Tensorflow fit method with generator error. AttributeError: 'tuple' object has no attribute 'shape'

user_14121166 0
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I'm trying to get a basic segmentation model going before making major tweaks and no matter how simple I make it I receive this error. I'm working on Collaboratory

Found 500 images belonging to 1 classes.
Found 500 images belonging to 1 classes.
Found 50 images belonging to 1 classes.
Found 50 images belonging to 1 classes.
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-23-420c271bfe7a> in <module>()
      3                           steps_per_epoch = (32),
      4                           validation_data=val_generator(),
----> 5                           callbacks=callbacks_list)

10 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    971           except Exception as e:  # pylint:disable=broad-except
    972             if hasattr(e, "ag_error_metadata"):
--> 973               raise e.ag_error_metadata.to_exception(e)
    974             else:
    975               raise

AttributeError: in user code:

    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function  *
        return step_function(self, iterator)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step  **
        outputs = model.train_step(data)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:759 train_step
        self.compiled_metrics.update_state(y, y_pred, sample_weight)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:388 update_state
        self.build(y_pred, y_true)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:319 build
        self._metrics, y_true, y_pred)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/nest.py:1139 map_structure_up_to
        **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/nest.py:1235 map_structure_with_tuple_paths_up_to
        *flat_value_lists)]
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/nest.py:1234 <listcomp>
        results = [func(*args, **kwargs) for args in zip(flat_path_list,
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/nest.py:1137 <lambda>
        lambda _, *values: func(*values),  # Discards the path arg.
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:419 _get_metric_objects
        return [self._get_metric_object(m, y_t, y_p) for m in metrics]
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:419 <listcomp>
        return [self._get_metric_object(m, y_t, y_p) for m in metrics]
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:440 _get_metric_object
        y_t_rank = len(y_t.shape.as_list())

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

I think it's related to the generator based off what I found online but I can't pinpoint what exactly it is. It could be that I've also improperly compiled the model for segmentation? (I'm a novice with this type of model)

Here is my model

Model: "functional_4"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_4 (InputLayer)         [(None, 1024, 1024, 3)]   0         
_________________________________________________________________
blockx_conv1 (Conv2D)        (None, 1024, 1024, 64)    1792      
_________________________________________________________________
blockx_conv2 (Conv2D)        (None, 1024, 1024, 64)    36928     
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 512, 512, 64)      0         
_________________________________________________________________
blocky_conv1 (Conv2D)        (None, 512, 512, 128)     73856     
_________________________________________________________________
blocky_conv2 (Conv2D)        (None, 512, 512, 256)     295168    
_________________________________________________________________
max_pooling2d_7 (MaxPooling2 (None, 256, 256, 256)     0         
_________________________________________________________________
blockxy_conv1 (Conv2D)       (None, 256, 256, 512)     1180160   
_________________________________________________________________
dropout_3 (Dropout)          (None, 256, 256, 512)     0         
_________________________________________________________________
blockxy_conv2 (Conv2D)       (None, 256, 256, 1024)    25691136  
_________________________________________________________________
max_pooling2d_8 (MaxPooling2 (None, 128, 128, 1024)    0         
_________________________________________________________________
blockxy_conv3 (Conv2D)       (None, 128, 128, 1024)    1049600   
_________________________________________________________________
blockxy_conv4 (Conv2D)       (None, 128, 128, 3)       3075      
_________________________________________________________________
up_sampling2d_3 (UpSampling2 (None, 1024, 1024, 3)     0         
=================================================================
Total params: 28,331,715
Trainable params: 28,331,715
Non-trainable params: 0

My compile as follows. I think this could also be a potential source of error as I still am not sure what optimizers and loss functions I should be using.

model.compile(optimizer='adam', loss='categorical_crossentropy',metrics=['acc','loss','val_loss','val_acc'])

Here is my fit method. I kept it simple to try to troubleshoot

results = model.fit(train_generator(), epochs=1, 
                          steps_per_epoch = (32),
                          validation_data=val_generator(),                          
                          callbacks=callbacks_list)

here's the generator I've used just in case

def train_generator(batch=16):
  from tensorflow.keras.preprocessing.image import ImageDataGenerator
  train_datagen = ImageDataGenerator(
          rescale=1./255)

  train_image_generator = train_datagen.flow_from_directory(
  '/content/drive/My Drive/Thesis Pics/train_frames/',
  batch_size = batch,
  target_size=(1024,768))

  train_mask_generator = train_datagen.flow_from_directory(
  '/content/drive/My Drive/Thesis Pics/train_masks/',
  batch_size = batch,
  target_size=(1024,768))

  
  train_generator = zip(train_image_generator, train_mask_generator)

  return train_generator

Since I'm new to segmentation I'm not quite sure of the nuances I need to be aware of that may be different from classification. Is there something obvious I have missed?

keras tensorflow python
4 Answers

I am guessing now, but .fit() expects data, a tf.data.Dataset structure or a data_generator (which I am not great familiar with). However, you are passing a tuple as you return zip(train_image_generator, train_mask_generator), which is no format .fit() can use for training

user_138044430

The first parameter of model.fit is bellow

Arguments:
        x: Input data. It could be:
          - A Numpy array (or array-like), or a list of arrays
            (in case the model has multiple inputs).
          - A TensorFlow tensor, or a list of tensors
            (in case the model has multiple inputs).
          - A dict mapping input names to the corresponding array/tensors,
            if the model has named inputs.
          - A `tf.data` dataset. Should return a tuple
            of either `(inputs, targets)` or
            `(inputs, targets, sample_weights)`.
          - A generator or `keras.utils.Sequence` returning `(inputs, targets)`
            or `(inputs, targets, sample weights)`.
          A more detailed description of unpacking behavior for iterator types
          (Dataset, generator, Sequence) is given below.

But you are passing a tuple with zip function

user_47945550

Despite everywhere saying you can use a zip method for a generator. I instead created a zip of datasets and then used THAT to solve our problem. So I fed a single dataset that was itself two datasets.

IMAGE_SIZE = (256,256)
def train_generator():
  frames = tf.keras.preprocessing.image_dataset_from_directory('/content/drive/My Drive/Thesis Pics/train_frames',label_mode=None,image_size=IMAGE_SIZE,batch_size=4)
  masks = tf.keras.preprocessing.image_dataset_from_directory('/content/drive/My Drive/Thesis Pics/train_masks',label_mode=None,image_size=IMAGE_SIZE,batch_size=4)
  train = tf.data.Dataset.zip((frames,masks))
  return train

def val_generator():
  frames = tf.keras.preprocessing.image_dataset_from_directory('/content/drive/My Drive/Thesis Pics/val_frames',label_mode=None,image_size=IMAGE_SIZE,batch_size=4)
  masks = tf.keras.preprocessing.image_dataset_from_directory('/content/drive/My Drive/Thesis Pics/val_masks',label_mode=None,image_size=IMAGE_SIZE,batch_size=4)
  val = tf.data.Dataset.zip((frames,masks))
  return val

train_gen = train_generator()
val_gen = val_generator()
user_141211660

What solved the problem for me was to change:

metrics=["acc"]

to:

metrics=["binary_accuracy"]

You could try to remove the metrics and see if it works and then incorporate the metrics one by one, with full name.

Sorry, I can't give a more comprehensive explanation why that is. It seems to me to be a bug that still persists.

https://github.com/keras-team/autokeras/issues/1095

user_68779360
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