# Lab 4 Multi-variable linear regression import tensorflow as tf tf.set_random_seed(777) # for reproducibility x_data = [[73., 80., 75.], [93., 88., 93.], [89., 91., 90.], [96., 98., 100.], [73., 66., 70.]] y_data = [[152.], [185.], [180.], [196.], [142.]] # placeholders for a tensor that will be always fed. X = tf.placeholder(tf.float32, shape=[None, 3]) Y = tf.placeholder(tf.float32, shape=[None, 1]) W = tf.Variable(tf.random_normal([3, 1]), name='weight') b = tf.Variable(tf.random_normal([1]), name='bias') # Hypothesis hypothesis = tf.matmul(X, W) + b # Simplified cost/loss function cost = tf.reduce_mean(tf.square(hypothesis - Y)) # Minimize optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-5) train = optimizer.minimize(cost) # Launch the graph in a session. sess = tf.Session() # Initializes global variables in the graph. sess.run(tf.global_variables_initializer()) for step in range(2001): cost_val, hy_val, _ = sess.run( [cost, hypothesis, train], feed_dict={X: x_data, Y: y_data}) if step % 10 == 0: print(step, "Cost: ", cost_val, "\nPrediction:\n", hy_val) ''' 0 Cost: 7105.46 Prediction: [[ 80.82241058] [ 92.26364136] [ 93.70250702] [ 98.09217834] [ 72.51759338]] 10 Cost: 5.89726 Prediction: [[ 155.35159302] [ 181.85691833] [ 181.97254944] [ 194.21760559] [ 140.85707092]] ... 1990 Cost: 3.18588 Prediction: [[ 154.36352539] [ 182.94833374] [ 181.85189819] [ 194.35585022] [ 142.03240967]] 2000 Cost: 3.1781 Prediction: [[ 154.35881042] [ 182.95147705] [ 181.85035706] [ 194.35533142] [ 142.036026 ]] '''
lab-04-2-multi_variable_matmul_linear_regression
猜你喜欢
转载自blog.csdn.net/qq_30868235/article/details/80875482
今日推荐
周排行