Tensorflow測試
阿新 • • 發佈:2018-11-10
Tensorflow版本:1.8.0
python版本:3.5.2
測試程式碼:
import tensorflow as tf import numpy import matplotlib.pyplot as plt rng = numpy.random learning_rate = 0.01 training_epochs = 1000 display_step = 50 #資料集x train_X = numpy.asarray([3.3,4.4,5.5,7.997,5.654,.71,6.93,4.168,9.779,6.182,7.59,2.167, 7.042,10.791,5.313,9.27,3.1]) #資料集y train_Y = numpy.asarray([1.7,2.76,3.366,2.596,2.53,1.221,1.694,1.573,3.465,1.65,2.09, 2.827,3.19,2.904,2.42,2.94,1.3]) n_samples = train_X.shape[0] X = tf.placeholder("float") Y = tf.placeholder("float") W = tf.Variable(rng.randn(), name="weight") b = tf.Variable(rng.randn(), name="bias") pred = tf.add(tf.multiply (X, W), b) cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples) optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) init = tf.initialize_all_variables() with tf.Session() as sess: sess.run(init) # 訓練資料 for epoch in range(training_epochs): for (x, y) in zip(train_X, train_Y): sess.run(optimizer, feed_dict={X: x, Y: y}) print("優化完成!") training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y}) print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n') #視覺化顯示 plt.plot(train_X, train_Y, 'ro', label='Original data') plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line') plt.legend() plt.show()
結果: