)
for t in xrange(epoch_cycle):
print 'epoch', t
current_cost = 0
#validate_cost = 0
x_batch, y_hat_batch, batch_num = DataParser.make_batch(batch_size)
#x_batch = [[[-2.50, -1.39], [-2.50, 0.92]], [[-2.50, -1.39], [-2.49, 0.93]], [[-2.50, -1.39], [-2.48, 0.94]]]
#y_hat_batch = [[[0,1]], [[0,1]], [[0,1]]]
for i in xrange(batch_num):
c_cost = train(x_batch[i], y_hat_batch[i])
#print 'c_cost', c_cost
current_cost += c_cost
current_cost /= batch_num
print 'current_cost', current_cost
DataParser.save_parameters(parameters)
#current_validate_cost = validate(validation_set)
#print 'validate cost', current_validate_cost
#if current_validate_cost < validate_cost:
# validate_cost = current_validate_cost
#else:
# break
test_data, test_id = DataParser.load_test_data(sample_file)
result = test(test_data)
result = list(result)
result = map(list, zip(*result)) # transpose
with open('result.txt', 'w') as f:
for i in xrange(len(result)):
f.write(test_id[i] + ',')
max_value = 0
)
for t in xrange(epoch_cycle):
print 'epoch', t
current_cost = 0
#validate_cost = 0
x_batch, y_hat_batch, batch_num = DataParser.make_batch(batch_size)
#x_batch = [[[-2.50, -1.39], [-2.50, 0.92]], [[-2.50, -1.39], [-2.49, 0.93]], [[-2.50, -1.39], [-2.48, 0.94]]]
#y_hat_batch = [[[0,1]], [[0,1]], [[0,1]]]
for i in xrange(batch_num):
c_cost = train(x_batch[i], y_hat_batch[i])
#print 'c_cost', c_cost
current_cost += c_cost
current_cost /= batch_num
print 'current_cost', current_cost
DataParser.save_parameters(parameters, 'parameter.txt')
DataParser.save_parameters(grad_hists, 'parameter-g.txt')
#current_validate_cost = validate(validation_set)
#print 'validate cost', current_validate_cost
#if current_validate_cost < validate_cost:
# validate_cost = current_validate_cost
#else:
# break
test_data, test_id = DataParser.load_test_data(sample_file)
result = test(test_data)
result = list(result)
result = map(list, zip(*result)) # transpose
with open('result.txt', 'w') as f:
for i in xrange(len(result)):
f.write(test_id[i] + ',')
