs, bs, return_losses='both')
+    predicted = ritual.model.forward(inputs, deterministic=True)
+    print(name + " loss", "{:12.6e}".format(loss))
+    print(name + " accuracy", "{:6.2f}%".format(mloss * 100))
+print_error("train", inputs, outputs)
+print_error("valid", valid_inputs, valid_outputs)
+```
+
 ## contributing
 
 i'm just throwing this code out there,