Check failed: (void*)cp <= (void*)ptr (0x7fffffff7fffffff vs. 0x12dfb6fc0)

[ShwetaTiwariRepo]

i am receiving multiple error with Different architecture of deep learning model, most of the time segmentation fault, tried changing my code to use a simple multilayer architecture to train a model of mnist dataset , got the error as F tensorflow/core/common_runtime/pool_allocator.cc:138] Check failed: (void)cp <= (void)ptr (0x7fffffff7fffffff vs. 0x12dfb6fc0).

below is my code to replicate the issue.

from tensorflow.keras.datasets import mnist import numpy as np from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Dropout from tensorflow.keras.optimizers import SGD, Adam from tensorflow.keras.preprocessing.image import ImageDataGenerator from sklearn.preprocessing import LabelBinarizer from sklearn.model_selection import train_test_split

((trainData, trainLabels), (testData, testLabels)) = mnist.load_data() data = np.vstack([trainData, testData]) labels = np.hstack([trainLabels, testLabels])

data = np.array(data, dtype="float32")

data = np.expand_dims(data, axis=-1) data /= 255.0

le = LabelBinarizer() labels = le.fit_transform(labels) counts = labels.sum(axis=0)

classTotals = labels.sum(axis=0) classWeight = {} for i in range(0, len(classTotals)): classWeight[i] = classTotals.max() / classTotals[i]

(trainX, testX, trainY, testY) = train_test_split(data, labels, train_size=0.80,stratify=labels, random_state=0)

aug = ImageDataGenerator( rotation_range=10, zoom_range=0.05, width_shift_range=0.1, height_shift_range=0.1, shear_range=0.15, horizontal_flip=False, fill_mode="nearest")

model=Sequential()

model.add(Conv2D(filters=32, kernel_size=(3,3),activation='relu', input_shape=(28,28,1))) model.add(MaxPool2D(pool_size=(2,2),strides=2)) model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding='same')) model.add(MaxPool2D(pool_size=(2,2),strides=2)) model.add(Conv2D(filters=128,kernel_size=(3,3), activation='relu',padding='valid')) model.add(MaxPool2D(pool_size=(2,2),strides=2))

model.add(Flatten())

model.add(Dense(64,activation='relu')) model.add(Dense(128,activation='relu'))

model.add(Dense(10,activation='softmax'))

EPOCHS=10

opt = SGD(lr=0.01, decay=0.01 / EPOCHS)

model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])

H = model.fit( aug.flow(trainX, trainY, batch_size=16), validation_data=(testX, testY), steps_per_epoch=len(trainX) // 16, epochs=EPOCHS, class_weight=classWeight, verbose=1)

[jmgaudet]

I think it has something to do with classWeight, because if you comment-out that parameter, it'll work.

[ShwetaTiwariRepo]

thanks, it works after commenting it, but why that should be an issue, the same code works on x86_64 environment.