Windows下tensorboard簡(jiǎn)要教程

在構(gòu)造模型的時(shí)候加入幾句話：

init = tf.global_variables_initializer()

(初始化結(jié)束以后加入這幾句話)

tf.summary.scalar("loss", correct_pred)
tf.summary.scalar("accuracy", accuracy)

(如果有兩句以上語(yǔ)句子寓，才使用下面的語(yǔ)句)

summary_op = tf.summary.merge_all()
with tf.Session() as sess:
    summary_writer = tf.summary.FileWriter("E:/gru/logs", graph_def=sess.graph_def)
    # Run the initializer
    sess.run(init)

    for step in range(1, training_steps+1):
        batch_x, batch_y = mnist.train.next_batch(batch_size)
        # Run optimization op (backprop)
        sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})
        if step % display_step == 0 or step == 1:
            # Calculate batch loss and accura
    summary_str = sess.run(summary_op,feed_dict={X: batch_x, Y: batch_y})
    summary_writer.add_summary(summary_str, step)

代碼寫好后可以點(diǎn)點(diǎn)擊調(diào)試
代碼部分結(jié)束：
打開(kāi)你的cmd暗挑，請(qǐng)注意在cmd中一定可以運(yùn)行python，如果不能運(yùn)行請(qǐng)檢查環(huán)境變量是否配置成功斜友，輸入以下指令炸裆，其中l(wèi)ogdir的地址一定要和運(yùn)行的python文件中中一致

打開(kāi)瀏覽器http://127.0.0.1:6006/，就可以可視化觀察自己想觀測(cè)的圖了蝙寨，并且可以看到所寫的模型的基本結(jié)構(gòu)

image.png

這些是很久之前寫的教程晒衩，在使用tensorboard中發(fā)現(xiàn)新的問(wèn)題：

（1）由于tensorflow版本更新的問(wèn)題嗤瞎，許多東西用著用著就不能用了，比如：
tf.train.SummaryWriter改為：tf.summary.FileWriter
這里貼一個(gè)博客听系，可以對(duì)應(yīng)著修改：
https://blog.csdn.net/waterydd/article/details/70237984
（2）在cmd中使用tensorboard出現(xiàn)：importerror:cannot import name 'encodings'贝奇，對(duì)tensorflow進(jìn)行升級(jí)處理，打開(kāi)cmd靠胜，輸入
pip uninstall tensorflow
pip -U install tensorflow
友情提示：換源
(3)使用tf.summary.merge_all()注意掉瞳，在不同模型中分開(kāi)merge，例如gan網(wǎng)絡(luò)
示例代碼：

from __future__ import print_function

import tensorflow as tf
from tensorflow.contrib import rnn

# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("E:/MachineLearning/", one_hot=True)

'''
To classify images using a recurrent neural network, we consider every image
row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then
handle 28 sequences of 28 steps for every sample.
'''

# Training Parameters
learning_rate = 0.001
training_steps = 10000
batch_size = 128
display_step = 200

# Network Parameters
num_input = 28 # MNIST data input (img shape: 28*28)
timesteps = 28 # timesteps
num_hidden = 128 # hidden layer num of features
num_classes = 10 # MNIST total classes (0-9 digits)

# tf Graph input
X = tf.placeholder("float", [None, timesteps, num_input])
Y = tf.placeholder("float", [None, num_classes])

# Define weights
weights = { 'out': tf.Variable(tf.random_normal([num_hidden, num_classes]))
}
biases = {'out': tf.Variable(tf.random_normal([num_classes]))}


def RNN(x, weights, biases):
    x = tf.unstack(x, timesteps, 1)
    # Define a lstm cell with tensorflow
    lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)
    # Get lstm cell output
    outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)

    # Linear activation, using rnn inner loop last output
    return tf.matmul(outputs[-1], weights['out']) + biases['out']

logits = RNN(X, weights, biases)
prediction = tf.nn.softmax(logits)

# Define loss and optimizer
loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
    logits=logits, labels=Y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss_op)

# Evaluate model (with test logits, for dropout to be disabled)
correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()
tf.summary.scalar("loss", correct_pred)
tf.summary.scalar("accuracy", accuracy)
summary_op = tf.summary.merge_all()
# Start training
with tf.Session() as sess:
    summary_writer = tf.summary.FileWriter("E:/gru/logs", graph_def=sess.graph_def)
    # Run the initializer
    sess.run(init)

    for step in range(1, training_steps+1):
        batch_x, batch_y = mnist.train.next_batch(batch_size)
        # Reshape data to get 28 seq of 28 elements
        batch_x = batch_x.reshape((batch_size, timesteps, num_input))
        # Run optimization op (backprop)
        sess.run(train_op, feed_dict={X: batch_x, Y: batch_y})
        if step % display_step == 0 or step == 1:
            # Calculate batch loss and accuracy
            loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x, Y: batch_y})
            print("Step " + str(step) + ", Minibatch Loss= " + "{:.4f}".format(loss) + ", Training Accuracy= "+"{:.3f}".format(acc))
            summary_str = sess.run(summary_op,feed_dict={X: batch_x, Y: batch_y})
            summary_writer.add_summary(summary_str, step)
    print("Optimization Finished!")

    # Calculate accuracy for 128 mnist test images
    test_len = 128
    test_data = mnist.test.images[:test_len].reshape((-1, timesteps, num_input))
    test_label = mnist.test.labels[:test_len]
    print("Testing Accuracy:", \
        sess.run(accuracy, feed_dict={X: test_data, Y: test_label}))
    print('sink')