Not able to use the model.fit() in Keras after much work

Question

I have been trying to develop a CNN Model to dictect and predict the Flower categories. I got stuck at the model.fit(). Here is my code. The datasets I used are

https://drive.google.com/drive/folders/1-QOrDBpVvXWb_zAsaxZnalmvUQRA7yOb?usp=sharing

Here is my code;

    '''
model = Sequential()

model.add(Conv2D(32, (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(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(Conv2D(512, (3,3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))

# 1st Hidden Layer
model.add(Dense(500, activation='sigmoid'))
model.add(BatchNormalization())
model.add(Dropout(0.25))

# 2nd Hidden Layer
model.add(Dense(100, activation='softmax'))
model.add(BatchNormalization())
model.add(Dropout(0.25))

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

model.summary()

from keras.callbacks import EarlyStopping, ReduceLROnPlateau
earlystop = EarlyStopping(patience = 10)
learning_rate_reduction = ReduceLROnPlateau(monitor = 'val_acc',patience = 2,verbose = 1,factor = 0.5,min_lr = 0.00001)
callbacks = [earlystop,learning_rate_reduction]

    df_full_flower_dataset['category'] = df_full_flower_dataset['category'].replace({1:'daisy',2:'dandelion',3:'rose',4:'sunflower',5:'tulip'})
    train_df,validate_df = train_test_split(df_full_flower_dataset,test_size=0.20,random_state=42)
    train_df = train_df.reset_index(drop=True)
    validate_df = validate_df.reset_index(drop=True)
    total_train=train_df.shape[0]
    total_validate=validate_df.shape[0]
    batch_size=15
    
    train_datagen = ImageDataGenerator(rotation_range=15,
                                    rescale=1./255,
                                    shear_range=0.1,
                                    zoom_range=0.2,
                                    horizontal_flip=True,
                                    width_shift_range=0.1,
                                    height_shift_range=0.1
                                    )
    
    train_generator = train_datagen.flow_from_dataframe(train_df,
                                                     x_col='filename',y_col='category',
                                                     target_size=Image_Size,
                                                     class_mode='categorical',
                                                     batch_size=batch_size)
    
    validation_datagen = ImageDataGenerator(rescale=1./255)
    validation_generator = validation_datagen.flow_from_dataframe(
        validate_df,  
        x_col='filename',
        y_col='category',
        target_size=Image_Size,
        class_mode='categorical',
        batch_size=batch_size
    )
    
    test_datagen = ImageDataGenerator(rotation_range=15,
                                    rescale=1./255,
                                    shear_range=0.1,
                                    zoom_range=0.2,
                                    horizontal_flip=True,
                                    width_shift_range=0.1,
                                    height_shift_range=0.1)
    
    test_generator = train_datagen.flow_from_dataframe(train_df,
                                                     x_col='filename',y_col='category',
                                                     target_size=Image_Size,
                                                     class_mode='categorical',
                                                     batch_size=batch_size)
    
    model.fit(
            train_generator,
            steps_per_epoch = 10,
            epochs = 2,
            validation_data = test_generator,verbose = 1,
            validation_steps = 32)
    
    Epoch 1/2
    ---------------------------------------------------------------------------
    InvalidArgumentError                      Traceback (most recent call last)
    <ipython-input-145-f3e92aad38c9> in <module>()
          8         steps_per_epoch = 10,
          9         epochs = 2,
    ---> 10         validation_data = test_generator)
    
    1 frames
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
         57     ctx.ensure_initialized()
         58     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
    ---> 59                                         inputs, attrs, num_outputs)
         60   except core._NotOkStatusException as e:
         61     if name is not None:
    
    InvalidArgumentError:  required broadcastable shapes
    
    '''

This is the error I got... I am new to programming with python and deep learning. But it seems this model.fit() has me stuck. How can I solve this issue?