dynet xor demo [python version]

import dynet as dy
import random
HIDDEN_SIZE = 20
NUM_EPOCHS = 20
# Define the model and SGD optimizer
model = dy.Model()
W_xh_p = model.add_parameters((HIDDEN_SIZE, 2))
b_h_p = model.add_parameters(HIDDEN_SIZE)
W_hy_p = model.add_parameters((1, HIDDEN_SIZE))
b_y_p = model.add_parameters(1)
trainer = dy.SimpleSGDTrainer(model)
# Define the training data, consisting of (x,y) tuples
data = [([1,1],1), ([-1,1],-1), ([1,-1],-1), ([-1,-1],1)]
# Define the function we would like to calculate
def (x):
	dy.renew_cg()
	w_xh = dy.parameter(W_xh_p)
	b_h = dy.parameter(b_h_p)
	W_hy = dy.parameter(W_hy_p)
	b_y = dy.parameter(b_y_p)
	x_val = dy.inputVector(x)
	h_val = dy.tanh(w_xh * x_val + b_h)
	y_val = W_hy * h_val + b_y
	return y_val
# Perform training
for epoch in range(NUM_EPOCHS):
	epoch_loss = 0
	random.shuffle(data)
	for x, ystar in data:
		y = calc_function(x)
		loss = dy.squared_distance(y, dy.scalarInput(ystar))
		epoch_loss += loss.value()
		loss.backward()
		trainer.update()
	print("Epoch %d: loss=%f" % (epoch, epoch_loss))
# Print results of prediction
for x, ystar in data:
	y = calc_function(x)
	print("%r -> %f" % (x, y.value()))