Incompatible shapes error, when using convolutional LSTM network

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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
• How would I put my own dataset into this code?

  I have been looking at a Tensorflow tutorial for unsupervised learning, and I'd like to put in my own dataset; the code currently uses the MNIST dataset. I know how to create my own datasets in Tensorflow, but I have trouble setting the code used here to my own. I am pretty new to Tensorflow, and the filepath to my dataset in my project is \data\training and \data\test-val\

  # Python ≥3.5 is required
  import sys
  assert sys.version_info >= (3, 5)
  
  # Scikit-Learn ≥0.20 is required
  import sklearn
  assert sklearn.__version__ >= "0.20"
  
  # TensorFlow ≥2.0-preview is required
  import tensorflow as tf
  from tensorflow import keras
  assert tf.__version__ >= "2.0"
  
  # Common imports
  import numpy as np
  import os
  
  (X_train_full, y_train_full), (X_test, y_test) = keras.datasets.fashion_mnist.load_data()
  X_train_full = X_train_full.astype(np.float32) / 255
  X_test = X_test.astype(np.float32) / 255
  X_train, X_valid = X_train_full[:-5000], X_train_full[-5000:]
  y_train, y_valid = y_train_full[:-5000], y_train_full[-5000:]
  
  def rounded_accuracy(y_true, y_pred):
      return keras.metrics.binary_accuracy(tf.round(y_true), tf.round(y_pred))
  
  tf.random.set_seed(42)
  np.random.seed(42)
  
  conv_encoder = keras.models.Sequential([
      keras.layers.Reshape([28, 28, 1], input_shape=[28, 28]),
      keras.layers.Conv2D(16, kernel_size=3, padding="SAME", activation="selu"),
      keras.layers.MaxPool2D(pool_size=2),
      keras.layers.Conv2D(32, kernel_size=3, padding="SAME", activation="selu"),
      keras.layers.MaxPool2D(pool_size=2),
      keras.layers.Conv2D(64, kernel_size=3, padding="SAME", activation="selu"),
      keras.layers.MaxPool2D(pool_size=2)
  ])
  conv_decoder = keras.models.Sequential([
      keras.layers.Conv2DTranspose(32, kernel_size=3, strides=2, padding="VALID", activation="selu",
                                   input_shape=[3, 3, 64]),
      keras.layers.Conv2DTranspose(16, kernel_size=3, strides=2, padding="SAME", activation="selu"),
      keras.layers.Conv2DTranspose(1, kernel_size=3, strides=2, padding="SAME", activation="sigmoid"),
      keras.layers.Reshape([28, 28])
  ])
  conv_ae = keras.models.Sequential([conv_encoder, conv_decoder])
  
  conv_ae.compile(loss="binary_crossentropy", optimizer=keras.optimizers.SGD(lr=1.0),
                  metrics=[rounded_accuracy])
  history = conv_ae.fit(X_train, X_train, epochs=5,
                        validation_data=[X_valid, X_valid])
  
  conv_encoder.summary()
  conv_decoder.summary()
  
  conv_ae.save("\models")
  

  Do note that I got this code from another StackOverflow answer.
• Comparison between object detection algorithm speeds

  I am writing my final degree project and I am having trouble to compare different algorithms in the state of the art. I am comparing ResNet, MobileNet SSD, YOLOv4, VGG16, and VGG19 used in embedded devices such as Jetson Nano or Raspberry pi. All algorithms are used for object detection but I am unable to find information about which one is faster or usually gets a higher accuracy. Also, I was looking if they can be used in low-performance devices. I would be grateful if someone is able to help me.

  Thanks in advance.
• Every time I train my CNN on matlab, is it remembering the old weights from the previous time I trained it? Or does it reset them?

  So for example, I have trained a CNN on my data using a learning rate of 0.0003 and 10 epochs, with a minibatch size of 32. After training it, lets say I get an accuracy of 0.7. Now I want to adjust the learning rate and the minibatch size and try training it again to see how the accuracy changes, using the trainNetwork Matlab function. My question is, is it training the model from scratch or is it training them using the weights previously calculated? I want it to start from scratch to prevent overfitting every time I adjust the hyperparamters. Sorry if this is intuitive and I'm being dumb lol I just wanna make sure.
• Preparing input data for LSTM layer with conditions

  I have a data frame that looks like the one below:

  DF.head(20):
  time        var1       var2       prob     
  12:30       10          12         85
  12:31       15          45         85
  12:32       18          12         85
  12:33       17          26         85
  12:34       11          14         85
  12:35       14          65         85
  12:36       19          29         92
  12:37       15          32         92
  12:38       13          44         92
  12:39       15          33         92
  12:40       11          15         92
  12:41       15          45         92
  12:42       13          44         94
  12:43       15          33         94
  12:44       11          15         94
  12:45       15          45         94
  12:46       13          44         92
  12:47       15          33         92
  12:48       11          15         92
  12:49       15          45         92
  

  I want to predict the value of prob for a sequence of 6 previous values. So for the given example, I will take two-time series -> var1 and var2 from time 12:30 to 12:35 to predict prob for 12:35. the input shape that will go to LSTM as per my knowledge will be (df. shape[0],6,1). but I do not know how to convert my input from 2 dimensions to 3 dimensions. I also have a condition where I need to see the previous 6 times only if they are all under the same prob value. so in the given example, I won't be able to take the previous 6 values for prob = 94 as 94 occurs only 4 times and I cannot make 6 timesteps from that.

  My pseudo code looks like this:

  for i in range(df.shape[0]):        #loop across all rows
    if final_df[i,'prob'] == final_df[i+1,'prob']:     #go until the value of prob change
        make multiple non overlaping dataframes of shape (6,2)
    else:
        continue
  

  I need help building the logic and preparing the input data for my LSTM.
• Replace bidirectional LSTM with GRU in coref?

  I am training the coarse-to-fine coreference model (for some other language than English) from Allennlp with template configs from bert_lstm.jsonnet. When I replace the type “lstm” of the context layer with “gru”, it works, but seems to have very little impact on training. The same 63 GB of RAM are consumed each epoch, validation f1-score is hovering around the same value. Is this change in config actually replace Bi-LSTM layer with Bi-GRU layer, or am I missing something?

      "context_layer": {
      "type": "gru",
      "bidirectional": true,
      "hidden_size": gru_dim,
      "input_size": bert_dim,
      "num_layers": 1
  },
  
• NaNs in predictions with LSTM

  I have an LSTM model that I have trained and tested it with a dataset. Now I want to test it to an other dataset and I use the following snippet:

  from keras.models import load_model
  import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.metrics import mean_squared_error
  from sklearn.metrics import mean_absolute_percentage_error
  
  model = load_model('lstm.h5')
  df = pd.read_csv('datasets/Residential_4.csv')
  data = df['energy_kWh'].values
  data = data.reshape((-1,1))
  scaler = MinMaxScaler(feature_range=(0,1))
  data = scaler.fit_transform(data)
  
  lookback = 7 * 24
  prediction_horizon = 24
  
  X_test, Y_test   = Create_Dataset(data, lookback, prediction_horizon)
  X_test  = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
  
  test_predict  = model.predict(X_test)
  
  test_predict  = scaler.inverse_transform(test_predict)
  Y_test = scaler.inverse_transform(Y_test)
  
  def Create_Dataset(df, lookback=1, prediction_horizon=1):
      X, Y = [], []
      for i in range(lookback, len(df)-lookback):
          X.append(df[i-lookback : i, 0])
          Y.append(df[i : i + prediction_horizon, 0])
      return np.array(X), np.array(Y)
  

  The problem however is that the test_predict has NaN values after the row 96. Any idea of why is this happening?
• Why the frame2video function doesn't work?

  I write a function in PyQt5 in order to all frames in one directory can convert to a single video. My code is :

  import os
  
  import cv2
  import numpy as np
  from PIL import Image
  
  
  def frame2video(im_dir, video_dir, fps):
      im_list = os.listdir(im_dir)
      im_list.sort(key=lambda x: int(x.split('.')[0]))
      img = Image.open(os.path.join(im_dir, im_list[0]))
      size = img.size
  
      fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
      videoWriter = cv2.VideoWriter(video_dir, fourcc, fps, size)
      for i in im_list:
          im_name = os.path.join(im_dir + i)
          frame = cv2.imdecode(np.fromfile(im_name, dtype=np.uint8), -1)
          videoWriter.write(frame)
      videoWriter.release()
  
  
  if __name__ == '__main__':
      video_storedir = 'G:/dataset/video_new.mp4'
      if not os.path.exists(video_storedir):
          os.mkdir(video_storedir)
      path = 'G:/dataset' + '/' + 'example_Trim' + '_new/'
      frame2video(path, video_storedir, 30)
  

  but it doesn't work, and run is still work, and the corresponding folder doesn't have the processed video....

  the screenshot is:
• How can I create a video streaming server using node.js?

  Until now I have only worked with rest-APIs but now I need to create a server which serves several hundred videos of different lengths to client and I have zero experience in video or audio streaming.

  I have seen few tutorials and know data will be served in chunks and I can control chunk size and client will need to have a range header and all that stuff, but all those tutorials were for simple servers that had a single endpoint and video. Will I be able to manage hundreds of videos the same way?

  It would be a great help if you can answer the questions below and guide me about things I need to know before I start development.

  1. Can I handle video files the same way I usually handle other files by storing it on server or using services like amazon s3 bucket?

  2. I read that webRTC can also be used for streaming should I use it or stick to the simple method?

  3. Are there any useful packages or technologies that can make development easier?

  4. Videos also need to be served on mobile application platforms will that change things, do I need something else to make it mobile app compatible?

  5. If you have a tutorial you think can be suitable for my needs, Can you please share it?

  I really appreciate your help and time. Feel free to mention any thing that I missed or need to know, thanks in advance.
• How to convert frames to mp4 using ffmpeg - Conversion Error

  I have 1,170 frames (.png), named from img_001.png to img_1170.png. I am trying to create an mp4 video from these frames using ffmpeg in windows. However, every command that I have used returns a conversion errors, some of which are:

  Error initializing output stream 0:0 -- Error while opening encoder for output stream #0:0 - maybe incorrect parameters such as bit_rate, rate, width or height Conversion failed!

  Appreciate any suggestions and codes.