Model_InceptionV3.py

from keras.applications.inception_v3 import InceptionV3
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D

# parameters for architecture
input_shape = (224, 224, 3)
num_classes = 6
conv_size = 32

# parameters for training
batch_size = 32
num_epochs = 20

# load InceptionV3 from Keras
InceptionV3_model = InceptionV3(include_top=False, input_shape=input_shape)

# add custom Layers
x = InceptionV3_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(512, activation="relu")(x)
Custom_Output = Dense(num_classes, activation='softmax')(x)

# define the input and output of the model
model = Model(inputs = InceptionV3_model.input, outputs = Custom_Output)
        
# compile the model
model.compile(loss='categorical_crossentropy',
                    optimizer='adam',
                    metrics=['accuracy'])

model.summary()

# train the model 
history = model.fit(x_train, y_train,
                    batch_size=batch_size,
                    epochs=num_epochs,
                    verbose=1,
                    validation_split=0.1)

x_train was not declared.
So there is an error.