import tensorflow as tf import numpy as np def add_layer(inputs, in_size, out_size, n_layer, activation_function=None): layer_name = 'layer%s' % n_layer with tf.name_scope("layer"): with tf.name_scope("weight"): Weights = tf.Variable(tf.random_normal([in_size,out_size]),name="W") tf.summary.histogram(layer_name+'/Weights',Weights) with tf.name_scope("biases"): biases = tf.Variable(tf.zeros([1, out_size])+0.1,name="b") tf.summary.histogram(layer_name+'/biases',biases) with tf.name_scope("Wx_plus_b"): Wx_plus_b = tf.matmul(inputs, Weights)+biases if activation_function == None: outputs = Wx_plus_b else: outputs = activation_function(Wx_plus_b) tf.summary.histogram(layer_name+'/outputs',outputs) 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 with tf.name_scope("inputs"): xs = tf.placeholder(tf.float32,[None,1],name="x_input") ys = tf.placeholder(tf.float32,[None,1],name="y_input") l1 = add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu) prediction = add_layer(l1, 10, 1, n_layer=2, activation_function=None) with tf.name_scope("losskk"): loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]),name="losskk") tf.summary.scalar('loss',loss) with tf.name_scope("train"): train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss) init = tf.global_variables_initializer() sess = tf.Session() merged = tf.summary.merge_all() writer = tf.summary.FileWriter("logss",sess.graph) sess.run(init) for i in range(1000): sess.run(train_step,feed_dict={xs:x_data,ys:y_data}) if i%50==0: result = sess.run(merged,feed_dict={xs:x_data,ys:y_data}) writer.add_summary(result,i)代码如上,保存运行之后会在相应的文件夹下生成文件。此时执行在pycharm左下角的Terminal窗口输入如下命令 ,则会弹出相应的网址,打开即为可视化过程!
tensorboard --logdir=文件名