InvalidArgumentError: Data t InvalidArgumentError: Data type mismatch at component 0: expected int32 but got int64 - Tensorflow
I am trying to train a model with Tensorflow following a course and I get the mentioned error.
here is the relevant part of my code:
element_spec = ({'input_ids': tf.TensorSpec(shape=(16, 512), dtype=tf.int32, name=None),
'attention_masks': tf.TensorSpec(shape=(16, 512), dtype=tf.int32, name=None)},
tf.TensorSpec(shape=(16, 5), dtype=tf.float64, name=None))
train_ds = tf.data.experimental.load('train', element_spec)
val_ds = tf.data.experimental.load('val', element_spec)
#in order to keep the history of our runs
history = model.fit(
train_ds,
validation_data = val_ds,
epochs = 3
)
I tried running it on my laptop(no specific GPUs) and it is taking forever, so I decided to run the same thing on google colab but I get this error which I find odd:
Epoch 1/3
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-146-9b8dacb137ae> in <module>()
3 train_ds,
4 validation_data = val_ds,
----> 5 epochs = 3
6 )
1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
57 ctx.ensure_initialized()
58 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 59 inputs, attrs, num_outputs)
60 except core._NotOkStatusException as e:
61 if name is not None:
InvalidArgumentError: Data type mismatch at component 0: expected int32 but got int64.
[[node IteratorGetNext
(defined at /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:866)
]] [Op:__inference_train_function_16829]
Errors may have originated from an input operation.
Input Source operations connected to node IteratorGetNext:
In[0] iterator (defined at /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1216)
is the problem just somehow changing epoch = 3 from int64 to int32?
Solution 1:
Ok, I figured it out. I had defined dtype=tf.int32
in element spec. After changing it to dtype=tf.int64
it is working now.