def NeuralNetwork(input_Nn, output_Nn):
np_input_Nn = np.array(input_Nn[0])
np_output_Nn = np.array(output_Nn[0])
x = np_input_Nn.astype(float) # .reshape(-1, 1)
y = np_output_Nn.astype(float) # .ravel()
nn = MLPRegressor(
hidden_layer_sizes=(100,), activation='tanh', solver='adam', alpha=0.0001, batch_size='auto',
learning_rate='adaptive', learning_rate_init=0.001, power_t=0.5, max_iter=1000, shuffle=True,
random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9,
nesterovs_momentum=True, early_stopping=True, validation_fraction=0.5, beta_1=0.9, beta_2=0.999,
epsilon=1e-08)
nn.n_outputs_ = 3
n = nn.fit(x, y)
# test_x = np.arange(0.0, 1, 0.05).reshape(-1, 1)
test_x = np.array([0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0])
test_y = nn.predict(test_x.reshape(1, -1))
print(test_y)
# fig = plt.figure()
print(nn.score(x, y))
def deserialize_mlp_regressor(model_dict):
model = MLPRegressor(**model_dict['params'])
model.coefs_ = model_dict['coefs_']
model.loss_ = model_dict['loss_']
model.intercepts_ = model_dict['intercepts_']
model.n_iter_ = model_dict['n_iter_']
model.n_layers_ = model_dict['n_layers_']
model.n_outputs_ = model_dict['n_outputs_']
model.out_activation_ = model_dict['out_activation_']
return model
]]),
list([[7.61211153e-01], [5.29023058e-01], [-6.76783513e-01],
[-1.23527535e-01], [1.04599422e-01], [1.06178562e+00],
[-1.09977597e-43], [-1.22990539e-90], [-3.14851814e-21],
[7.33380751e-01]])
]
net.intercepts_ = [
list([
1.44847648, 1.47542637, 0.51003163, 0.45278632, -0.0056204, 1.53020242,
-0.23453891, -0.00187764, -0.21982535, 1.69397764
]),
list([1.9355952])
]
net.n_outputs_ = 1
net.n_layers_ = 3
net.out_activation_ = "identity"
def dotProd(a, b):
return sum(a[i] * b[i] for i in xrange(len(a)))
def arrToTuple(arr):
tupArr = [tuple(elem) for elem in arr]
return tuple(tupArr)
def isEndState(state):
return state[1] == 0
# Validate input parameters.
# mlp_estimator._validate_hyperparameters() # 检查参数是否合理
if np.any(np.array(hidden_layer_sizes) <= 0):
raise ValueError("hidden_layer_sizes must be > 0, got %s." %
hidden_layer_sizes)
# X, y = mlp_estimator._validate_input(X, y, incremental)
n_samples, n_features = X.shape
# Ensure y is 2D
# TODO:保证array为两维，即输入的y应该是np.array([[1, 2, 3]])这才是1行3列的array
# if y.ndim == 1:
# y = y.reshape((-1, 1))
mlp_estimator.n_outputs_ = y.shape[1]
layer_units = ([n_features] + hidden_layer_sizes + [mlp_estimator.n_outputs_])
# check random state
mlp_estimator._random_state = check_random_state(mlp_estimator.random_state)
incremental = False
if not hasattr(mlp_estimator, 'coefs_') or (not mlp_estimator.warm_start
and not incremental):
# First time training the model
mlp_estimator._initialize(y, layer_units)
# lbfgs does not support mini-batches
if mlp_estimator.solver == 'lbfgs':
batch_size = n_samples
