Model_SimpleNet.py

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

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

# parameters for training
batch_size = 32
num_epochs = 20

# build the model
model = Sequential()

model.add(Conv2D(conv_size, (3, 3), activation='relu', padding='same', input_shape=input_shape)) 
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(conv_size, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
    
model.add(Conv2D(conv_size, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
    
model.add(Flatten())

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

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

# 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.