[FIXED] Tensorflow keras Input shape claimed as (None, None) while printed out as (20, 30)

Issue

I am trying to make a CNN-LTSM model with Keras, and I want to use keras.utils.Sequence to feed data into model (as the data is continuous, and I want every available window in the data be used for training).

But then Keras keeps complaining about the shape of my input data.

Error:

Epoch 1/100
WARNING:tensorflow:Model was constructed with shape (None, 20, 30) for input KerasTensor(type_spec=TensorSpec(shape=(None, 20, 30), dtype=tf.float32, name='input_7'), name='input_7', description="created by layer 'input_7'"), but it was called on an input with incompatible shape (None, None).

Cell 5 line 2
     15 dataSeq = TrainDataFeedSequence(train, INPUT_SIZE)
     17 # for i in range(0, len(dataSeq)):
     18 #     curInputShape = dataSeq.__getitem__(i)[0].shape
     19 #     if (curInputShape[0] == None or curInputShape[1] == None or curInputShape == (None, None)):
     20 #         print(dataSeq.__getitem__(i)[0])
---> 22 model.fit(x=dataSeq, epochs=100, verbose=1)

in filter_traceback.<locals>.error_handler(*args, **kwargs)
     67     filtered_tb = _process_traceback_frames(e.__traceback__)
     68     # To get the full stack trace, call:
     69     # `tf.debugging.disable_traceback_filtering()`
---> 70     raise e.with_traceback(filtered_tb) from None
     71 finally:
     72     del filtered_tb

in outer_factory.<locals>.inner_factory.<locals>.tf__train_function(iterator)
     13 try:
     14     do_return = True
---> 15     retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
     16 except:
     17     do_return = False

ValueError: Exception encountered when calling layer "sequential_6" "                 f"(type Sequential).
    
    Input 0 of layer "conv1d_3" is incompatible with the layer: expected min_ndim=3, found ndim=2. Full shape received: (None, None)

My Code for my sequence

class TrainDataFeedSequence(tf.keras.utils.Sequence):

    def __init__(self, data, batch_size):
        self.data = data
        self.batch_size = batch_size

    def __len__(self):
        return len(self.data) - self.batch_size

    def __getitem__(self, idx):
        inputs = self.data.iloc[idx:idx+self.batch_size,:]
        targets = self.data.iloc[idx+self.batch_size,:1]
        return (inputs, targets)

dataSeq = TrainDataFeedSequence(train, 20)

# this prints out nothing
# for i in range(0, len(dataSeq)):
#     curInputShape = dataSeq.__getitem__(i)[0].shape
#     if (curInputShape[0] == None or curInputShape[1] == None or curInputShape == (None, None)):
#         print(dataSeq.__getitem__(i)[0])

model.fit(x=dataSeq, epochs=100)

I'm quite sure that every item in my Sequence are not in shape (None, None), so what goes wrong?

keras == 2.10.0,tensorflow == 2.10.0, python == 3.9.18

I tried to print out all the shape of input items as well and all of them are in shape (20, 30).

Solution

To whoever wants to know, it's because of the TrainDataFeedSequence doesn't return input and targets as np.Array

making two np.Array with inputs and targets and return them solved the error

working version:

class TrainDataFeedSequence(tf.keras.utils.Sequence):

    def __init__(self, data, batch_size):
        self.data = data
        self.batch_size = batch_size

    def __len__(self):
        return len(self.data) - self.batch_size

    def __getitem__(self, idx):
        inputs = self.data.iloc[idx:idx+self.batch_size,:]
        targets = self.data.iloc[idx+self.batch_size,:1]
        return np.array(inputs), np.array(targets)

dataSeq = TrainDataFeedSequence(train, 20)

# this prints out nothing
# for i in range(0, len(dataSeq)):
#     curInputShape = dataSeq.__getitem__(i)[0].shape
#     if (curInputShape[0] == None or curInputShape[1] == None or curInputShape == (None, None)):
#         print(dataSeq.__getitem__(i)[0])

model.fit(x=dataSeq, epochs=100)

Worth mentioning that this exact hand-made Sequence is responsible for screwing up my training process, so better don't use it directly, this should help

Answered By - laezZ_boi