tensorboard可視化(二)
tensorboard可視化(二) 1.導包 import tensorflow as tf import numpy as np
2.make up some data x_data = np.linspace(-1, 1, 300, dtype=np.float32)[:, np.newaxis] noise = np.random.normal(0, 0.05, x_data.shape).astype(np.float32) y_data = np.square(x_data) - 0.5 + noise
3.將xs和ys包含進來，形成一個大的圖層，圖層名字叫做inputs with tf.name_scope('inputs'): # 為xs指定名稱x_input xs = tf.placeholder(tf.float32, [None, 1],name='x_input') # 為ys指定名稱y_input ys = tf.placeholder(tf.float32, [None, 1],name='y_input')
4.在 layer 中為 Weights, biases 設置變化圖表 # add_layer多加一個n_layer參數(表示第幾層) def add_layer(inputs , in_size, out_size,n_layer, activation_function=None): ## add one more layer and return the output of this layer layer_name='layer%s'%n_layer with tf.name_scope(layer_name): # 對weights進行繪製圖標 with tf.name_scope('weights'): Weights= tf.Variable(tf.random_normal([in_size, out_size]),name='W') tf.summary.histogram(layer_name + '/weights', Weights) # 對biases進行繪製圖標 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.add(tf.matmul(inputs,Weights), biases) if activation_function is None: outputs=Wx_plus_b else: outputs= activation_function(Wx_plus_b) # 對outputs進行繪製圖標 tf.summary.histogram(layer_name + '/outputs', outputs) return outputs
5.修改隱藏層與輸出層 # 由於我們對addlayer 添加了一個參數, 所以修改之前調用addlayer()函數的地方. 對此處進行修改: # add hidden layer l1= add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu) # add output layer prediction= add_layer(l1, 10, 1, n_layer=2, activation_function=None)
6.設置loss的變化圖 # loss是在tesnorBorad 的event下面的, 這是由於我們使用的是tf.scalar_summary() 方法. with tf.name_scope('loss'): loss = tf.reduce_mean(tf.reduce_sum( tf.square(ys - prediction), reduction_indices=[1])) tf.summary.scalar('loss', loss) # tensorflow >= 0.12
7.給所有訓練圖合併 # 機器學習提升準確率 with tf.name_scope('train'): train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss) # 0.1表示學習效率 # 初始化 sess= tf.Session() merged = tf.summary.merge_all() writer = tf.summary.FileWriter("logs/", sess.graph) # sess.run(tf.global_variables_initializer())
8.訓練數據 for i in range(1000): sess.run(train_step, feed_dict={xs:x_data, ys:y_data}) if i%50 == 0: rs = sess.run(merged,feed_dict={xs:x_data,ys:y_data}) writer.add_summary(rs, i)
