訓練模型

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt money=np.array([[109],[82],[99], [72], [87], [78], [86], [84], [94], [57]]).astype(np.float32) click=np.array([[11], [8], [8], [6],[ 7], [7], [7], [8], [9], [5]]).astype(np.float32) x_test=money[0:5].reshape(-1,1) y_test=click[0:5] x_train=money[5:].reshape(-1,1) y_train=click[5:] x=tf.placeholder(tf.float32,[None,1],name='x') #保存要輸入的格式 w=tf.Variable(tf.zeros([1,1])) b=tf.Variable(tf.zeros([1])) y=tf.matmul(x,w)+b tf.add_to_collection('pred_network', y) #用于加載模型獲取要預測的網絡結構 y_=tf.placeholder(tf.float32,[None,1]) cost=tf.reduce_sum(tf.pow((y-y_),2)) train_step=tf.train.GradientDescentOptimizer(0.000001).minimize(cost) init=tf.global_variables_initializer() sess=tf.Session() sess.run(init) cost_history=[] saver = tf.train.Saver() for i in range(100):feed={x:x_train,y_:y_train}sess.run(train_step,feed_dict=feed)cost_history.append(sess.run(cost,feed_dict=feed)) # 輸出最終的W,b和cost值 print("109的預測值是:",sess.run(y, feed_dict={x: [[109]]})) print("W_Value: %f" % sess.run(w), "b_Value: %f" % sess.run(b), "cost_Value: %f" % sess.run(cost, feed_dict=feed)) # saver_path = saver.save(sess, "/modelsave/model.ckpt",global_step=100) # print("model saved in file: ", saver_path)#saver.save(sess, "modelsave/model") saver.save(sess, "modelsave/linermodel.cpkt")

加載模型

import tensorflow as tf with tf.Session() as sess:new_saver=tf.train.import_meta_graph('modelsave/model.ckpt-100.meta')new_saver.restore(sess,"modelsave/model.ckpt-100")graph = tf.get_default_graph()x=graph.get_operation_by_name('x').outputs[0]y=tf.get_collection("pred_network")[0]print("109的預測值是:",sess.run(y, feed_dict={x: [[109]]}))

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