如何直觀顯示損失和準確性最好？

Question

我在張量流中編寫了一個卷積神經網絡來執行mnist數據集。一切正常，但我想要在tensorboard中可視化模型。我該怎麼做？這裏是我的代碼：

from __future__ import print_function 

import tensorflow as tf 

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

# Parameters 
learning_rate = 0.001 
training_iters = 200000 
batch_size = 128 
display_step = 10 

# Network Parameters 
n_input = 784 # MNIST data input (img shape: 28*28) 
n_classes = 10 # MNIST total classes (0-9 digits) 
dropout = 0.75 # Dropout, probability to keep units 

# tf Graph input 
x = tf.placeholder(tf.float32, [None, n_input]) 
y = tf.placeholder(tf.float32, [None, n_classes]) 
keep_prob = tf.placeholder(tf.float32) #dropout (keep probability) 


# Create some wrappers for simplicity 
def conv2d(x, W, b, strides=1): 
    # Conv2D wrapper, with bias and relu activation 
    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME') 
    x = tf.nn.bias_add(x, b) 
    return tf.nn.relu(x) 


def maxpool2d(x, k=2): 
    # MaxPool2D wrapper 
    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1], 
        padding='SAME') 


# Create model 
def conv_net(x, weights, biases, dropout): 
    # Reshape input picture 
    x = tf.reshape(x, shape=[-1, 28, 28, 1]) 

    # Convolution Layer 
    conv1 = conv2d(x, weights['wc1'], biases['bc1']) 
    # Max Pooling (down-sampling) 
    conv1 = maxpool2d(conv1, k=2) 

    # Convolution Layer 
    conv2 = conv2d(conv1, weights['wc2'], biases['bc2']) 
    # Max Pooling (down-sampling) 
    conv2 = maxpool2d(conv2, k=2) 

    # Fully connected layer 
    # Reshape conv2 output to fit fully connected layer input 
    fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]]) 
    fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1']) 
    fc1 = tf.nn.relu(fc1) 
    # Apply Dropout 
    fc1 = tf.nn.dropout(fc1, dropout) 

    # Output, class prediction 
    out = tf.add(tf.matmul(fc1, weights['out']), biases['out']) 
    return out 

# Store layers weight & bias 
weights = { 
    # 5x5 conv, 1 input, 32 outputs 
    'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])), 
    # 5x5 conv, 32 inputs, 64 outputs 
    'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])), 
    # fully connected, 7*7*64 inputs, 1024 outputs 
    'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])), 
    # 1024 inputs, 10 outputs (class prediction) 
    'out': tf.Variable(tf.random_normal([1024, n_classes])) 
} 

biases = { 
    'bc1': tf.Variable(tf.random_normal([32])), 
    'bc2': tf.Variable(tf.random_normal([64])), 
    'bd1': tf.Variable(tf.random_normal([1024])), 
    'out': tf.Variable(tf.random_normal([n_classes])) 
} 

# Construct model 
pred = conv_net(x, weights, biases, keep_prob) 

# Define loss and optimizer 
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, 
labels=y)) 
optimizer = 
tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) 

# Evaluate model 
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) 
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) 

# Initializing the variables 
init = tf.initialize_all_variables() 

# Launch the graph 
with tf.Session() as sess: 
    sess.run(init) 
    step = 1 
    # Keep training until reach max iterations 
    while step * batch_size < training_iters: 
     batch_x, batch_y = mnist.train.next_batch(batch_size) 
     # Run optimization op (backprop) 
     sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, 
            keep_prob: dropout}) 
     if step % display_step == 0: 
      # Calculate batch loss and accuracy 
      loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x, 
                   y: batch_y, 
                  keep_prob: 1.}) 
      print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + \ 
        "{:.6f}".format(loss) + ", Training Accuracy= " + \ 
        "{:.5f}".format(acc)) 
     step += 1 
    print("Optimization Finished!") 

    # Calculate accuracy for 256 mnist test images 
    print("Testing Accuracy:", \ 
     sess.run(accuracy, feed_dict={x: mnist.test.images[:256], 
             y: mnist.test.labels[:256], 
             keep_prob: 1.})) 

Answer 1

創建摘要標量和操作：

cost = tf.summary.scalar("cost", cost) 
accuracy = tf.summary.scalar("accuracy", accuracy) 
train_summary_op = tf.summary.merge([cost,accuracy]) 

待辦事項作者：

train_writer = tf.summary.FileWriter(log_dir+'/train', 
             graph=tf.get_default_graph()) 

使用它在會話中運行，寫總結：

loss, acc, summary = sess.run([cost, accuracy, train_summary_op], feed_dict={x: batch_x,y: batch_y,keep_prob: 1.}) 
train_writer.add_summary(train_summary_str, step) 

後來用張量板（po int寫入您正在撰寫摘要的文件夾）以可視化丟失和準確性的圖形。

我希望這是有幫助的。