TFLearn代码示例

import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.estimator import regression
import tflearn.datasets.mnist as mnist
x , y, x_test, y_test = mnist.load_data(one_hot=True)
x = x.reshape([-1,28,28,1])
x_test = x_test.reshape([-1,28,28,1])
CNN = input_data(shape=[None, 28, 28, 1],name='input')
CNN = conv_2d(CNNN, 32, 5, activation='relu',regularizer='L2')
CNN = conv_2d(CNN, 32, 5, activation='relu',regularizer='L2')
CNN = max_pool_2d(CNN,2)
CNN = local_response_normalization(CNN)
CNN = conv_2d(CNN,64,5,activation='relu',regularizer='L2')
CNN =max_pool_2d(CNN,2)
CNN = local_response_normalization(CNN)
CNN = fully_connected(CNN,1024,activation=None)
CNN = dropout(CNN,0.5)
CNN = fully_connected(CNN,10,activation='softmax')
CNN = regression(CNN,optimizer='adam',learning_rate=0.0001,loss='categorical_crossentropy',name='target')
model = tflearn.DNN(CNN,tensorboard_verbose=0,tensorboard_dir='MNIST_tflearn_board/',checkpoint_path='MNIST_tflearn_checkpoints/checkpoint')
model.fit({'input':x},{'target':y},n_epoch=3,validation_set=({'input': x_test},{'target': y_test}),snapshot_step=1000,show_metric=True,run_id='convnet_mnist')

#训练集结束后评估
evaluation  = model.evaluate({'input':x_test},{'target': y_test})
print(evaluation) #输出准确率[0.9852]

#查看预测结果
pred = model.predict({'input':x_test})
print((np.argmax(test_y,1)==np.argmax(pred,1)).mean())

#########################################################
#从网址读取图片
import urllib.request
url='https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1567673429047&di=a81560189960acb86bd45ab6b3ff0821&imgtype=0&src=http%3A%2F%2Fa.hiphotos.baidu.com%2Fimage%2Fpic%2Fitem%2F0ff41bd5ad6eddc40189fc4133dbb6fd52663319.jpg'
im_as_string = urllib.request.urlopen(url).read()

im = tf.image.decode_jpeg(im_as_string, channels=3)
#对于png,使用以下代码
im = tf.image.decode_png(im_as_string, channels=3)

#要从计算机加载图像,可以在目标目录中创建一个文件名队列
filename_queue = tf.train.string_input_producer(tf.train.match_filenames_once('./images/*.jpg'))
img_reader = tf.WholeFileReader()
_,imgae_file = img_reader.read(fielname_queue)
image = tf.image.decode_jpeg(image_file)

sess=tf.Session()
sess.run(im)


#########################队列操作
import tensorflow as tf
sess = tf.InteractiveSession
sess = tf.InteractiveSession()
queue1 = tf.FIFOQueue(capacity=10, dtypes=[tf.string])
enque_op = queue1.enqueue(["F"])
sess.run(queue1.size())
sess.run(enque_op)
sess.run(queue1.size())
enque_op = queue1.enqueue(['I'])
enque_op.run()
enque_op = queue1.enqueue(['F'])
enque_op.run()
enque_op = queue1.enqueue(['O'])
enque_op.run()
sess.run(queue1.size())
x = queue1.dequeue()
x.eval
x.eval()
x = queue1.dequeue()
x.eval()
x = queue1.dequeue()
x.eval()
x = queue1.dequeue()
x.eval()




##########################多线程
import threading
import time
gen_random_normal = tf.random_normal(shape=())
queue = tf.FIFOQueue(capacity=100,dtypes=[tf.float32],shapes=())
enque = queue.enqueue(gen_random_normal)
def add():
    for i in range(10):
def add():
    for i in range(10):
        sess.run(enque)

#创建多线程列表       
threads = [threading.Thread(target=add, args=()) for i in range(10)]
threads
for t in threads:
    t.start()
    
print(sess.run(queue.size()))
time.sleep(0.01)
print(sess.run(queue.size()))
time.sleep(0.01)
print(sess.run(queue.size()))
x = queue.dequeue_many(10)
print(x.eval())
sess.run(queue.size())
#############################

########################tf.train.Coordinator 多线程协调器
gen_random_normal = tf.random_normal(shape=())
queue = tf.FIFOQueue(capacity=100,dtypes=[tf.float32],shapes=())
enque = queue.enqueue(gen_random_normal)
def add(coord, i):
    while not coord.should_stop():
        sess.run(enque)
        if i == 11:
            coord.request_stop()
            
coord = tf.train.Coordinator()
threads = [threading.Thread(target=add,args=(coord,i)) for i in range(10)]
coord.join(threads)
for t in threads:
    t.start()
    
print(sess.run(queue.size()))

###################使用内置的tf.train.QueueRunner
gen_random_normal = tf.random_normal(shape=())
queue = tf.RandomShuffleQueue(capacity=100,dtypes=[tf.float32],min_after_dequeue=1)
enque_op = queue.enqueue(gen_random_normal)
qr = tf.train.QueueRunner(queue, [enque_op] * 4)
coord = tf.train.Coordinator()
enque_threads = qr.create_threads(sess, coord=coord, start=True)
coord.request_stop()
coord.join(enque_threads)

#############################################完整示例