tensorboard-sklearn数据-loss

记录sklearn数据训练时的loss值，用tensorboard可视化

三步骤：红字处

import tensorflow as tf
from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import LabelBinarizer
 
# load data
digits = load_digits()
X = digits.data
y = digits.target
y = LabelBinarizer().fit_transform(y)   # 转换格式
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3)
 
def add_layer(inputs, in_size, out_size, layer_name, active_function=None):
    """
    :param inputs:
    :param in_size: 行
    :param out_size: 列 ， [行， 列] =矩阵
    :param active_function:
    :return:
    """
    with tf.name_scope('layer'):
        with tf.name_scope('weights'):
            W = tf.Variable(tf.random_normal([in_size, out_size]), name='W')  #
        with tf.name_scope('bias'):
            b = tf.Variable(tf.zeros([1, out_size]) + 0.1)  # b是一行数据，对应out_size列个数据
        with tf.name_scope('Wx_plus_b'):
            Wx_plus_b = tf.matmul(inputs, W) + b
        Wx_plus_b = tf.nn.dropout(Wx_plus_b, keep_prob=keep_prob)
        if active_function is None:
            outputs = Wx_plus_b
        else:
            outputs = active_function(Wx_plus_b)
        tf.summary.histogram(layer_name + '/outputs', outputs)  # 1.2.记录outputs值，数据直方图
        return outputs
 
# define placeholder for inputs to network
keep_prob = tf.placeholder(tf.float32)  # 不被dropout的数量
xs = tf.placeholder(tf.float32, [None, 64])  # 8*8
ys = tf.placeholder(tf.float32, [None, 10])
 
# add output layer
l1 = add_layer(xs, 64, 50, 'l1', active_function=tf.nn.tanh)
prediction = add_layer(l1, 50, 10, 'l2', active_function=tf.nn.softmax)
 
# the loss between prediction and really
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), reduction_indices=[1]))
tf.summary.scalar('loss', cross_entropy)  # 字符串类型的标量张量，包含一个Summaryprotobuf  1.1记录标量
# training
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
 
sess = tf.Session()
merged = tf.summary.merge_all()  # 2.把所有summary节点整合在一起,只需run一次,这儿只有cross_entropy
sess.run(tf.initialize_all_variables())
 
train_writer = tf.summary.FileWriter('log/train', sess.graph)  # 3.写入
test_writer = tf.summary.FileWriter('log/test', sess.graph)
 
# start training
for i in range(500):
    sess.run(train_step, feed_dict={xs: X_train, ys: y_train, keep_prob: 0.5})  # keep_prob训练时保留50%，防止过拟合
    if i % 50 == 0:
        # record loss
        train_result = sess.run(merged, feed_dict={xs: X_train, ys: y_train, keep_prob: 1})  # 3.1 激活 tensorboard记录保留100%的数据
        test_result = sess.run(merged, feed_dict={xs: X_test, ys: y_test, keep_prob: 1})
        train_writer.add_summary(train_result, i)
        test_writer.add_summary(test_result, i)
 
print("Record Finished !!!")