Difference between Python's cv2.imread(), Tensorflow's JS tf.node.decodeImage()

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  from this code

        import { View, Text } from 'react-native'
      
      import React from 'react'
      
      export default function App() {
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      <Text>Apps</Text>   
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  it's formatted to this

  import {
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    Text
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  import React from 'react'
  
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      View >
  
      <
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      <
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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

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• 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")
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  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
  
  
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  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.

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  I'm working on a binary semantic segmentation problem. I built an UNet model with MobileNetV2 backbone. Here is my model code:

  def upsample(filters, size, apply_dropout=False):
      initializer = tf.random_normal_initializer(0., 0.02)
      layer = Sequential()
      layer.add(layers.Conv2DTranspose(filters, size, strides=2, padding='same', kernel_initializer=initializer,
                                       use_bias=False))
      layer.add(layers.BatchNormalization())
      if apply_dropout:
          layer.add(layers.Dropout(0.5))
      layer.add(layers.ReLU())
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  def UNet(image_size, num_classes):
      inputs = Input(shape=image_size + (3,))
  
      base_model = applications.MobileNetV2(input_shape=image_size + (3,), include_top=False)
      layer_names = [
          'block_1_expand_relu',
          'block_3_expand_relu',
          'block_6_expand_relu',
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      down_stack = Model(inputs=base_model.input, outputs=base_model_outputs)
      down_stack.trainable = False
  
      up_stack = [
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          upsample(256, 3),
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      skips = down_stack(inputs)
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      skips = reversed(skips[:-1])
  
      for up, skip in zip(up_stack, skips):
          x = up(x)
          x = layers.Concatenate()([x, skip])
  
      outputs = layers.Conv2DTranspose(filters=num_classes, kernel_size=3, strides=2, padding='same')(x)
  
      return Model(inputs, outputs)
  

  To load the images and masks for training, I built an image loader inherits from keras.Sequnce.

  class ImageLoader(utils.Sequence):
  
      def __init__(self, batch_size, img_size, img_paths, mask_paths):
          self.batch_size = batch_size
          self.img_size = img_size
          self.img_paths = img_paths
          self.mask_paths = mask_paths
  
      def __len__(self):
          return len(self.mask_paths) // self.batch_size
  
      def __getitem__(self, idx):
          i = idx * self.batch_size
          batch_img_paths = self.img_paths[i:i + self.batch_size]
          batch_mask_paths = self.mask_paths[i:i + self.batch_size]
  
          x = np.zeros((self.batch_size,) + self.img_size + (3,), dtype='float32')
          for j, path in enumerate(batch_img_paths):
              img = utils.load_img(path, target_size=self.img_size)
              img = utils.img_to_array(img)
              x[j] = img
  
          y = np.zeros((self.batch_size,) + self.img_size + (1,), dtype='uint8')
          for j, path in enumerate(batch_mask_paths):
              img = utils.load_img(path, target_size=self.img_size, color_mode='grayscale')
              img = utils.img_to_array(img)
              # [0, 255] -> [0, 1]
              img //= 255
              y[j] = img
  
          return x, y
  

  In my segmentation problem, all the labels are in the range [0, 1]. However, when I try to compile and then fit the model using Adam optimizer, Sparse categorical cross entropy loss and metric tf.keras.metrics.MeanIoU, I encountered with the following problem:

  Node: 'confusion_matrix/assert_non_negative_1/assert_less_equal/Assert/AssertGuard/Assert'
  2 root error(s) found.
    (0) INVALID_ARGUMENT:  assertion failed: [`predictions` contains negative values.  ] [Condition x >= 0 did not hold element-wise:] [x (confusion_matrix/Cast:0) = ] [-1 -1 -1...]
       [[{{node confusion_matrix/assert_non_negative_1/assert_less_equal/Assert/AssertGuard/Assert}}]]
       [[confusion_matrix/assert_less_1/Assert/AssertGuard/pivot_f/_31/_67]]
    (1) INVALID_ARGUMENT:  assertion failed: [`predictions` contains negative values.  ] [Condition x >= 0 did not hold element-wise:] [x (confusion_matrix/Cast:0) = ] [-1 -1 -1...]
       [[{{node confusion_matrix/assert_non_negative_1/assert_less_equal/Assert/AssertGuard/Assert}}]]
  

  At first, I used accuracy as a metrics for training and I didn't encounter this problem, however when I changed to MeanIoU, this problem happened. Does anyone know how to fix this problem? Thank you very much!

  UPDATE: I've searched on StackOverflow and found this question about a similar error, however the fix mentioned in that link (reduce learning rate) doesn't work in my case.
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  Error: Uncaught (in promise) Error: Error when checking model : the Array of Tensors that you are passing to your model is not the size the the model expected. Expected to see 1 Tensor(s), but instead got 150 Tensors(s).

  async function load_model() {
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