Multi input Functional API CNN model - keras

Question

I am making multi input cnn model using keras functional api but it
is giving error... data:
trainset1 = trainset.flow_from_directory(
'/content/',
target_size=(404,410),
batch_size=32,
#seed=50,
class_mode='categorical') print('In Training Set..Entropy....') trainset12 = trainset.flow_from_directory(
'/content/',
target_size=(404,410),
batch_size=32,
#seed=50,
class_mode='categorical')
model: input1 = Input(shape=(404,410,3)) input2 = Input(shape =
(404,410,3))
# x = layers.Dense(128, activation= 'relu')
x = layers.Conv2D(25, (5, 5), activation='relu',
padding='same')(input1) x = layers.MaxPool2D(pool_size=(2, 2),
padding='same')(x)
x1 = layers.Conv2D(25, (5, 5), activation='relu',
padding='same')(input2) x1 = layers.MaxPool2D(pool_size=(2, 2),
padding='same')(x1) flat_layer1 = Flatten()(x) flat_layer2 =
Flatten()(x1)
print(flat_layer1.shape)
print(flat_layer2.shape) concat_layer= Concatenate()([flat_layer1,flat_layer2])
concat_layer= concatenate([flat_layer1,flat_layer2])
x = layers.Dense(16, activation= 'relu')(flat_layer1) #(concat_layer) outputs = layers.Dense(2, activation='softmax')(concat_layer) model =
keras.Model(inputs=[input1,input2], outputs = outputs)
model.compile(
loss = keras.losses.BinaryCrossentropy(),
optimizer=keras.optimizers.Adam(learning_rate=0.001),
metrics=["accuracy"] )
model.fit([trainset1,trainset12] ,batch_size=32,epochs=5, verbose=2)
GIVING ERROR:
--------------------------------------------------------------------------- ValueError Traceback (most recent
call last) in ()
----> 1 model.fit([trainset1,trainset12] ,batch_size=32,epochs=5, verbose=2)
1 frames
/usr/local/lib/python3.7/dist-packages/keras/engine/data_adapter.py
in select_data_adapter(x, y)
989 "Failed to find data adapter that can handle "
990 "input: {}, {}".format(
--> 991 _type_name(x), _type_name(y)))
992 elif len(adapter_cls) > 1:
993 raise RuntimeError(
ValueError: Failed to find data adapter that can handle input:
(<class 'list'> containing values of types {"<class
'keras.preprocessing.image.DirectoryIterator'>"}), <class
'NoneType'"""
what should i do now?