TypeError: can't pickle _thread.RLock objects in mutithreading

I have been doing my final year project and trying to make my model pickeble but seems like there have been an error poping up.I searched many similar types of questions in stack over flow but could not get the solution cause it's an logical error and I am quite new in tensorflow and python.Please help me with this error.

Note:I am using Python 3.7.6

import numpy as np  import pickle  import cv2  from os import listdir  from sklearn.preprocessing import LabelBinarizer  from keras.models import Sequential  from keras.layers.normalization import BatchNormalization  from keras.layers.convolutional import Conv2D  from keras.layers.convolutional import MaxPooling2D  from keras.layers.core import Activation, Flatten, Dropout, Dense  from keras import backend as K  from keras.preprocessing.image import ImageDataGenerator  from keras.optimizers import Adam  from keras.preprocessing import image  from keras.preprocessing.image import img_to_array  from sklearn.preprocessing import MultiLabelBinarizer  from sklearn.model_selection import train_test_split  import matplotlib.pyplot as plt    EPOCHS = 25  INIT_LR = 1e-3  BS = 32  default_image_size = tuple((256, 256))  image_size = 0  directory_root = 'C:\\Users\\User\\PLANT_DISEASE\\plantdisease_dataset'  width=256  height=256  depth=3    def convert_image_to_array(image_dir):      try:          image = cv2.imread(image_dir)          if image is not None :              image = cv2.resize(image, default_image_size)                 return img_to_array(image)          else :              return np.array([])      except Exception as e:          print(f"Error : {e}")          return None    image_list, label_list = [], []  try:      print("[INFO] Loading images ...")      root_dir = listdir(directory_root)      for directory in root_dir :          # remove .DS_Store from list          if directory == ".DS_Store" :              root_dir.remove(directory)        for plant_folder in root_dir :          plant_disease_folder_list = listdir(f"{directory_root}/{plant_folder}")                    for disease_folder in plant_disease_folder_list :              # remove .DS_Store from list              if disease_folder == ".DS_Store" :                  plant_disease_folder_list.remove(disease_folder)            for plant_disease_folder in plant_disease_folder_list:              print(f"[INFO] Processing {plant_disease_folder} ...")              plant_disease_image_list = listdir(f"{directory_root}/{plant_folder}/{plant_disease_folder}/")                                for single_plant_disease_image in plant_disease_image_list :                  if single_plant_disease_image == ".DS_Store" :                      plant_disease_image_list.remove(single_plant_disease_image)                                      for image in plant_disease_image_list[:200]:                  image_directory = f"{directory_root}/{plant_folder}/{plant_disease_folder}/{image}"                  if image_directory.endswith(".jpg") == True or image_directory.endswith(".JPG") == True:                      image_list.append(convert_image_to_array(image_directory))                      label_list.append(plant_disease_folder)      print("[INFO] Image loading completed")    except Exception as e:      print(f"Error : {e}")    image_size = len(image_list)    label_binarizer = LabelBinarizer()  image_labels = label_binarizer.fit_transform(label_list)  pickle.dump(label_binarizer,open('label_transform.pkl', 'wb'))  n_classes = len(label_binarizer.classes_)    print(label_binarizer.classes_)    np_image_list = np.array(image_list, dtype=np.float16) / 225.0    print("[INFO] Spliting data to train, test")  x_train, x_test, y_train, y_test = train_test_split(np_image_list, image_labels, test_size=0.2, random_state = 42)    aug = ImageDataGenerator(      rotation_range=25, width_shift_range=0.1,      height_shift_range=0.1, shear_range=0.2,       zoom_range=0.2,horizontal_flip=True,       fill_mode="nearest")    model = Sequential()  inputShape = (height, width, depth)  chanDim = -1  if K.image_data_format() == "channels_first":      inputShape = (depth, height, width)      chanDim = 1  model.add(Conv2D(32, (3, 3), padding="same",input_shape=inputShape))  model.add(Activation("relu"))  model.add(BatchNormalization(axis=chanDim))  model.add(MaxPooling2D(pool_size=(3, 3)))  model.add(Dropout(0.25))  model.add(Conv2D(64, (3, 3), padding="same"))  model.add(Activation("relu"))  model.add(BatchNormalization(axis=chanDim))  model.add(Conv2D(64, (3, 3), padding="same"))  model.add(Activation("relu"))  model.add(BatchNormalization(axis=chanDim))  model.add(MaxPooling2D(pool_size=(2, 2)))  model.add(Dropout(0.25))  model.add(Conv2D(128, (3, 3), padding="same"))  model.add(Activation("relu"))  model.add(BatchNormalization(axis=chanDim))  model.add(Conv2D(128, (3, 3), padding="same"))  model.add(Activation("relu"))  model.add(BatchNormalization(axis=chanDim))  model.add(MaxPooling2D(pool_size=(2, 2)))  model.add(Dropout(0.25))  model.add(Flatten())  model.add(Dense(1024))  model.add(Activation("relu"))  model.add(BatchNormalization())  model.add(Dropout(0.5))  model.add(Dense(n_classes))  model.add(Activation("softmax"))    model.summary()    opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)  # distribution  model.compile(loss="binary_crossentropy", optimizer=opt,metrics=["accuracy"])  # train the network  print("[INFO] training network...")    history = model.fit(      aug.flow(x_train, y_train, batch_size=BS),      validation_data=(x_test, y_test),      steps_per_epoch=len(x_train) // BS,      epochs=EPOCHS, verbose=1      )    acc = history.history['accuracy']  val_acc = history.history['val_accuracy']  loss = history.history['loss']  val_loss = history.history['val_loss']  epochs = range(1, len(acc) + 1)  #Train and validation accuracy  plt.plot(epochs, acc, 'b', label='Training accurarcy')  plt.plot(epochs, val_acc, 'r', label='Validation accurarcy')  plt.title('Training and Validation accurarcy')  plt.legend()    plt.figure()  #Train and validation loss  plt.plot(epochs, loss, 'b', label='Training loss')  plt.plot(epochs, val_loss, 'r', label='Validation loss')  plt.title('Training and Validation loss')  plt.legend()  plt.show()    print("[INFO] Calculating model accuracy")  scores = model.evaluate(x_test, y_test)  print(f"Test Accuracy: {scores[1]*100}")  

The error part:

print("[INFO] Saving model...")  pickle.dump(model,open('cnn_model.pkl', 'wb'))  

error message:

[INFO] Saving model...  ---------------------------------------------------------------------------  TypeError                                 Traceback (most recent call last)  <ipython-input-17-a6d98bf59220> in <module>        1 print("[INFO] Saving model...")  ----> 2 pickle.dump(model,open('cnn_model.pkl', 'wb'))    TypeError: can't pickle _thread.RLock objects  

Thank you.

https://stackoverflow.com/questions/65730304/typeerror-cant-pickle-thread-rlock-objects-in-mutithreading January 15, 2021 at 11:57AM