"0    It really boggles my mind when someone comes across a movie like this and claims it to be one of the worst slasher films out there. This is by far not one of the worst out there"     still not a good movie     but not the worst nonetheless. Go see something like Death Nurse or Blood Lake and then come back to me and tell me if you think the Night Brings Charlie is the worst. The film has decent camera work and editing     which is way more than I can say for many more extremely obscure slasher films.<br /><br />The film doesn't deliver on the on-screen deaths     there's one death where you see his pruning saw rip into a neck     but all other deaths are hardly interesting. But the lack of on-screen graphic violence doesnt mean this isn't a slasher film     just a bad one.<br /><br />The film was obvIoUsly intended not to be taken too serIoUsly. The film came in at the end of the second slasher cycle     so it certainly was a reflection on traditional slasher elements     done in a tongue in cheek way. For example     after a kill     Charlie goes to the towns 'welcome sign and marks the population down one less. This is something that can only get a laugh.<br /><br />If you're into slasher films     definitely give this film a watch. It is slightly different than your usual slasher film with possibility of two killers     but not by much. The comedy of the movie is pretty much telling the audience to relax and not take the movie so god darn serIoUs. You may forget the movie     you may remember it. Ill remember it because I love the name.                                                                                                            
"0    Mary Pickford becomes the chieftain of a Scottish clan after the death of her father"     and then has a romance. As fellow commenter Snow Leopard said     the film is rather episodic to begin. Some of it is amusing     such as Pickford whipping her clansmen to church     while some of it is just there. All in all     the story is weak     especially the recycled     contrived romance plot-line and its climax. The transfer is so dark its difficult to appreciate the scenery     but even accounting for that     this doesn't appear to be director Maurice Tourneurs best work. Pickford and Tourneur collaborated once more in the somewhat more accessible 'The Poor Little Rich Girl     typecasting Pickford as a child character.                                                                                                                        0    Well"     at least my theater group did     lol. So of course I remember watching Grease since I was a little girl     while it was never my favorite musical or story     it does still hold a little special place in my heart since its still a lot of fun to watch. I heard horrible things about Grease 2 and that's why I decided to never watch it     but my boyfriend said that it really wasnt all that bad and my friend agreed     so I decided to give it a shot     but I called them up and just laughed. First off the plot is totally stolen from the first one and it wasn't really clever     not to mention they just used the same characters     but with different names and actors. Tell me     how did the Pink Ladies and T-Birds continue years on after the former gangs left? Not to mention the creator face motor cycle enemy     gee     what a striking resemblance to the guys in the first film as well as these T-Birds were just stupid and ridiculous.<br /><br />Another year at Rydell and the music and dancing hasnt stopped. But when a new student who is Sandy's cousin comes into the scene     he is love struck by a pink lady     Stephanie. But she must stick to the code where only Pink Ladies must stick with the T-Birds     so the new student     decides to train as a T-Bird to win her heart. So he dresses up as a rebel motor cycle bandit who can ride well and defeat the evil bikers from easily kicking the T-Birds butts. But will he tell Stephanie who he really is or will she find out on her own? Well     find out for yourself.<br /><br />Grease 2 is like a silly TV show of some sort that didn't work. The gang didnt click as well as the first Grease did     not to mention Frenchy coming back was a bit silly and unbelievable     because I thought that she graduated from Rydell     but apparently she didn't. The songs were not really that catchy; Im glad that Michelle was able to bounce back so fast     but that's probably because she was the only one with talent in this silly little sequel     I wouldnt really recommend this film     other than if you are curIoUs     but I warned you     this is just a pathetic attempt at more money from the famous musical.<br /><br />2/10                                                            1    I must give How She Move a near-perfect rating because the content is truly great. As a prevIoUs reviewer commented"     I have no idea how this film has found itself in IMDBs bottom 100 list! Thats absolutely ridiculous! Other films--particular those that share the dance theme--can't hold a candle to this one in terms of its combination of top-notch     believable acting     and amazing dance routines.<br /><br />From start to finish the underlying story (this is not just about winning a competition) is very easy to delve into     and surprisingly realistic. None of the main characters in this are 2-dimensional by any means and     by the end of the film     its very easy to feel emotionally invested in them. (And     even if you're not the crying type     you might get a little weepy-eyed before the credits roll.) <br /><br />I definitely recommend this film to dance-lovers and     even more so     to those who can appreciate a poignant and well-acted storyline. How She Move isnt perfect of course (what film is?)     but it's definitely a cut above movies that use pretty faces to hide a half-baked plot and/or characters who lack substance. The actors and settings in this film make for a very realistic ride that is equally enthralling thanks to the amazing talent of the dancers!                                                                                                                    
0    I must say"     when I read the storyline on the back of the case     It sounded really interesting     but when I started to watch the movie seemed boring at first and even more at the end. Some scenes are way too long and the story has not been worked out properly.                                                                                                                                                    
0    i am 13 and i hated this film its the worst film on earth i totally wasted my time watching it and was disappointed with it cause on the cover and on the back the film it looks pretty good"     but i was wrong its bad. but when i saw delta she was totally different and a bad actress and i really didnt know how old the 2 girls was trying to be i was so confused. the film was in some parts confusing and i didn't enjoy it at all but i watched all the film just to see if it was going to get better but it didnt     it was boring    dull and did i say BORING.and i don't think many other people liked it as well as me.boring boring boring                                                                                                                                                    
0    The acting may be okay"     the more u watch this movie     the more u wish you werent     this movie is so horrible     that if I could get a hold of every copy     I would burn them all and not look back     this movie is terrible!!                                                                                                                                        0    I've seen some bad things in my time. A half dead cow trying to get out of waist high mud; a head on collision between two cars; a thousand plates smashing on a kitchen floor; human beings living like animals.<br /><br />But never in my life have I seen anything as bad as The Cat in the Hat.<br /><br />This film is worse than 911"     worse than Hitler     worse than Vllad the Impaler     worse than people who put kittens in microwaves.<br /><br />It is the most disturbing film of all time     easy.<br /><br />I used to think it was a joke     some elaborate joke and that Mike Myers was maybe a high cocaine sniffing drug addled betting junkie who lost a bet or something.<br /><br />I shudder                                                                                                                                            

import numpy as np 
 pandas as pd 
from matplotlib  pyplot as plt
 tensorflow as tf
from tensorflow.keras  models,layers,preprocessing,optimizers,losses,metrics
from tensorflow.keras.layers.experimental.preprocessing  TextVectorization
 re,string
 
train_data_path = ./data/imdb/train.csv"
test_data_path =  ./data/imdb/test.csv
 
MAX_WORDS = 10000  # 仅考虑最高频的10000个词
MAX_LEN = 200   每个样本保留200个词的长度
BATCH_SIZE = 20 
 
 
 构建管道
def split_line(line):
    arr = tf.strings.split(line,\t)
    label = tf.expand_dims(tf.cast(tf.strings.to_number(arr[0]),tf.int32),axis = 0)
    text = tf.expand_dims(arr[1],1)"> 0)
    return (text,label)
 
ds_train_raw =  tf.data.TextLineDataset(filenames = [train_data_path]) \
   .map(split_line,num_parallel_calls = tf.data.experimental.AUTOTUNE) \
   .shuffle(buffer_size = 1000).batch(BATCH_SIZE) \
   .prefetch(tf.data.experimental.AUTOTUNE)
 
ds_test_raw = tf.data.TextLineDataset(filenames = [test_data_path]) \
   .map(split_line,1)"> tf.data.experimental.AUTOTUNE) \
   .batch(BATCH_SIZE) \
   .prefetch(tf.data.experimental.AUTOTUNE)
 
 
 构建词典
 clean_text(text):
    lowercase = tf.strings.lower(text)
    stripped_html = tf.strings.regex_replace(lowercase,1)"><br />',1)">' )
    cleaned_punctuation = tf.strings.regex_replace(stripped_html,[%s]' % re.escape(string.punctuation),1)">'')
     cleaned_punctuation
 
vectorize_layer = TextVectorization(
    standardize=clean_text,split = whitespace,max_tokens=MAX_WORDS-1,有一个留给占位符
    output_mode=intMAX_LEN)
 
ds_text = ds_train_raw.map(lambda text,label: text)
vectorize_layer.adapt(ds_text)
print(vectorize_layer.get_vocabulary()[0:100])
 
 
 单词编码
ds_train = ds_train_raw.map(

[btheandaoftoisinitithisthatwasasforwithmoviebutfilmonnotyouhisarehavebeheoneitsatallbyantheyfromwhosolikeherjustorabouthasifoutsometherewhatgoodmorewhenverysheevenmynowoulduptimeonlywhichstoryreallytheirwerehadseecanmethanwemuchwellgetbeenwillintopeoplealsootherdobadbecausegreatfirsthowhimmostdontmadethenthemfilmsmovieswaymakecouldtooanyaftercharacters']

 演示自定义模型范例，实际上应该优先使用Sequential或者函数式API
 
tf.keras.backend.clear_session()
 
class CnnModel(models.Model):
    def __init__(self):
        super(CnnModel,self).()
 
     build(self,input_shape):
        self.embedding = layers.Embedding(MAX_WORDS,7,input_length=MAX_LEN)
        self.conv_1 = layers.Conv1D(16,kernel_size= 5,name = conv_1",activation = relu)
        self.pool = layers.MaxPool1D()
        self.conv_2 = layers.Conv1D(128,kernel_size=2,1)">conv_2)
        self.flatten = layers.Flatten()
        self.dense = layers.Dense(1,1)">sigmoid)
        super(CnnModel,self).build(input_shape)
 
     call(self,x):
        x = self.embedding(x)
        x = self.conv_1(x)
        x = self.pool(x)
        x = self.conv_2(x)
        x = self.flatten(x)
        x = self.dense(x)
        (x)
 
model = CnnModel()
model.build(input_shape =(None,MAX_LEN))
model.summary()

 打印时间分割线
@tf.function
 printbar():
    ts = tf.timestamp()
    today_ts = ts%(24*60*60)
 
    hour = tf.cast(today_ts//3600+8,tf.int32)%tf.constant(24)
    minite = tf.cast((today_ts%3600)//60),tf.int32)
 
     timeformat(m):
        if tf.strings.length(tf.strings.format({}:
            return(tf.strings.format(0{}else tf.strings.join([timeformat(hour),timeformat(minite),timeformat(second)],separator = :)
    tf.print(=========="*8,end = ""print(timestring)
optimizer = optimizers.Nadam()
loss_func = losses.BinaryCrossentropy()
 
train_loss = metrics.Mean(name=train_loss)
train_metric = metrics.BinaryAccuracy(name=train_accuracy)
 
valid_loss = metrics.Mean(name=valid_loss)
valid_metric = metrics.BinaryAccuracy(name=valid_accuracy)
 
 
@tf.function
 train_step(model,features,labels):
    with tf.GradientTape() as tape:
        predictions = model(features,training = True)
        loss = loss_func(labels,predictions)
    gradients = tape.gradient(loss,model.trainable_variables)
    optimizer.apply_gradients(zip(gradients,model.trainable_variables))
 
    train_loss.update_state(loss)
    train_metric.update_state(labels,predictions)
 
 
@tf.function
 valid_step(model,labels):
    predictions = model(features,1)"> False)
    batch_loss = train_model(model,ds_train,ds_valid,epochs):
    for epoch in tf.range(1,epochs+1):
 
        for features,labels in ds_train:
            train_step(model,labels)
 
         ds_valid:
            valid_step(model,1)">此处logs模板需要根据metric具体情况修改
        logs = Epoch={},Loss:{},Accuracy:{},Valid Loss:{},Valid Accuracy:{}' 
 
        if epoch%1==0:
            printbar()
            tf.(tf.strings.format(logs,(epoch,train_loss.result(),train_metric.result(),valid_loss.result(),valid_metric.result())))
            tf.)
 
        train_loss.reset_states()
        valid_loss.reset_states()
        train_metric.reset_states()
        valid_metric.reset_states()
 
train_model(model,ds_test,epochs = 6)

================================================================================14:45:06
Epoch=1,Loss:0.474225521,Accuracy:0.7376,Valid Loss:0.336961836,Valid Accuracy:0.8526

================================================================================14:45:12
Epoch=2,Loss:0.245222151,Accuracy:0.9035,Valid Loss:0.326947063,Valid Accuracy:0.8666

================================================================================14:45:17
Epoch=3,Loss:0.165854618,Accuracy:0.93795,Valid Loss:0.365531504,Valid Accuracy:0.867

================================================================================14:45:23
Epoch=4,Loss:0.104812928,Accuracy:0.96395,Valid Loss:0.448238105,Valid Accuracy:0.861

================================================================================14:45:29
Epoch=5,Loss:0.0595887862,Accuracy:0.98125,Valid Loss:0.602612,Valid Accuracy:0.8624

================================================================================14:45:35
Epoch=6,Loss:0.0318539739,Accuracy:0.9905,Valid Loss:0.762770712,Valid Accuracy:0.8598

 evaluate_model(model,ds_valid):
     ds_valid:
         valid_step(model,labels)
    logs = Valid Loss:{},1)"> 
    tf.五，使用模型
可以使用以下方法:

model.predict(ds_test)
model(x_test)
model.call(x_test)
model.predict_on_batch(x_test)

推荐优先使用model.predict(ds_test)方法，既可以对Dataset，也可以对Tensor使用。

model.predict(ds_test)for x_test,_ in ds_test.take(1):
    (model(x_test))
    以下方法等价：
    print(model.call(x_test))
    print(model.predict_on_batch(x_test))

评估结果：

tf.Tensor(
[[9.9007505e-01]
 [9.9999797e-01]
 [9.9836570e-01]
 [2.6509229e-06]
 [4.7592866e-01]
 [3.7760619e-05]
 [8.0391978e-08]
 [1.6816575e-05]
 [9.9996006e-01]
 [9.9695146e-01]
 [1.0000000e+00]
 [9.9962234e-01]
 [1.9009445e-08]
 [9.7622436e-01]
 [4.4549329e-06]
 [2.8802201e-01]
 [1.0730105e-04]
 [3.8324962e-03]
 [2.2874507e-03]
 [9.9966860e-01]],shape=(20,1),dtype=float32)

六，@R_65_301@
推荐使用TensorFlow原生方式@R_65_301@。

model.save(./data/tf_model_savedmodeltf)
export saved model.)
 
model_loaded = tf.keras.models.load_model()
model_loaded.predict(ds_test)

结果：

WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed  a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.
INFO:tensorflow:Assets written to: ./data/tf_model_savedmodel/assets
export saved model.
WARNING:tensorflow:No training configuration found in save file,so the model was *not* compiled. Compile it manually.
array([[0.99007505],[0.999998  ],[0.9983657 ],...,[0.99621141.        ]],dtype=float32)

 
参考：
开源电子书地址：https://lyhue1991.github.io/eat_tensorflow2_in_30_days/
GitHub 项目地址：https://github.com/lyhue1991/eat_tensorflow2_in_30_days
 原文链接：/tensorflow/991507.html