how to print all parameters of a keras 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
• Can't use Keras MeanIoU to train semantic segmentation model

  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())
      return layer
  
  
  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',
          'block_13_expand_relu',
          'block_16_project',
      ]
      base_model_outputs = [base_model.get_layer(name).output for name in layer_names]
      down_stack = Model(inputs=base_model.input, outputs=base_model_outputs)
      down_stack.trainable = False
  
      up_stack = [
          upsample(512, 3),
          upsample(256, 3),
          upsample(128, 3),
          upsample(64, 3)
      ]
  
      skips = down_stack(inputs)
      x = skips[-1]
      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.
• 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
• How to make a chatbot for discord using python

  I need advise and/or resources to make a chatbot for discord in python, i have some knowledge of python and the discord api but I know nothing about chat bots or how to implement them in python, can anyone lead me to resources about chatbots and artificial intelligence?
• Image Background Remover Using Python

  I want to make Image Background Remover Using Python But I do not know how much data It will take and time to reach the accuracy of remove.bg I'm using U-2-Net Ai models https://github.com/xuebinqin/U-2-Net/ Some results are same but not every result is as good enough as remove.bg In a rating I would tell my app as 2/5 and remove.bg as 4/5 Please tell me How can I achieve accuracy like remove.bg Any help or suggestions are appreciated. Thanks