Can't use Keras MeanIoU to train semantic segmentation model

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• Python File Tagging System does not retrieve nested dictionaries in dictionary

  I am building a file tagging system using Python. The idea is simple. Given a directory of files (and files within subdirectories), I want to filter them out using a filter input and tag those files with a word or a phrase.

  If I got the following contents in my current directory:

  data/
     budget.xls
     world_building_budget.txt
  a.txt
  b.exe
  hello_world.dat
  world_builder.spec
  

  and I execute the following command in the shell:

  py -3 tag_tool.py -filter=world -tag="World-Building Tool"
  

  My output will be:

  These files were tagged with "World-Building Tool":
  data/ world_building_budget.txt
  hello_world.dat
  world_builder.spec
  

  My current output isn't exactly like this but basically, I am converting all files and files within subdirectories into a single dictionary like this:

  def fs_tree_to_dict(path_):
      file_token = ''
      for root, dirs, files in os.walk(path_):
          tree = {d: fs_tree_to_dict(os.path.join(root, d)) for d in dirs}
          tree.update({f: file_token for f in files})
          return tree
  

  Right now, my dictionary looks like this: key:''.

  In the following function, I am turning the empty values '' into empty lists (to hold my tags):

  def empty_str_to_list(d):
      for k,v in d.items():
          if v == '':
              d[k] = []
          elif isinstance(v, dict):
              empty_str_to_list(v)
  

  When I run my entire code, this is my output:

  hello_world.dat ['World-Building Tool']
  world_builder.spec ['World-Building Tool']
  

  But it does not see data/world_building_budget.txt. This is the full dictionary:

  {'data': {'world_building_budget.txt': []},  
  'a.txt': [],
  'hello_world.dat': [], 
  'b.exe': [], 
  'world_builder.spec': []}
  
  

  This is my full code:

  import os, argparse
  
  
  def fs_tree_to_dict(path_):
      file_token = ''
      for root, dirs, files in os.walk(path_):
          tree = {d: fs_tree_to_dict(os.path.join(root, d)) for d in dirs}
          tree.update({f: file_token for f in files})
          return tree
  
  
  def empty_str_to_list(d):
      for k, v in d.items():
          if v == '':
              d[k] = []
          elif isinstance(v, dict):
              empty_str_to_list(v)
  
  
  parser = argparse.ArgumentParser(description="Just an example", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
  
  parser.add_argument("--filter", action="store", help="keyword to filter files")
  parser.add_argument("--tag", action="store", help="a tag phrase to attach to a file")
  parser.add_argument("--get_tagged", action="store", help="retrieve files matching an existing tag")
  
  args = parser.parse_args()
  
  filter = args.filter
  tag = args.tag
  get_tagged = args.get_tagged
  current_dir = os.getcwd()
  
  files_dict = fs_tree_to_dict(current_dir)
  empty_str_to_list(files_dict)
  
  for k, v in files_dict.items():
      if filter in k:
          if v == []:
              v.append(tag)
              print(k, v)
  
          elif isinstance(v, dict):
              empty_str_to_list(v)
  
      if get_tagged in v:
          print(k, v)
  
• Actaully i am working on a project and in it, it is showing no module name pip_internal plz help me for the same. I am using pycharm(conda interpreter

   File "C:\Users\pjain\AppData\Local\Programs\Python\Python310\lib\runpy.py", line 196, in _run_module_as_main
      return _run_code(code, main_globals, None,
    File "C:\Users\pjain\AppData\Local\Programs\Python\Python310\lib\runpy.py", line 86, in _run_code
      exec(code, run_globals)
    File "C:\Users\pjain\AppData\Local\Programs\Python\Python310\Scripts\pip.exe\__main__.py", line 4, in <module>
    File "C:\Users\pjain\AppData\Local\Programs\Python\Python310\lib\site-packages\pip\_internal\__init__.py", line 4, in <module>
      from pip_internal.utils import _log
  

  I am using pycharm with conda interpreter.

  
• Looping the function if the input is not string

  I'm new to python (first of all) I have a homework to do a function about checking if an item exists in a dictionary or not.

  inventory = {"apple" : 50, "orange" : 50, "pineapple" : 70, "strawberry" : 30}
  
  def check_item():
      x = input("Enter the fruit's name: ")
      if not x.isalpha():
          print("Error! You need to type the name of the fruit")
      elif x in inventory:
          print("Fruit found:", x)
          print("Inventory available:", inventory[x],"KG")
      else:
          print("Fruit not found")
  check_item()
  

  I want the function to loop again only if the input written is not string. I've tried to type return Under print("Error! You need to type the name of the fruit") but didn't work. Help
• Only download certain label tf dataset

  Looking to do some fine tuning. The dataset (found here: https://knowyourdata-tfds.withgoogle.com/#dataset=sun397&filters=kyd%2Fsun397%2Flabel:%2Fh%2Fhouse&tab=ITEM&select=kyd%2Fsun397%2Flabel&item=%2Fh%2Fhouse%2Fsun_blpzjomvikwtulrq.jpg&expanded_groups=sun397) that Im trying to finetune w is pretty large and i just want to use/download the images with label /h/house. Any tips on how I can best accomplish this? Thanks!

  import tensorflow as tf
  import tensorflow_hub as hub
  import tensorflow_datasets as tfds
  import numpy as np
  import matplotlib.pyplot as plt
  import functools
  import pandas
  
  (train_ds, valid_ds), info = tfds.load("sun397", split=["train", "validation"], as_supervised=True, with_info=True, label = "/h/house")
  int_to_class_label = info.features['label'].int2str
  
  
• TFF: How can I train any model using a server running tff-runtime and a client running tff-client?

  I read all the tensorflow-federated tutorials, including this one https://www.tensorflow.org/federated/gcp_setup, but I couldn't understand how to use this for training a model.

  I'm doing a graduation project, to start I need to do this POC using tensorflow-federated to train a model with one server and one client in order to apply cross-silo setup for recognition of organs affected by covid in the future. If anyone can point me a direction, I'd be very grateful.
• how to print all parameters of a keras model

  I am trying to print all the 1290 parameters in dense_1 layer, but model.get_weights()[7] only show 10 parameters. How could I print all the 1290 parameters of dense_1 layer? What is the difference between model.get_weights() and model.layer.get_weights()

  >model.get_weights()[7]
  array([-2.8552295e-04, -4.3254648e-03, -1.8752701e-04,  2.3482188e-03,
         -3.4848123e-04,  7.6121779e-04, -2.7494309e-06, -1.9068648e-03,
          6.0777756e-04,  1.9550985e-03], dtype=float32)
  
  >model.summary()
  Model: "sequential"
  _________________________________________________________________
  Layer (type)                Output Shape              Param #   
  =================================================================
   conv2d (Conv2D)             (None, 26, 26, 32)        320       
                                                                   
   conv2d_1 (Conv2D)           (None, 24, 24, 64)        18496     
                                                                   
   max_pooling2d (MaxPooling2D  (None, 12, 12, 64)       0         
   )                                                               
                                                                   
   dropout (Dropout)           (None, 12, 12, 64)        0         
                                                                   
   flatten (Flatten)           (None, 9216)              0         
                                                                   
   dense (Dense)               (None, 128)               1179776   
                                                                   
   dropout_1 (Dropout)         (None, 128)               0         
                                                                   
   dense_1 (Dense)             (None, 10)                1290      
  _________________________________________________________________
                                                                   
  =================================================================
  Total params: 1,199,882
  Trainable params: 1,199,882
  Non-trainable params: 0
  _________________________________________________________________
  
• Training plot is not appearing properly for keras model

  I have data where I need to train it with X and Y. Traning part is done but when I want to plot the prediction and actual data, it is appearing with so many lines instead of showing just non-linear regression line.

  model= Sequential()
  model.add(Dense(7,input_dim=1, activation="tanh"))
  model.add(Dense(1))
  
  model.compile(loss="mse", 
  optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), metrics= 
  ["mae"])
  history=model.fit(X,Y,epochs=1000)
  
  predict=model.predict(X)
  plt.scatter(X, Y,edgecolors='g')
  plt.plot(X, predict,'r')
  plt.legend([ 'Predictated Y' ,'Actual Y'])
  plt.show()
  

  Please see the attached imageplotting image