TypeError: can't pickle _thread.RLock objects ( Deep Learning)

Question

I'm Getting:- loss: 0.4413 - accuracy: 0.8617 - val_loss: 0.7057 - val_accuracy: 0.8038

Code:

import numpy as np
import pandas as pd
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
import random 
import cv2
import imutils
import random
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelBinarizer
from keras.utils import np_utils
from tensorflow.keras.models import Sequential
from tensorflow.keras import optimizers
from sklearn.preprocessing import LabelBinarizer
from tensorflow.keras import backend as K
from tensorflow.keras.layers import Dense, Activation, Flatten, Dense,MaxPooling2D, Dropout
from tensorflow.keras.layers import Conv2D, MaxPooling2D, BatchNormalization


DATADIR = "E:\Final Year project\Virtual Robotic Arm controlled using Hand Gesture Reconginse & AI\Traning Data\Dataset\Train"
CATEGORIES = ["0","1","2","3","4","5","6","7","8","9","A","B","C","D","E","F","G","H","I","J","K","L","M","N","O","P","Q","R","S","T","U","V","W","X","Y","Z","a1","b1","c1","d1","e1","f1","g1","h1","i1","j1","k1","l1","m1","n1","o1","p1","q1","r1","s1","t1","u1","v1","w1","x1","y1","z1"]



for category in CATEGORIES:  #
    path = os.path.join(DATADIR,category)  # create path to dogs and cats
    for img in os.listdir(path):  # iterate over each image per dogs and cats
        img_array = cv2.imread(os.path.join(path,img) ,cv2.IMREAD_GRAYSCALE)  # convert to array
        plt.imshow(img_array, cmap='gray')  # graph it
        plt.show()  # display!

        break  # we just want one for now so break
    break  #...and one more!
    
img_array
print(img_array.shape)


# Image Resize
IMG_SIZE = 32
new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
plt.imshow(new_array, cmap='gray')
plt.show()       


from tqdm import *



training_data = []

def create_training_data():
    for category in CATEGORIES:  # do dogs and cats

        path = os.path.join(DATADIR,category)  # create path to dogs and cats
        class_num = CATEGORIES.index(category)  # get the classification  (0 or a 1). 0=dog 1=cat

        for img in tqdm(os.listdir(path)):  # iterate over each image per dogs and cats
            try:
                img_array = cv2.imread(os.path.join(path,img) ,cv2.IMREAD_GRAYSCALE)  # convert to array
                new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))  # resize to normalize data size
                training_data.append([new_array, class_num])  # add this to our training_data
            except Exception as e:  # in the interest in keeping the output clean...
                pass
            #except OSError as e:
            #    print("OSErrroBad img most likely", e, os.path.join(path,img))
            #except Exception as e:
            #    print("general exception", e, os.path.join(path,img))

create_training_data()

print(len(training_data))


random.shuffle(training_data)

from sklearn.model_selection import train_test_split
train_data , test_data = train_test_split(training_data,test_size=0.2,random_state=42)
train_data , val_data = train_test_split(train_data,test_size=0.2,random_state=42)

print(len(train_data))--->4012
print(len(test_data))--->1255
print(len(val_data))---> 1004

train_X = []
train_Y = []
for features,label in train_data:
    train_X.append(features)
    train_Y.append(label)

test_X = []
test_Y = []
for features,label in test_data:
    test_X.append(features)
    test_Y.append(label)

val_X = []
val_Y = []
for features,label in val_data:
    val_X.append(features)
    val_Y.append(label)

LB = LabelBinarizer()
train_Y = LB.fit_transform(train_Y)
test_Y = LB.fit_transform(test_Y)
val_Y = LB.fit_transform(val_Y)

train_X = np.array(train_X)/255.0
train_X = train_X.reshape(-1,32,32,1)
train_Y = np.array(train_Y)

test_X = np.array(test_X)/255.0
test_X = test_X.reshape(-1,32,32,1)
test_Y = np.array(test_Y)

val_X = np.array(val_X)/255.0
val_X = val_X.reshape(-1,32,32,1)
val_Y = np.array(val_Y)

print(train_X.shape,test_X.shape,val_X.shape)---> (4012, 32, 32, 1) (1255, 32, 32, 1) (1004, 
32, 32, 1)

print(train_Y.shape,test_Y.shape,val_Y.shape)---> (4012, 62) (1255, 62) (1004, 62)


model = Sequential()

model.add(Conv2D(32, (3, 3), padding = "same", activation='relu', input_shape=(32,32,1)))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
 
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(62, activation='softmax'))
model.summary()

model.compile(loss='categorical_crossentropy', optimizer="adam",metrics=['accuracy'])
history = model.fit(train_X,train_Y, epochs=25, batch_size=32, validation_data = (val_X, val_Y),  verbose=1)

import pickle
# Save the Modle to file in the current working directory

Filename = "Model.pkl"  

with open(Filename, 'wb') as file:  
    pickle.dump(model, file)

---

TypeError Traceback (most recent call last) ~\AppData\Local\Temp/ipykernel_13132/1527247688.py in 5 6 with open(Pkl_Filename, 'wb') as file: ----> 7 pickle.dump(model, file)

TypeError: can't pickle _thread.RLock objects

Answer 1

This code executed successfully when I tried replicating the same error in both Google Colab and Jupyter notebook . You can check the saved.pkl file in the left pane.

Please find the attached gist for your reference.(I have used binary dataset to reproduce the issue).

You can try again executing the same code and let us know if the issue still persists with the details of python and tensorflow version you are using.