画像で異常検知をしようとしたのですがAttributeErrorが出て先に進めません。解決方法を教えてください。

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
/var/folders/vm/snb6b63s01b67s_d0m_qb5q40000gp/T/ipykernel_28079/2542085184.py in <module>
      1 # 画像の復元
----> 2 z_points = encoder.predict(original_images)
      3 reconst_images = decoder.predict(z_points)
      4 
      5 # 元画像との差分を計算

~/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
    907         max_queue_size=max_queue_size,
    908         workers=workers,
--> 909         use_multiprocessing=use_multiprocessing)
    910 
    911   def reset_metrics(self):

~/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_arrays.py in predict(self, model, x, batch_size, verbose, steps, callbacks, **kwargs)
    713     batch_size = model._validate_or_infer_batch_size(batch_size, steps, x)
    714     x, _, _ = model._standardize_user_data(
--> 715         x, check_steps=True, steps_name='steps', steps=steps)
    716     return predict_loop(
    717         model,

~/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split, shuffle, extract_tensors_from_dataset)
   2470           feed_input_shapes,
   2471           check_batch_axis=False,  # Don't enforce the batch size.
-> 2472           exception_prefix='input')
   2473 
   2474     # Get typespecs for the input data and sanitize it if necessary.

~/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    517   if shapes is not None:
    518     data = [
--> 519         standardize_single_array(x, shape) for (x, shape) in zip(data, shapes)
    520     ]
    521   else:

~/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_utils.py in <listcomp>(.0)
    517   if shapes is not None:
    518     data = [
--> 519         standardize_single_array(x, shape) for (x, shape) in zip(data, shapes)
    520     ]
    521   else:

~/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_utils.py in standardize_single_array(x, expected_shape)
    442         'Expected an array data type but received an integer: {}'.format(x))
    443 
--> 444   if (x.shape is not None and len(x.shape) == 1 and
    445       (expected_shape is None or len(expected_shape) != 1)):
    446     if tensor_util.is_tensor(x):

AttributeError: 'str' object has no attribute 'shape'

# 学習データの読み込み＆前処理
train_images = glob.glob("//*.png")
train = []
for i in train_images:
    image = cv2.imread(i)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    train.append(image)

train = np.array(train)
train = train.astype('float32') / 255

# 学習用ハイパーパラメータ
LEARNING_RATE = 0.0005
BATCH_SIZE = 8
Z_DIM = 50
EPOCHS = 3

# エンコーダ
encoder_input = Input(shape=(128,128,3), name='encoder_input')
x = encoder_input
x = Conv2D(filters=32, kernel_size=3, strides=1, padding='same', name='encoder_conv_0')(x)
x = LeakyReLU()(x)
x = Conv2D(filters=32, kernel_size=3, strides=1, padding='same', name='encoder_conv_0_1')(x)
x = LeakyReLU()(x)
x = Conv2D(filters=64, kernel_size=3, strides=2, padding='same', name='encoder_conv_1')(x)
x = LeakyReLU()(x)
x = Conv2D(filters=64, kernel_size=3, strides=2, padding='same', name='encoder_conv_2')(x)
x = LeakyReLU()(x)
x = Conv2D(filters=64, kernel_size=3, strides=1, padding='same', name='encoder_conv_3')(x)
x = LeakyReLU()(x)
shape_before_flattening = K.int_shape(x)[1:]
x = Flatten()(x)
encoder_output = Dense(Z_DIM, name='encoder_output')(x)
encoder = Model(encoder_input, encoder_output)

# デコーダ
decoder_input = Input(shape=(Z_DIM,), name='decoder_input')
x = Dense(np.prod(shape_before_flattening))(decoder_input)
x = Reshape(shape_before_flattening)(x)
x = Conv2DTranspose(filters=64, kernel_size=3, strides=1, padding='same', name='decoder_conv_t_0')(x)
x = LeakyReLU()(x)
x = Conv2DTranspose(filters=64, kernel_size=3, strides=2, padding='same', name='decoder_conv_t_1')(x)
x = LeakyReLU()(x)
x = Conv2DTranspose(filters=32, kernel_size=3, strides=2, padding='same', name='decoder_conv_t_2')(x)
x = LeakyReLU()(x)
x = Conv2DTranspose(filters=32, kernel_size=3, strides=1, padding='same', name='decoder_conv_t_2_5')(x)
x = LeakyReLU()(x)
x = Conv2DTranspose(filters=3, kernel_size=3, strides=1, padding='same', name='decoder_conv_t_3')(x)
x = Activation('sigmoid')(x)
decoder_output = x
decoder = Model(decoder_input, decoder_output)

# エンコーダ/デコーダ連結
model_input = encoder_input
model_output = decoder(encoder_output)
model = Model(model_input, model_output)

# 学習用設定設定（最適化関数、損失関数）
optimizer = Adam(learning_rate=LEARNING_RATE)

def r_loss(y_true, y_pred):
  return K.mean(K.square(y_true - y_pred), axis=[1,2,3])

model.compile(optimizer=optimizer, loss=r_loss)

# 学習実行
model.fit(
    train,
    train,
    batch_size=BATCH_SIZE,
    epochs=EPOCHS
)

#テスト用画像の読み込み
original_images = glob.glob("//*.png")
test = []
for n in original_images:
    img = cv2.imread(n)
    img= cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    test.append(img)

test = np.array(test)
test = test.astype('float32') / 255

# 画像の復元
z_points = encoder.predict(original_images)
reconst_images = decoder.predict(z_points)

# 元画像との差分を計算
diff_images = np.absolute(reconst_images - original_images)

original_images = glob.glob("//*.png")
test = []
for n in original_images:
    img = cv2.imread(n)
    img= cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    test.append(img)

test = np.array(test)
test = test.astype('float32') / 255