for i in range(10):
seed = random.randint(-65536, 65535) # -13920 good seed
random.seed(seed)
print("seed : ", seed)
nn = NeuralNetwork(neuronsPerLayer=[31, 31, 1])
kb = []
testKB = []
for i in range(10000):
kb.append((generateDispensableCard(), [1]))
kb.append((generateIndispensableCard(), [0]))
for i in range(1000):
testKB.append((generateDispensableCard(), [1]))
testKB.append((generateIndispensableCard(), [0]))
untrainedErrorOnKB = nn.test(knowledgeBase=kb)
print("untrainedErrorOnKB : ", untrainedErrorOnKB)
untrainedErrorOnTest = nn.test(knowledgeBase=testKB)
print("untrainedErrorOnTest : ", untrainedErrorOnTest)
for i in range(1000):
test = False
if not i % 20:
test = True
trainedErrorOnKBi = nn.train(knowledgeBase=kb, doTests=test)
print("trainedErrorOnKB", i, " :", trainedErrorOnKBi)
else:
trainedErrorOnKBi = nn.train(knowledgeBase=kb, doTests=test)
trainedErrorOnKB1000 = nn.test(knowledgeBase=kb)
cost = ((test_y['pred_survived'] - test_y['Survived'])**2).mean()
test_y = test_y[['pred_survived']]
test_y.columns = ['Survived']
return test_y, cost
if __name__ == '__main__':
x, y = prep_train_data()
test_x, test_y = prep_test_data()
h_layers = [10, 10]
inputs = x.values
outputs = y.values
nn = NeuralNetwork(n_inputs=4,
n_outputs=2,
h_layers=h_layers,
inputs=inputs,
expected_outputs=outputs)
nn.open_session()
nn.make_model(is_weights_rand=True)
nn.make_tensor_board()
s = time.time()
nn.train(epochs=999, learning_rate=0.01, isliveplot=False)
raw_outputs, _ = nn.test(test_inputs=test_x.values,
test_outputs=test_y.values)
test_y, cost = categorize_output(raw_outputs, test_y)
test_y.to_csv('./data/my_submission.csv')
print('cost: ', cost)
e = time.time() - s
print("Training took ", e, "seconds")
nn.close_session()
nn.plot()