Hi,

The code below shows an error. Can you please fix the error in the same code provided

Not Defined gives error F1Score and CohenKappa

# Define Constants

FAST_RUN = False

IMAGE_WIDTH=256 # 150 accept maybe 256

IMAGE_HEIGHT=256 # maybe 256

IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)

IMAGE_CHANNELS=3 # maybe not need

physical_devices = tf.config.experimental.list_physical_devices('GPU')

print(physical_devices)

if physical_devices:

tf.config.experimental.set_memory_growth(physical_devices[0], True)

# Prepare Traning Data

filenames = os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using _CNN\MixImages")

categories = []

for filename in filenames:

category = filename.split('l')[0]

if category == 'image_benign_':

categories.append(0)

else:

categories.append(1)

df = pd.DataFrame({

'filename': filenames,

'category': categories

})

print(df.head())

print(df.tail())

# in collab it will work

df['category'].value_counts().plot.bar()

# See sample image

# sample = random.choice(filenames)

# image = load_img("D:\Ransomware_Detection_using _CNN\MixImages"+sample)

# plt.imshow(image)

model = Sequential()

model.add(Conv2D(16, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNELS)))

model.add(BatchNormalization())

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Dropout(0.25))

model.add(Conv2D(32, (3, 3), activation='relu'))

model.add(BatchNormalization())

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Dropout(0.25))

model.add(Conv2D(64, (3, 3), activation='relu'))

model.add(BatchNormalization())

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Dropout(0.25))

model.add(Conv2D(128, (3, 3), activation='relu'))

model.add(BatchNormalization())

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Dropout(0.25))

model.add(Conv2D(256, (3, 3), activation='relu'))

model.add(BatchNormalization())

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Dropout(0.25))

model.add(Flatten())

model.add(Dense(512, activation='relu'))

model.add(BatchNormalization())

model.add(Dropout(0.5))

model.add(Dense(2, activation='softmax')) # 2 because we have cat and dog classes

model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy',

tf.keras.metrics.Precision(name='precision'),

tf.keras.metrics.Recall(name='recall'),

tf.keras.metrics.AUC(name='auc'),

tf.keras.metrics.CategoricalAccuracy(name='categorical_accuracy'),

tf.keras.metrics.FalsePositives(name='false_positives'),

tf.keras.metrics.FalseNegatives(name='false_negatives'),

tf.keras.metrics.TruePositives(name='true_positives'),

tf.keras.metrics.TrueNegatives(name='true_negatives')])

#########################################################

# Not Defined gives error F1Score and CohenKappa

tf.keras.metrics.F1Score(num_classes=2, name='f1_score'),

tf.keras.metrics.CohenKappa(name='cohen_kappa')])

##########################################################