模型儲存

tf.train.Saver()

The Saver class adds ops to save and restore variables to and from checkpoints. It also provides convenience methods to run these ops.

兩個重要的函式。
一個是saver.save() 將某個session中的模型和引數都儲存在save-path,並且後面是迭代次數。

而對於restrore()函式，我認為理解恢復操作的最好方法是將它簡單的看做是一種資料初始化操作，就是講之前的session中的資料完整的init出來，在當前的session中。

# -*- coding: UTF-8 -*

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)

learning_rate = 0.001
batch_size = 100
display_step = 1
model_path = "../tmp/model.ckpt"

n_hidden_1 = 256
n_hidden_2 = 256
n_input = 784
 

n_classes = 10

x = tf.placeholder(tf.float32,[None,n_input])
y = tf.placeholder(tf.float32,[None,n_classes])

weights = {
    'h1':tf.Variable(tf.random_normal([n_input,n_hidden_1])),
    'h2':tf.Variable(tf.random_normal([n_hidden_1,n_hidden_2])),
    'out':tf.Variable(tf.random_normal([n_hidden_2,n_classes]))
}

biases = {
    'b1'
 
:tf.Variable(tf.random_normal([n_hidden_1])),
    'b2':tf.Variable(tf.random_normal([n_hidden_2])),
    'out':tf.Variable(tf.random_normal([n_classes]))
}

#構建模型
def multilayer_preceptron(x,weights,biases):
    #hidden 1 with relu activation
    layer_1 = tf.add(tf.matmul(x,weights['h1']),biases['b1'])
    layer_1 = tf.nn.relu(layer_1)
    #hidden 2 with relu activation
    layer_2 = tf.add(tf.matmul(layer_1,weights['h2']),biases['b2'])
    layer_2 = tf.nn.relu(layer_2)
    #output layer with linear activation
    out_layer = tf.matmul(layer_2,weights['out'])+biases['out']
    return out_layer

pred = multilayer_preceptron(x,weights,biases)

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)

init = tf.global_variables_initializer()

saver = tf.train.Saver()

print "Starting 1st session..."
if __name__ == '__main__':
    with tf.Session() as sess:
        #init variables
        sess.run(init)

        for epoch in range(3):
            avg_cost = 0
            total_batch = int(mnist.train.num_examples/batch_size)
            #loop
            for i in range(total_batch):
                batch_x,batch_y = mnist.train.next_batch(batch_size)
                _,c = sess.run([optimizer,cost],feed_dict={
                    x:batch_x,
                    y:batch_y
                })

                avg_cost += c/total_batch

            if epoch % display_step == 0:
                print "Epoch:", '%04d' % (epoch + 1), "cost=", \
                    "{:.9f}".format(avg_cost)

        print "First Optimization Finished!"

        # Test model
        correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
        # Calculate accuracy
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
        print "Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels})

        # Save model weights to disk
        save_path = saver.save(sess, model_path)
        print "Model saved in file: %s" % save_path

    #running a new session..

    with tf.Session() as sess:
        sess.run(init)

        #理解恢復操作的最好方法是將它簡單的看做是一種資料初始化操作
        load_path = saver.restore(sess,model_path)
        print "Model restored from file:%s"%save_path

        for epoch in range(7):
            avg_cost = 0
            total_batch = int(mnist.train.num_examples/batch_size)
            #loop
            for i in range(total_batch):
                batch_x,batch_y = mnist.train.next_batch(batch_size)
                _,c = sess.run([optimizer,cost],feed_dict={
                    x:batch_x,
                    y:batch_y
                })

                avg_cost += c/total_batch

            if epoch % display_step == 0:
                print "Epoch:", '%04d' % (epoch + 1), "cost=", \
                    "{:.9f}".format(avg_cost)
        print "Second Optimization Finished!"

        # Test model
        correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
        # Calculate accuracy
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
        print "Accuracy:", accuracy.eval(
            {x: mnist.test.images, y: mnist.test.labels})

TensorBoard

tf.summary.scalar() 將記錄要顯示的變數，在tensorboard中顯示，所有的summary也相當於op,定義完scalar後，將他們merge所有的op為一個組合。

在session函式迭代裡面，run()出函式。

summary_writer = tf.summary.FileWriter(logs_path,graph=tf.get_default_graph())
寫函式將所有的引數儲存在log中，便於我們呼叫。

然後在迭代裡面講當前的summary op ，add進寫檔案。

最後，在終端裡面，tensorboard –logdit=”“

basic model

# -*- coding: UTF-8 -*

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)

learning_rate = 0.01
training_epochs = 25
batch_size = 100
display_step = 1
logs_path = '../tmp/tensorflow_logs/example'

x = tf.placeholder(tf.float32,[None,784],name='InputData')
y = tf.placeholder(tf.float32,[None,10],name='LabelData')

w = tf.Variable(tf.zeros([784,10]),name='Weights')
b = tf.Variable(tf.zeros([10]),name='Bias')

with tf.name_scope('Model'):
    pred = tf.nn.softmax(tf.matmul(x,w)+b)

with tf.name_scope('Loss'):
    cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))

with tf.name_scope('SGD'):
    optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

with tf.name_scope('Accuracy'):
    acc = tf.equal(tf.argmax(pred,1),tf.argmax(y,1))
    acc = tf.reduce_mean(tf.cast(acc,tf.float32))

init = tf.global_variables_initializer()

tf.summary.scalar("loss",cost)

tf.summary.scalar("accuracy",acc)

merged_summary_op = tf.summary.merge_all()

with tf.Session() as sess:
    sess.run(init)

    summary_writer = tf.summary.FileWriter(logs_path,graph=tf.get_default_graph())

    for epoch in range(training_epochs):
        avg_cost = 0
        total_batch = int(mnist.train.num_examples/batch_size)
        #loop
        for i in range(total_batch):
            batch_xs,batch_ys = mnist.train.next_batch(batch_size)

            _,c,summary = sess.run([optimizer,cost,merged_summary_op],
                                   feed_dict={x:batch_xs,y:batch_ys})

            summary_writer.add_summary(summary,(epoch)*total_batch+i)

            avg_cost+=c/total_batch


        if (epoch + 1) % display_step == 0:
            print "Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(avg_cost)

    print "Optimization Finished!"

    # Test model
    # Calculate accuracy
    print "Accuracy:", acc.eval({x: mnist.test.images, y: mnist.test.labels})

    print "Run the command line:\n" \
          "--> tensorboard --logdir=/tmp/tensorflow_logs " \
          "\nThen open http://127.0.0.0:6006/ into your web browser"

升級版的Tensorboard

# -*- coding: UTF-8 -*

import tensorflow as tf

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

# Parameters
learning_rate = 0.01
training_epochs = 10
batch_size = 100
display_step = 1
logs_path = '../tmp/tensorflow_logs/example2'

# Network Parameters
n_hidden_1 = 20 # 1st layer number of features
n_hidden_2 = 40 # 2nd layer number of features
n_input = 784 # MNIST data input (img shape: 28*28)
n_classes = 10 # MNIST total classes (0-9 digits)

# tf Graph Input
# mnist data image of shape 28*28=784
x = tf.placeholder(tf.float32, [None, 784], name='InputData')
# 0-9 digits recognition => 10 classes
y = tf.placeholder(tf.float32, [None, 10], name='LabelData')

#使用tf.summary.scalar記錄標量
# 使用tf.summary.histogram記錄資料的直方圖
# 使用tf.summary.distribution記錄資料的分佈圖
# 使用tf.summary.image記錄影象資料

# Create model
def multilayer_perceptron(x, weights, biases):
    # Hidden layer with RELU activation
    layer_1 = tf.add(tf.matmul(x, weights['w1']), biases['b1'])
    layer_1 = tf.nn.relu(layer_1)
    # Create a summary to visualize the first layer ReLU activation
    tf.summary.histogram("relu1", layer_1)
    # Hidden layer with RELU activation
    layer_2 = tf.add(tf.matmul(layer_1, weights['w2']), biases['b2'])
    layer_2 = tf.nn.relu(layer_2)
    # Create another summary to visualize the second layer ReLU activation
    tf.summary.histogram("relu2", layer_2)
    # Output layer
    out_layer = tf.add(tf.matmul(layer_2, weights['w3']), biases['b3'])
    return out_layer

# Store layers weight & bias
weights = {
    'w1': tf.Variable(tf.random_normal([n_input, n_hidden_1]), name='W1'),
    'w2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2]), name='W2'),
    'w3': tf.Variable(tf.random_normal([n_hidden_2, n_classes]), name='W3')
}
biases = {
    'b1': tf.Variable(tf.random_normal([n_hidden_1]), name='b1'),
    'b2': tf.Variable(tf.random_normal([n_hidden_2]), name='b2'),
    'b3': tf.Variable(tf.random_normal([n_classes]), name='b3')
}

# Encapsulating all ops into scopes, making Tensorboard's Graph
# Visualization more convenient
with tf.name_scope('Model'):
    # Build model
    pred = multilayer_perceptron(x, weights, biases)

with tf.name_scope('Loss'):
    # Softmax Cross entropy (cost function)
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))

with tf.name_scope('SGD'):
    # Gradient Descent
    optimizer = tf.train.GradientDescentOptimizer(learning_rate)
    # Op to calculate every variable gradient
    grads = tf.gradients(loss, tf.trainable_variables())
    grads = list(zip(grads, tf.trainable_variables()))
    # Op to update all variables according to their gradient
    apply_grads = optimizer.apply_gradients(grads_and_vars=grads)

with tf.name_scope('Accuracy'):
    # Accuracy
    acc = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
    acc = tf.reduce_mean(tf.cast(acc, tf.float32))

# Initializing the variables
init = tf.global_variables_initializer()

# Create a summary to monitor cost tensor
tf.summary.scalar("loss", loss)
# Create a summary to monitor accuracy tensor
tf.summary.scalar("accuracy", acc)
# Create summaries to visualize weights
for var in tf.trainable_variables():
    tf.summary.histogram(var.name, var)
# Summarize all gradients
for grad, var in grads:
    tf.summary.histogram(var.name + '/gradient', grad)
# Merge all summaries into a single op
merged_summary_op = tf.summary.merge_all()

# Launch the graph
with tf.Session() as sess:
    sess.run(init)

    # op to write logs to Tensorboard
    summary_writer = tf.summary.FileWriter(logs_path,
                                            graph=tf.get_default_graph())

    # Training cycle
    for epoch in range(training_epochs):
        avg_cost = 0.
        total_batch = int(mnist.train.num_examples/batch_size)
        # Loop over all batches
        for i in range(total_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            # Run optimization op (backprop), cost op (to get loss value)
            # and summary nodes
            _, c, summary = sess.run([apply_grads, loss, merged_summary_op],
                                     feed_dict={x: batch_xs, y: batch_ys})
            # Write logs at every iteration
            summary_writer.add_summary(summary, epoch * total_batch + i)
            # Compute average loss
            avg_cost += c / total_batch
        # Display logs per epoch step
        if (epoch+1) % display_step == 0:
            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))

    print("Optimization Finished!")

    # Test model
    # Calculate accuracy
    print("Accuracy:", acc.eval({x: mnist.test.images, y: mnist.test.labels}))

    print("Run the command line:\n" \
          "--> tensorboard --logdir=/tmp/tensorflow_logs " \
          "\nThen open http://0.0.0.0:6006/ into your web browser")