仅在名称为空的情况下按名称删除特定文件夹

问题描述

我正在与一个客户合作，该客户希望仅在其内容为空时才删除特定文件夹。 我们正在尝试使用Powershell解决此问题，以下是我到目前为止提出的内容。它已成功删除“ 2019 DOCS！”。文件夹，但全部删除，而不是空的。

我似乎在查找字符串中的空文件夹的属性时遇到问题？

import tensorflow as tf
from tensorflow.keras.applications.inception_v3 import InceptionV3
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential,Model
from tensorflow.keras.layers import Conv2D,MaxPooling2D,Activation,Dropout,Flatten,Dense
from tensorflow.keras import backend as K
from tensorflow.keras import metrics,optimizers
import matplotlib.pyplot as plt

train_datagen = ImageDataGenerator( 
    rescale=1. / 255,rotation_range = 30,zoom_range = 0.2,width_shift_range=0.1,height_shift_range=0.1,validation_split = 0.15) 
  
test_datagen = ImageDataGenerator(rescale=1. / 255) 
  
train_generator = train_datagen.flow_from_directory( 
    train_dir,target_size = (75,75),batch_size = 214,class_mode = 'categorical',subset='training') 
  
#validation_generator = test_datagen.flow_from_directory( 
#    validation_dir,#    target_size = (75,#    batch_size = 37,#    class_mode = 'categorical',#    subset = 'validation')

test_generator = test_datagen.flow_from_directory(
    test_dir,target_size=(75,batch_size = 32,class_mode = 'categorical')

# Inspect batch
sample_training_images,_ = next(train_generator)

from tensorflow.keras.applications.inception_v3 import InceptionV3
def model_output_for_TL (pre_trained_model,last_output):    
    x = Flatten()(last_output)
    
    # Dense hidden layer
    x = Dense(1024,activation='relu')(x)
    x = Dropout(0.5)(x)
    
    # Output neuron. 
    x = Dense(2,activation='softmax')(x)
    
    model = Model(pre_trained_model.input,x)
    
    return model
pre_trained_model = InceptionV3(input_shape = (75,75,3),include_top = False,classes=173,weights = 'imagenet')
for layer in pre_trained_model.layers:
  layer.trainable = False
last_layer = pre_trained_model.get_layer('mixed5')
last_output = last_layer.output
model_TL = model_output_for_TL(pre_trained_model,last_output)

model_TL.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['accuracy'])
history_TL = model_TL.fit(
      train_generator,steps_per_epoch=10,epochs=60,verbose=2)
      #validation_data = validation_generator)
tf.keras.models.save_model(model_TL,'my_model.hdf5')

解决方法

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