import numpy as np import tensorflow as tf import matplotlib.pyplot as plt train_x = np.linspace(-1, 1, 100) print(len(train_x), 'len(train_x)') # 100 len(train_x) print(type(train_x), 'type(train_x)') # <class 'numpy.ndarray'> type(train_x) print(train_x, 'train_x') ''' [-1. -0.97979798 -0.95959596 -0.93939394 -0.91919192 -0.8989899 -0.87878788 -0.85858586 -0.83838384 -0.81818182 -0.7979798 -0.77777778 -0.75757576 -0.73737374 -0.71717172 -0.6969697 -0.67676768 -0.65656566 -0.63636364 -0.61616162 -0.5959596 -0.57575758 -0.55555556 -0.53535354 -0.51515152 -0.49494949 -0.47474747 -0.45454545 -0.43434343 -0.41414141 -0.39393939 -0.37373737 -0.35353535 -0.33333333 -0.31313131 -0.29292929 -0.27272727 -0.25252525 -0.23232323 -0.21212121 -0.19191919 -0.17171717 -0.15151515 -0.13131313 -0.11111111 -0.09090909 -0.07070707 -0.05050505 -0.03030303 -0.01010101 0.01010101 0.03030303 0.05050505 0.07070707 0.09090909 0.11111111 0.13131313 0.15151515 0.17171717 0.19191919 0.21212121 0.23232323 0.25252525 0.27272727 0.29292929 0.31313131 0.33333333 0.35353535 0.37373737 0.39393939 0.41414141 0.43434343 0.45454545 0.47474747 0.49494949 0.51515152 0.53535354 0.55555556 0.57575758 0.5959596 0.61616162 0.63636364 0.65656566 0.67676768 0.6969697 0.71717172 0.73737374 0.75757576 0.77777778 0.7979798 0.81818182 0.83838384 0.85858586 0.87878788 0.8989899 0.91919192 0.93939394 0.95959596 0.97979798 1. ] train_x ''' train_y = 2 * train_x + np.random.randn(*train_x.shape) * 0.3 print(train_y, 'train_y') ''' [-1.97537371 -2.0707564 -2.50659017 -1.74005574 -1.7182739 -1.69568339 -2.17685749 -1.65533878 -1.81077207 -1.24474538 -1.47408266 -1.78770071 -2.55034481 -1.09649091 -0.83456965 -1.12748184 -1.60790972 -1.44289216 -1.11855699 -0.87314642 -1.01221576 -1.42516523 -0.88050722 -1.490482 -1.72149092 -0.70313579 -0.71962395 -0.98758657 -0.38254856 -0.93820301 -0.74938492 -0.58497603 -0.87109708 -0.4070504 0.21718465 0.06411439 -0.10974623 -0.5450655 -0.7198069 -0.36340493 -0.55079501 -0.37930734 -0.33364772 0.01452697 -0.20080607 -0.1647803 -0.36632996 -0.52541821 -0.0417619 -0.10118491 -0.26923789 0.23949143 0.15637785 0.03223219 0.24792483 -0.06720146 0.88348099 0.17352677 0.20654782 0.32752297 -0.0221242 1.11004173 0.543269 0.77831015 1.12086169 0.22127075 0.32665351 0.4745935 1.68278184 0.68084667 0.92837247 1.00389364 0.8130549 0.58524667 1.0982213 1.04425041 0.9826977 1.22104511 1.74847155 0.93826344 1.4680691 1.68145267 1.52103978 1.9941887 1.48961483 0.84978128 1.18871248 1.92147679 1.38878515 1.94890224 1.62084605 1.77141107 1.88397044 1.8813066 1.95174276 1.43820094 1.89797614 1.85152474 2.05707149 2.17824911] train_y ''' plt.plot(train_x, train_y, 'ro', label = 'Original data') plt.legend() plt.show()
