Tensorflow简单实例2
2019-09-20 17:09
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本文链接:https://blog.csdn.net/qq_38905818/article/details/101067590
目标:曲线拟合
[code]import tensorflow as tf import numpy as np import matplotlib.pyplot as plt # 生成200个随机点以及噪音 x_data = np.linspace(-0.5, 0.5, 200)[:, np.newaxis] noise = np.random.normal(0, 0.02, x_data.shape) y_data = np.square(x_data) + noise x = tf.placeholder(tf.float32, [None, 1]) y = tf.placeholder(tf.float32, [None, 1]) # 定义神经网络中间层 Weights_L1 = tf.Variable(tf.random.normal([1, 10])) biases_L1 = tf.Variable(tf.zeros([1, 10])) Wx_plus_b_L1 = tf.matmul(x, Weights_L1) + biases_L1 L1 = tf.nn.tanh(Wx_plus_b_L1) # 输出层 Weights_L2 = tf.Variable(tf.random_normal([10, 1])) biases_L2 = tf.Variable(tf.zeros([1, 1])) Wx_plus_b_L2 = tf.matmul(L1, Weights_L2) + biases_L2 prediction = tf.nn.tanh(Wx_plus_b_L2) # 二次代价函数 loss = tf.reduce_mean(tf.square(y - prediction)) # 梯度下降算法 train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for _ in range(2000): sess.run(train_step, feed_dict={x: x_data, y: y_data}) prediction_value = sess.run(prediction, feed_dict={x: x_data}) plt.figure() plt.scatter(x_data, y_data) plt.plot(x_data, prediction_value, "r-", lw=5) plt.show()
结果:
[code]x_data prediction [-0.5] [0.19294329] [-0.49497487] [0.19175923] [-0.48994975] [0.19051863] [-0.48492462] [0.18922187] [-0.4798995] [0.1878693] [-0.47487437] [0.18646146] [-0.46984925] [0.18499872] [-0.46482412] [0.18348157] [-0.45979899] [0.1819106] [-0.45477387] [0.18028645] [-0.44974874] [0.17860968] [-0.44472362] [0.17688093] [-0.43969849] [0.17510127] [-0.43467337] [0.173271] [-0.42964824] [0.17139119] [-0.42462312] [0.16946252] [-0.41959799] [0.16748609] [-0.41457286] [0.16546267] [-0.40954774] [0.16339341] [-0.40452261] [0.16127916] [-0.39949749] [0.15912119] [-0.39447236] [0.15692042] [-0.38944724] [0.15467812] [-0.38442211] [0.15239535] [-0.37939698] [0.15007356] [-0.37437186] [0.1477139] [-0.36934673] [0.14531776] [-0.36432161] [0.14288644] [-0.35929648] [0.1404215] [-0.35427136] [0.13792416] [-0.34924623] [0.13539611] [-0.34422111] [0.13283871] [-0.33919598] [0.13025367] [-0.33417085] [0.12764248] [-0.32914573] [0.1250069] [-0.3241206] [0.1223486] [-0.31909548] [0.11966904] [-0.31407035] [0.11697014] [-0.30904523] [0.11425359] [-0.3040201] [0.11152133] [-0.29899497] [0.1087749] [-0.29396985] [0.10601646] [-0.28894472] [0.10324754] [-0.2839196] [0.10047022] [-0.27889447] [0.09768649] [-0.27386935] [0.09489794] [-0.26884422] [0.0921068] [-0.2638191] [0.08931491] [-0.25879397] [0.08652426] [-0.25376884] [0.08373679] [-0.24874372] [0.08095439] [-0.24371859] [0.07817917] [-0.23869347] [0.07541309] [-0.23366834] [0.07265802] [-0.22864322] [0.06991606] [-0.22361809] [0.06718923] [-0.21859296] [0.06447934] [-0.21356784] [0.06178849] [-0.20854271] [0.05911853] [-0.20351759] [0.05647144] [-0.19849246] [0.05384925] [-0.19346734] [0.05125373] [-0.18844221] [0.04868681] [-0.18341709] [0.04615049] [-0.17839196] [0.04364644] [-0.17336683] [0.04117669] [-0.16834171] [0.03874289] [-0.16331658] [0.03634687] [-0.15829146] [0.03399033] [-0.15326633] [0.03167516] [-0.14824121] [0.02940293] [-0.14321608] [0.02717526] [-0.13819095] [0.02499384] [-0.13316583] [0.02286028] [-0.1281407] [0.0207761] [-0.12311558] [0.01874267] [-0.11809045] [0.01676186] [-0.11306533] [0.01483469] [-0.1080402] [0.01296279] [-0.10301508] [0.01114744] [-0.09798995] [0.00938991] [-0.09296482] [0.00769159] [-0.0879397] [0.00605358] [-0.08291457] [0.0044771] [-0.07788945] [0.00296333] [-0.07286432] [0.00151332] [-0.0678392] [0.00012813] [-0.06281407] [-0.00119129] [-0.05778894] [-0.00244397] [-0.05276382] [-0.00362901] [-0.04773869] [-0.00474551] [-0.04271357] [-0.0057927] [-0.03768844] [-0.00676977] [-0.03266332] [-0.00767608] [-0.02763819] [-0.0085108] [-0.02261307] [-0.00927347] [-0.01758794] [-0.00996348] [-0.01256281] [-0.01058028] [-0.00753769] [-0.01112327] [-0.00251256] [-0.01159212] [0.00251256] [-0.0119864] [0.00753769] [-0.01230577] [0.01256281] [-0.0125499] [0.01758794] [-0.01271853] [0.02261307] [-0.01281141] [0.02763819] [-0.01282828] [0.03266332] [-0.01276921] [0.03768844] [-0.01263374] [0.04271357] [-0.0124222] [0.04773869] [-0.01213433] [0.05276382] [-0.01177019] [0.05778894] [-0.01132978] [0.06281407] [-0.01081328] [0.0678392] [-0.01022079] [0.07286432] [-0.0095524] [0.07788945] [-0.00880829] [0.08291457] [-0.00798891] [0.0879397] [-0.00709423] [0.09296482] [-0.00612465] [0.09798995] [-0.00508054] [0.10301508] [-0.00396235] [0.1080402] [-0.00277015] [0.11306533] [-0.00150467] [0.11809045] [-0.00016631] [0.12311558] [0.00124456] [0.1281407] [0.00272749] [0.13316583] [0.00428167] [0.13819095] [0.00590681] [0.14321608] [0.00760225] [0.14824121] [0.00936736] [0.15326633] [0.01120149] [0.15829146] [0.01310407] [0.16331658] [0.01507429] [0.16834171] [0.01711155] [0.17336683] [0.01921509] [0.17839196] [0.02138421] [0.18341709] [0.02361807] [0.18844221] [0.025916] [0.19346734] [0.02827699] [0.19849246] [0.03070048] [0.20351759] [0.03318542] [0.20854271] [0.03573113] [0.21356784] [0.03833658] [0.21859296] [0.04100097] [0.22361809] [0.04372352] [0.22864322] [0.04650301] [0.23366834] [0.04933885] [0.23869347] [0.05222993] [0.24371859] [0.05517522] [0.24874372] [0.05817383] [0.25376884] [0.06122481] [0.25879397] [0.06432732] [0.2638191] [0.06747992] [0.26884422] [0.07068209] [0.27386935] [0.0739326] [0.27889447] [0.07723044] [0.2839196] [0.08057454] [0.28894472] [0.08396398] [0.29396985] [0.08739763] [0.29899497] [0.09087444] [0.3040201] [0.09439346] [0.30904523] [0.0979536] [0.31407035] [0.10155372] [0.31909548] [0.10519281] [0.3241206] [0.10886969] [0.32914573] [0.11258352] [0.33417085] [0.11633309] [0.33919598] [0.12011722] [0.34422111] [0.12393505] [0.34924623] [0.12778552] [0.35427136] [0.13166726] [0.35929648] [0.13557956] [0.36432161] [0.13952123] [0.36934673] [0.14349112] [0.37437186] [0.14748825] [0.37939698] [0.15151142] [0.38442211] [0.1555597] [0.38944724] [0.15963213] [0.39447236] [0.16372743] [0.39949749] [0.16784477] [0.40452261] [0.17198303] [0.40954774] [0.17614108] [0.41457286] [0.18031791] [0.41959799] [0.1845128] [0.42462312] [0.18872443] [0.42964824] [0.19295192] [0.43467337] [0.19719402] [0.43969849] [0.2014502] [0.44472362] [0.20571916] [0.44974874] [0.21000005] [0.45477387] [0.21429165] [0.45979899] [0.21859346] [0.46482412] [0.22290424] [0.46984925] [0.22722317] [0.47487437] [0.23154928] [0.4798995] [0.23588155] [0.48492462] [0.24021927] [0.48994975] [0.24456154] [0.49497487] [0.24890746] [0.5] [0.25325593]
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