import tensorflow as tf import numpy as np import matplotlib.pyplot as plt #随机生层200个随机点 在 -0.5到0.5之间 200个 x_data=np.linspace(-0.5,0.5,200)[:,np.newaxis] noise=np.random.normal(0,0.02,x_data.shape) y_data=np.square(x_data)+noise #[None,1] 行数不确定,一列 x=tf.placeholder(tf.float32,[None,1]) y=tf.placeholder(tf.float32,[None,1]) #定义中间层 #一行10列 Weights_l1=tf.Variable(tf.random_normal([1,10])) biases_l1=tf.Variable(tf.zeros([1,10])) Wx_plus_b_l1=tf.matmul(x,Weights_l1)+biases_l1 L1=tf.nn.tanh(Wx_plus_b_l1) #激励函数 #定义输出层 Weights_l2=tf.Variable(tf.random_normal([10,1])) biases_l2=tf.Variable(tf.zeros([1,1])) Wx_plus_b_l2=tf.matmul(L1,Weights_l2)+biases_l2 prediction=tf.nn.tanh(Wx_plus_b_l2) #得到预测值 激励函数 #二次代价函数 loss=tf.reduce_mean(tf.square(y-prediction)) #使用梯度下降优化器训练 train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for _ in range(2000): sess.run(train_step,feed_dict={x:x_data,y:y_data}) #获得预测值 prediction_value=sess.run(prediction,feed_dict={x:x_data}) plt.figure() plt.scatter(x_data,y_data) #'r-' 红色实线 lw=5 宽度为5 plt.plot(x_data,prediction_value,'r-',lw=5) plt.show()
TensorFlow(示例+预测)模版
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