1. 使用 TensorFlow 自动微分求解简单线性回归的参数(w, b) 1#载入库2importnumpy as np3importtensorflow as tf4importmatplotlib.pyplot as plt5plt.ion()678#定义损失函数为均方误差MSE=(sum(y-y0)^2)/n9defloss(y, y_pred):10#tf.reduce_mean 函数用于计算张量tensor沿着指定的数轴上的的平均值11return...
#create a input function for predictionprediction_input_fn =lambda:my_input(my_feature,targets,shuffle=False,num_epochs=1)#predictionpredictions = linear_regressor.predict(input_fn =prediction_input_fn) predictions= np.array([item["predictions"][0]foriteminpredictions])#errors MSEmean_squared_error...
(init) #初始化全局变量 for epoch in range(training_epochs): #全部的数据需要处理几遍 for x,y in zip(x_train,y_train): sess.run(train_op,feed_dict={X:x,Y:y}) w_val=sess.run(w) sess.close() plt.scatter(x_train,y_train) y_learned=w_val*x_train plt.plot(x_train,y_learned...
(X*theta.T)-yif(i%(iters/BLOCK)==0):pltShowThetaLine(data,theta,i)forjinrange(parameters):term=np.multiply(error,X[:,j])temp[0,j]=theta[0,j]-(alpha/len(X))*np.sum(term)theta=tempcost[i]=computeCost(X,y,theta)returntheta,costdefmain_func(argv):path='ex1data1.txt'data=pd....
for epoch in range(training_epochs): for (x, y) in zip(train_X, train_Y): sess.run(optimizer, feed_dict={X: x, Y: y}) #Display logs per epoch step if epoch % display_step == 0: print "Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(sess.run(cost, feed_...
for step in range(1, training_steps + 1): # Run the optimization to update W and b values. run_optimization() if step % display_step == 0: pred = linear_regression(X) loss = mean_square(pred, Y) print("step: %i, loss: %f, W: %f, b: %f" % (step, loss, W.numpy(), ...
model=LinearRegressionModel()criterion=torch.nn.MSELoss(size_average=False)optimizer=torch.optim.SGD(model.parameters(),lr=0.01)forepochinrange(500):pred_y=model(xs)loss=criterion(pred_y,ys)optimizer.zero_grad()loss.backward()optimizer.step()print('epoch {}, loss {}'.format(epoch,loss.item...
() as g: pred = linear_regression(X) loss = mean_square(pred,Y) # 计算梯度 gradients = g.gradient(loss,[W,b]) # 按gradients更新 W 和 b optimizer.apply_gradients(zip(gradients,[W,b]))# 针对给定训练步骤数开始训练for step in range(1,training_steps + 1): # 运行优化以更新W和b值...
# 针对给定训练步骤数开始训练 for step in range(1,training_steps + 1): # 运行优化以更新W和b值 run_optimization() if step % display_step == 0: pred = linear_regression(X) loss = mean_square(pred, Y) print("step: %i, loss: %f, W: %f, b: %f" % (step, loss, W.numpy(), ...
示例代码:/home/ubuntu/linear_regression_model.py linear_regression_model.py #!/usr/bin/python import numpy as np class linearRegressionModel: def __init__(self,x_dimen): self.x_dimen = x_dimen self._index_in_epoch = 0 self.constructModel()#呵呵 ...