Comparison between object detection algorithm speeds

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• 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.
• 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.
• How to make a chatbot for discord using python

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• 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
• 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
  _________________________________________________________________
  
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  How can i clone my working windows 10 too? (I dont use bootcamp assistant clear installation of windows because i loose audio connection in windows after bootcamp assistant installation).

  P.S. I hope there is a f2p decision, because dont want to pay 50$ for WinClone utility for MacOS, which I'm not 100% sure it works ._.

  Best wishes, Mike
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  I'm trying to change Storage Type from HDD to SSD on Cloud SQL after creating an instance as shown below but there are no options to do it:

  

  Is it impossible to change Storage Type from HDD to SSD on Cloud SQL after creating an instance? If impossible, are there any ways to do this?
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