目標：簡單處理二維平面中線性擬合一堆數據

效果：

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt rng = np.randomlearning_rate = 0.1 training_epochs = 1000 display_step = 50train_X = np.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1]) train_Y = np.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3]) n_samples = train_X.shape[0] print(n_samples)# tf Graph Input X = tf.placeholder("float") Y = tf.placeholder("float")# Set model weights 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.square(pred-Y))/(2*n_samples) optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)with tf.Session() as sess:sess.run(tf.global_variables_initializer())for epoch in range(training_epochs) :# zip() 函數用于將可迭代的對象作為參數，將對象中對應的元素打包成一個個元組，然后返回由這些元組組成的列表。for (x,y) in zip(train_X,train_Y):sess.run(optimizer,feed_dict={X:x,Y:y})#Display logs per epoch stepif (epoch+1) % display_step == 0:c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \"W=", sess.run(W), "b=", sess.run(b))# print("Optimization Finished!")# 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')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() 復制代碼代碼二：eager APIfrom __future__ import absolute_import, division, print_functionimport matplotlib.pyplot as plt import numpy as np import tensorflow as tf import tensorflow.contrib.eager as tfetf.enable_eager_execution()train_X = [3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1] train_Y = [1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3] n_samples = len(train_X) print(n_samples)# Parameters learning_rate = 0.01 display_step = 100 num_steps = 1000W = tfe.Variable(np.random.randn()) b = tfe.Variable(np.random.randn())# Linear regression (Wx + b) def linear_regression(inputs):return inputs * W + b# Mean square error def mean_square_fn(model_fn, inputs, labels):return tf.reduce_sum(tf.pow(model_fn(inputs) - labels, 2)) / (2 * n_samples)# SGD Optimizer optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)# Compute gradients grad = tfe.implicit_gradients(mean_square_fn)# Initial cost, before optimizing print("Initial cost= {:.9f}".format(mean_square_fn(linear_regression, train_X, train_Y)),"W=", W.numpy(), "b=", b.numpy())# Training for step in range(num_steps):optimizer.apply_gradients(grad(linear_regression, train_X, train_Y))if (step + 1) % display_step == 0 or step == 0:print("Epoch:", '%04d' % (step + 1), "cost=","{:.9f}".format(mean_square_fn(linear_regression, train_X, train_Y)),"W=", W.numpy(), "b=", b.numpy())# Graphic display plt.plot(train_X, train_Y, 'ro', label='Original data') plt.plot(train_X, np.array(W * train_X + b), label='Fitted line') plt.legend() plt.show() 復制代碼

總結

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