def test_data_home(): # get_data_home will point to a pre-existing folder data_home = get_data_home(data_home=DATA_HOME) assert_equal(data_home, DATA_HOME) assert_true(os.path.exists(data_home)) # clear_data_home will delete both the content and the folder it-self clear_data_home(data_home=data_home) assert_false(os.path.exists(data_home)) # if the folder is missing it will be created again data_home = get_data_home(data_home=DATA_HOME) assert_true(os.path.exists(data_home))
def test_data_home(data_home): # get_data_home will point to a pre-existing folder data_home = get_data_home(data_home=data_home) assert data_home == data_home assert os.path.exists(data_home) # clear_data_home will delete both the content and the folder it-self clear_data_home(data_home=data_home) assert not os.path.exists(data_home) # if the folder is missing it will be created again data_home = get_data_home(data_home=data_home) assert os.path.exists(data_home)
with tf.Session() as sess: print(y.eval()) print(z.eval()) # 为了计算 y 和 z, w和x计算了两次 with tf.Session() as sess: y_val, z_val = sess.run([y,z]) #仅计算一次w和x,相当于节省了计算效率 print(y_val) print(z_val) #201页 import numpy as np #from sklearn.datasets import iris #housing = fetch_califonia_housing() from sklearn import datasets #清空sklearn环境下所有数据 datasets.clear_data_home() #不加return命令,可以得到dictionary,并能知道数据每列含义 boston = datasets.load_boston() boston.keys() #载入波士顿房价数据 #X,y = datasets.load_boston(return_X_y=True) m, n = boston.data.shape #np.c_指将两矩阵按列方向合并,要求行数相同,这里是加入了全为1的偏置项 boston_data_plus_bias = np.c_[np.ones((m, 1)), boston.data] X = tf.constant(boston_data_plus_bias, dtype = tf.float32, name = 'X') y = tf.constant(boston.target.reshape(-1, 1), dtype = tf.float32, name = 'y') XT = tf.transpose(X) #最小二乘法求解线性回归参数公式 θ = (XT·X)^-1·XT·y