Tensorflow学习
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不知道应该写些什么,所以先空着??
import os os.environ[‘TF_CPP_MIN_LOG_LEVEL‘] = ‘2‘ import tensorflow as tf import numpy as np import matplotlib.pyplot as plt #import tensorflow.examples.tutorials.mnist.input_data as input_data def add_layer(inputs,in_size,out_size,activation_function=None): Weights = tf.Variable(tf.random_normal([in_size,out_size])) biases = tf.Variable(tf.zeros([1,out_size])+0.1) 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(-1,1,300)[:,np.newaxis] noise = np.random.normal(0,0.05,x_data.shape) y_data = np.square(x_data)-0.5+noise xs = tf.placeholder(tf.float32,[None,1]) ys = tf.placeholder(tf.float32,[None,1]) l1 = add_layer(xs,1,10,activation_function=tf.nn.relu) predition = add_layer(l1,10,1,activation_function=None) loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-predition),reduction_indices=[1])) train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss) init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) fig = plt.figure() ax = fig.add_subplot(1, 1, 1) ax.scatter(x_data, y_data) plt.ion() plt.show() for i in range(1000): sess.run(train_step,feed_dict={xs:x_data,ys:y_data}) if i %50 ==0: try: ax.lines.remove(lines[0]) except Exception: pass predition_value = sess.run(predition,feed_dict={xs:x_data}) lines = ax.plot(x_data,predition_value,‘r-‘,lw=5) plt.pause(0.1) print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))
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