Unimplemented Error Graph execution error in cnn model - conv-neural-network

Question

What's the problem with this?
I'm confuse right now I need to train my model and I read the documentation somehow but I'm getting this kind of error. At first the model is success and I save it but the accuracy is too low, so I reconstruct my model and get this. I just change the imgimg_width, img_height
classification_no = 6
# Train and Test dataset
train_dataset_path = "Datasets/train/"
test_dataset_path = "Datasets/test/"
img_width, img_height =100,100
size = 20
def detection_model():
detection_model = Sequential(
[
# module 1
tf.keras.layers.Conv2D(32, kernel_size=(3,3),input_shape = (img_width,img_height, 1)),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Conv2D(64,kernel_size= (3,3),padding='same'),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.Activation('relu'),
tf.keras.layers.MaxPooling2D(pool_size=(2,2)),
tf.keras.layers.Dropout(0.25),
tf.keras.layers.Flatten(),
# Dense
tf.keras.layers.Dense(64),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.Activation('relu'),
# Dropout
tf.keras.layers.Dropout(0.5),
# Dense
tf.keras.layers.Dense(64),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.Activation('relu'),
#output
tf.keras.layers.Dense(6, activation= 'softmax'),
]
)
detection_model.compile(loss='categorical_crossentropy',
optimizer=Adam(
learning_rate = 0.0001 ,
beta_1=0.9,
beta_2=0.999,
decay=1e-6),
metrics=['accuracy'])
detection_model.summary()
return detection_model
train = ImageDataGenerator(rescale= 1./255)
validation = ImageDataGenerator(rescale= 1./255)
def get_model_train():
data_train = train.flow_from_directory(
train_dataset_path,
target_size= (img_width,img_height),
batch_size= 16,
color_mode= 'rgba',
class_mode= 'categorical')
return data_train
def get_model_test():
data_test = validation.flow_from_directory(
test_dataset_path,
target_size= (img_width,img_height),
batch_size= 16,
color_mode= 'rgba',
class_mode= 'categorical')
return data_test
model = detection_model()
fit_model = model.fit(
get_model_train(),
steps_per_epoch= 2006//16 ,
epochs= size,
validation_data= get_model_test(),
validation_steps= 2006//16,
)