def testBoston(self):
random.seed(42)
boston = datasets.load_boston()
regressor = learn.LinearRegressor(
feature_columns=learn.infer_real_valued_columns_from_input(boston.data))
regressor.fit(boston.data, boston.target, max_steps=500)
score = mean_squared_error(boston.target, regressor.predict(boston.data))
self.assertLess(score, 150, "Failed with score = {0}".format(score))
def testBoston(self):
random.seed(42)
boston = datasets.load_boston()
regressor = learn.TensorFlowLinearRegressor(batch_size=boston.data.shape[0],
steps=500,
learning_rate=0.001)
regressor.fit(boston.data, boston.target)
score = mean_squared_error(boston.target, regressor.predict(boston.data))
self.assertLess(score, 150, "Failed with score = {0}".format(score))
def testBoston(self):
random.seed(42)
boston = datasets.load_boston()
regressor = learn.LinearRegressor(
feature_columns=learn.infer_real_valued_columns_from_input(boston.data))
regressor.fit(boston.data, boston.target, max_steps=500)
score = mean_squared_error(
boston.target, np.array(list(regressor.predict(boston.data))))
self.assertLess(score, 150, "Failed with score = {0}".format(score))
def loaddata():
"""
获取到数据集
:return:
"""
boston_data = datasets.load_boston()
X_train, Y_train = boston_data.data[:, 5], boston_data.target
# X_train=normaize(X_train)
n_samples = len(X_train)
return (X_train, Y_train, n_samples)
def multiple_linear_regression():
"""
多特征的线性回归模型
:return:
"""
boston_data = datasets.load_boston()
X_train, Y_train = boston_data.data, boston_data.target
n_samples = len(X_train)
# 将偏差作为一列特征添加进去
X_train = np.c_[X_train, np.zeros(shape=(n_samples, 1))]
n_features = X_train.shape[1]
X_train = (X_train - np.mean(X_train)) / np.std(X_train)
Y_train = np.reshape(Y_train, newshape=(n_samples, 1))
# TODO 暂时不考虑偏差，考虑偏差就是把偏差也当做一个特征值
X = tf.placeholder(dtype=tf.float32,
shape=(n_samples, n_features),
name="X")
Y = tf.placeholder(dtype=tf.float32, shape=(n_samples, 1), name="Y")
w = tf.Variable(tf.random_normal(shape=(n_features, 1)), name="w")
Y_that = tf.matmul(X, w)
loss = tf.reduce_mean(tf.square(Y - Y_that), name="loss")
optimizer = tf.train.GradientDescentOptimizer(
0.001, name="optimizer").minimize(loss)
linear_ops = tf.global_variables_initializer()
total = []
with tf.Session() as sess:
sess.run(linear_ops)
writer = tf.summary.FileWriter("multiplinear_regression", sess.graph)
for i in range(1000):
_, total_loss = sess.run([optimizer, loss],
feed_dict={
X: X_train,
Y: Y_train
})
total.append(total_loss)
logger.info("epoll {0} loss {1}".format(i, total_loss / n_samples))
writer.close()
w_value = sess.run(w)
writer.close()
show_loss(total)
# 预测
n = 500
Y_pred = np.matmul(X_train[n, :], w_value)
logger.info("pred {0} real {1}".format(Y_pred[0], Y_train[n][0]))
def testBostonDNN(self):
random.seed(42)
boston = datasets.load_boston()
regressor = learn.TensorFlowDNNRegressor(
hidden_units=[10, 20, 10], n_classes=0, batch_size=boston.data.shape[0], steps=200, learning_rate=0.001
)
regressor.fit(boston.data, boston.target)
score = mean_squared_error(boston.target, regressor.predict(boston.data))
self.assertLess(score, 110, "Failed with score = {0}".format(score))
weights = regressor.weights_
self.assertEqual(weights[0].shape, (13, 10))
self.assertEqual(weights[1].shape, (10, 20))
self.assertEqual(weights[2].shape, (20, 10))
self.assertEqual(weights[3].shape, (10, 1))
biases = regressor.bias_
self.assertEqual(len(biases), 4)
def testBostonDNN(self):
random.seed(42)
boston = datasets.load_boston()
regressor = learn.TensorFlowDNNRegressor(hidden_units=[10, 20, 10],
n_classes=0,
batch_size=boston.data.shape[0],
steps=200,
learning_rate=0.001)
regressor.fit(boston.data, boston.target)
score = mean_squared_error(boston.target, regressor.predict(boston.data))
self.assertLess(score, 110, "Failed with score = {0}".format(score))
weights = regressor.weights_
self.assertEqual(weights[0].shape, (13, 10))
self.assertEqual(weights[1].shape, (10, 20))
self.assertEqual(weights[2].shape, (20, 10))
self.assertEqual(weights[3].shape, (10, 1))
biases = regressor.bias_
self.assertEqual(len(biases), 4)
def testBoston(self):
boston = datasets.load_boston()
self.assertTupleEqual(boston.data.shape, (506, 13))
self.assertTupleEqual(boston.target.shape, (506, ))
def testBoston(self): boston = datasets.load_boston() self.assertTupleEqual(boston.data.shape, (506, 13)) self.assertTupleEqual(boston.target.shape, (506,))
