Issue
I have the training and testing numpy
arrays in the following shapes
TrainX = (1234, 50, 50) Type: <class 'numpy.ndarray'> # 1234 arrays of 50 by 50 floats
TrainY = (1234, 2) Type: <class 'numpy.ndarray'>
# TrainY was one column of binary class 0 or 1. Converted it through to_categorical()
TestX = (123, 50, 50) Type: <class 'numpy.ndarray'>
TestY = (123, 2) Type: <class 'numpy.ndarray'>
I use the following code for the LSTM,
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import LSTM, Dense, Dropout
model = Sequential()
model.add(LSTM(50, input_shape=(TrainX.shape[1], TrainX.shape[2])))
model.add(Dense(50))
model.add(Dropout(0.3))
model.add(Dense(2, activation="softmax"))
model.compile("adam", "categorical_crossentropy", metrics=["accuracy"])
model.fit(
TrainX,
TrainY,
batch_size=24,
epochs=48,
validation_data=[np.asarray(TestX).all(), np.asarray(TestY).all()],
class_weight=classweights,#calculated class weights
verbose=2,
)

First I used
TrainX
andTrainY
but got an errorValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

Based on this Keras LSTM input ValueError: Shapes are incompatible, I used
np.asarray(TrainX)
. But got the same error. 
So I added
np.asarray(TrainX).all()
one time andnp.asarray(TrainX).any()
another time. But got a different value error:ValueError: Failed to find data adapter that can handle input: <class 'numpy.bool_'>, <class 'numpy.bool_'>

Finally, I tried
pandas.DataFrame(TrainX)
for input. But it showed the following error,ValueError: Must pass 2d input. shape=(1234, 50, 50)
trace back of the errors 1 and 2:
Traceback (most recent call last):
File "C:\Users\...\main.py", line 73, in <module>
lmodel.lstm(TrainX, TestX, TrainY, TestY)
File "C:\Users\...\llmodel.py", line 65, in lstm
model.fit(
File "C:\Users\...\AppData\Roaming\Python\Python310\sitepackages\tensorflow\python\keras\engine\training.py", line 1137, in fit
data_handler = data_adapter.get_data_handler(
File "C:\Users\...\AppData\Roaming\Python\Python310\sitepackages\tensorflow\python\keras\engine\data_adapter.py", line 1394, in get_data_handler
return DataHandler(*args, **kwargs)
File "C:\Users\...\AppData\Roaming\Python\Python310\sitepackages\tensorflow\python\keras\engine\data_adapter.py", line 1169, in __init__
self._configure_dataset_and_inferred_steps(strategy, x, steps_per_epoch,
File "C:\Users\...\AppData\Roaming\Python\Python310\sitepackages\tensorflow\python\keras\engine\data_adapter.py", line 1177, in _configure_dataset_and_inferred_steps
if class_weight:
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
How can this issue be solved?
Solution
The log suggests that the problem is in your class_weight
argument. According to the docs, this argument should be
Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only). This can be useful to tell the model to "pay more attention" to samples from an underrepresented class.
which should be something like
class_weights = {0: 0.75, 1: 1.25}
if you want to assign a weight of 0.75 and 1.25 to class 0 and 1, respectively.
From the log, I suspect that instead of passing such a dictionary, you passed a numpy array of shape (n_samples, n_classes)
as class_weight
, which is inappropriate.
Answered By – bui
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