TensorFlow之添加层及建造神经网络
import tensorflow as tf import numpy as np #TensorFlow之添加层 #添加神经层函数(输入,输入大小,输出大小,激励函数) def add_layer(inputs,in_size,out_size,activation_function=None): Weights = tf.Variable(tf.random_normal([in_size,out_size])) #初始值不为0,所以+0.1 biases = tf.Variable(tf.zeros([1,out_size]) + 0.1) #没有被激活的值 Wx_plus_b = tf.matmul(inputs,Weights) + biases if activation_function is None: outputs = Wx_plus_b else: outputs = activation_function(Wx_plus_b) return outputs x_data = np.linspace(-1,2,300)[:,np.newaxis] #噪点 noise = np.random.normal(0,0.05,x_data.shape) y_data = np.square(x_data) - 0.5 + noise #定义2个参数 xs = tf.placeholder(tf.float32,[None,1]) ys = tf.placeholder(tf.float32,[None,1]) #输入层 l1 = add_layer(xs,1,10,activation_function = tf.nn.relu) #输出层 prediction = add_layer(l1,10,1,activation_function = None) #损失函数 loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),reduction_indices = [1])) #学习率通常小于1 #以0.1的学习率,通过loss变小,每一次的优化 train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss) init = tf.initialize_all_variables() sess = tf.Session() sess.run(init) for i in range(1000): sess.run(train_step,feed_dict = {xs:x_data,ys:y_data}) if i % 50 == 0: print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))结果:
RESTART: /Users/dongsai/Documents/MachineLearning/tensorflow/TensorFlow_Study/csdn/tf_lesson10.py WARNING:tensorflow:From /Users/dongsai/Documents/MachineLearning/tensorflow/TensorFlow_Study/csdn/tf_lesson10.py:39: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.Instructions for updating:Use `tf.global_variables_initializer` instead.3.228640.01883730.01430460.01276550.01190410.0113940.01103750.01071370.01046740.01028780.01014490.01000580.009894970.009808350.009723760.009626850.009538080.009456170.009361490.00927368
>>>
损失函数越来越小.
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