Develop Reference

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

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

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.

Extending an existing python macro from predicting 2 categories to 3 categories

I need some help to get an existing python example which works nicely to classify 2 categories and extend it to classify 3 categories.
here's the working example:
import numpy as np
import matplotlib.pyplot as plt
import os
import cv2 as cv
from tqdm import tqdm
import random
import tensorflow as tf
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
DATADIR = "C:/D/tmp/CNN"
CATEGORIES = ["Cat1","Cat2"]
IMG_SIZE = 100
training_data = []
def create_training_data():
for category in CATEGORIES:
path = os.path.join(DATADIR,category)
class_num = CATEGORIES.index(category)
for img in tqdm(os.listdir(path)):
try:
img_array = cv.imread(os.path.join(path,img) ,cv.IMREAD_GRAYSCALE) # convert to array
new_array = cv.resize(img_array, (IMG_SIZE, IMG_SIZE)) # resize to normalize data size
training_data.append([new_array, class_num])
except Exception as e:
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)
X = []
y = []
for features,label in training_data:
X.append(features)
y.append(label)
#print(X[0].reshape(-1, IMG_SIZE, IMG_SIZE, 1))
X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
y = np.array(y)
X = X/255.0
model = Sequential()
model.add(Conv2D(256, (3, 3), input_shape=X.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(256, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(64))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(X, y, batch_size=16, epochs=20, validation_split=0.3)
model.save('cat1_2.model')
My idea was to, apart from creating the 3rd folder with the images of the 3rd category to:
1- change to CATEGORIES = ["Cat1","Cat2","Cat3"]
2- change model.add(Dense(1)) to model.add(Dense(3))
3- change model.add(Activation('sigmoid')) to model.add(Activation('softmax'))
4- change from loss='binary_crossentropy' to loss='sparse_categorical_crossentropy'
Unfortunately that, although the model seems to converge to a very good accuracy (above .95) it always predict
[[0. 0. 1.]]
regardless the input.
What am I missing?

solved....rookie mistake....the error was actually in the prediction calling. basically I was passing the image to the model without scaling 0<->1 (i.e. diving by 255)..Once I did that predictions started to make sense...

How I can fix the "value error" for my CNN

Firstly, my level in English is bad, so sorry for that.
So when I start the training of my CNN, it return this error :
ValueError: validation_split is only supported for Tensors or NumPy arrays, found following types in the input: [<class 'int'>, <class 'int'>, <class 'int'>, .... ]
I'm beginner at CNN. I don't know where is the error, so I put my entire code here (written by sentdex YouTube channel) :
create my training data
import numpy as np
import os
import cv2
from tqdm import tqdm
import pickle
import random
DATADIR = "C:/content/datasets/Cats and dogs 2"
CATEGORIES = ["Dog", "Cat"]
IMG_SIZE = 100
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)) # 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(training_data)
random.shuffle(training_data)
X = []
y = []
for features,label in training_data:
X.append(features)
y.append(label)
X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
print(y)
pickle_out = open("X.pickle","wb")
pickle.dump(X, pickle_out)
pickle_out.close()
pickle_out = open("y.pickle","wb")
pickle.dump(y, pickle_out)
pickle_out.close()
Build and train the neural network
import tensorflow as tf
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
import pickle
pickle_in = open("X.pickle","rb")
X = pickle.load(pickle_in)
pickle_in = open("y.pickle","rb")
y = pickle.load(pickle_in)
X = X/255.0
model = Sequential()
model.add(Conv2D(256, (3, 3), input_shape=X.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(256, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(64))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(X, y, batch_size=32, epochs=3, validation_split=0.1)
Perhaps the "reshape at the array X" is strange, pycharm tell me that the two last arguments are unexpected.
Please tell me if you see improvement going for my code,
Thanks for your help

ValueError: validation_split is only supported for Tensors or NumPy
arrays, found following types in the input: [<class 'int'>, <class
'int'>, <class 'int'>, .... ]
for features,label in training_data:
X.append(features)
y.append(label)
X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
Combine your code with the error, y is a list in your code, try to convert it to numpy arrays.

confusion matrix using matplotlib

So I have been having trouble plotting a confusion matrix on my data. I am making an image classifier on pycharm with python 3.6. Below is my code, can anyone tell me if my code is working correctly? because I can see a confusion matrix on the outcome but I am not sure if what I did is correct.
import tensorflow as tf
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
import numpy as np
import matplotlib.pyplot as plt
import os
import cv2
DATADIR = "C:/Users/Acer/imagerec/MRI"
CATEGORIES = ["yes", "no"]
for category in CATEGORIES:
path = os.path.join(DATADIR,category)
for img in os.listdir(path):
img_array = cv2.imread(os.path.join(path,img) ,cv2.IMREAD_GRAYSCALE)
plt.imshow(img_array, cmap='gray')
plt.show()
break
break
print(img_array)
print(img_array.shape)
IMG_SIZE = 50
new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
plt.imshow(new_array, cmap='gray')
plt.show()
training_data = []
def create_training_data():
for category in CATEGORIES:
path = os.path.join(DATADIR, category)
class_num = CATEGORIES.index(category)
for img in os.listdir(path):
try:
img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)
new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
training_data.append([new_array, class_num])
except Exception as e:
pass
create_training_data()
print(len(training_data))
import random
random.shuffle(training_data)
for sample in training_data[:10]:
print(sample[1])
X = []
y = []
for features, label in training_data:
X.append(features)
y.append(label)
X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
import pickle
pickle_in = open("X.pickle","rb")
X = pickle.load(pickle_in)
pickle_in = open("y.pickle","rb")
y = pickle.load(pickle_in)
X = X/255.0
model = Sequential()
model.add(Conv2D(256, (3, 3), input_shape=X.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(256, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(X, y, batch_size=5, epochs=20, validation_split=0.1)
from sklearn.metrics import confusion_matrix
pred = model.predict(X)
pred = np.round(pred)
conf = confusion_matrix(y, pred)
import seaborn as sns
sns.heatmap(conf, annot=True)
import matplotlib.pyplot as plt
plt.show()
model.save('64x2-CNN.model')

Store images into multiply array and use it to train model

I get this error when train the model:
ValueError: Error when checking target:
expected dropout_5 to have shape (33,) but got array with shape (1,).
I want to store my images into 33 array from folder using path. I have categories the images into different folder which were 1,2,3,4,5...
I have use this code to do it but i dont know how to store it into different array. Can someone help me.
datadir = 'C:/Users/user/Desktop/RESIZE' #path of the folder
categories = ['1','2','3','4','5','6','7','8','9','A','B','C','D','E','F','G','H','I','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y']
img_rows, img_cols = 100, 100
training_data = []
for category in categories:
path = os.path.join(datadir,category)
class_num = categories.index(category)
for img in os.listdir(path):
img_array = cv2.imread(os.path.join(path,img),cv2.IMREAD_GRAYSCALE)
new_array = cv2.resize(img_array,(img_rows,img_cols))
training_data.append([new_array,class_num])
random.shuffle(training_data)
X = []
y = []
for features, label in training_data:
X.append(features)
y.append(label)
X = np.array(X).reshape(-1,img_rows,img_cols,1)
X = X.astype("float32")
pickle_out = open("X.pickle","wb")
pickle.dump(X,pickle_out)
pickle_out.close()
pickle_out = open("y.pickle","wb")
pickle.dump(y,pickle_out)
pickle_out.close()
After I save the file, then I use this code to train model and I want get 33 output layer but it only can work when my output layer(Dense) set 1.
I got this error:
ValueError: Error when checking target:
expected dropout_5 to have shape (33,) but got array with shape (1,)
Here was my training code.
import tensorflow as tf
from keras import optimizers
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers import Activation
import cv2
import os
import numpy as np
import pickle
from sklearn.utils import shuffle
X = pickle.load(open("X.pickle","rb"))
y = pickle.load(open("y.pickle","rb"))
X = X/255.0
model = Sequential()
model.add(Conv2D(32,(3,3), input_shape = X.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Dropout(0.25))
model.add(Conv2D(64,(3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Dropout(0.25))
model.add(Conv2D(128,(3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Dropout(0.4))
model.add(Dense(128))
model.add(Activation("relu"))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(33, activation='softmax'))
model.add(Dropout(0.4))
model.compile(loss = "binary_crossentropy", optimizer = "adam", metrics = ["accuracy"])
model.fit(X, y, batch_size = 2, epochs = 1, validation_split = 0.2)

You need to change your y as one hot encoded data to do the training.
Try this for y,
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
# integer encode
label_encoder = LabelEncoder()
integer_encoded = label_encoder.fit_transform(y)
print(label_encoder.classes_) # This is your classes.
print(integer_encoded.shape())
# binary encode
onehot_encoder = OneHotEncoder(sparse=False)
integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)
onehot_encoded = onehot_encoder.fit_transform(integer_encoded)
print(onehot_encoded.shape())
And one more thing, if you want to classify for 33 class than change your loss to categorical_crossentropy.