I get all expected output and pass all prior tests but when I fit my model (running given cell in section 4.4) I get the following error:
ValueError: Can not squeeze dim[1], expected a dimension of 1, got 104 for ‘{{node Squeeze}} = SqueezeT=DT_FLOAT, squeeze_dims=[-1]’ with input shapes: [?,104].
104 is the lenght of each sentence in the training set.
The function that fails is:
model.fit(train_dataset.batch(BATCH_SIZE),
validation_data = val_dataset.batch(BATCH_SIZE),
shuffle=True,
epochs = 2)
I’m stuck and can’t figure out what’s wrong. I would really appreciate some help.
Thanks.
I supect the issue is in my masked_accury function. It passes all the tests but not the grader.
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Hi @Alexandre_Duriez
On the last line:
# Compute masked accuracy (quotient between the total matches and the total valid values, i.e., the amount of non-masked values)
we do not specify the axis parameter. In other words, we want to calculate all the accurately predicted labels (in our case by summing matches_true_pred which accounts for padded tokens) and divide by the number of all the elements (in our case by summing mask, which accounts for padded tokens). And both of these are scalars (not vectors).
Cheers
Hey what i don’t understand is that why does it pass the “w2_unittest.test_masked_accuracy(masked_accuracy)” and then causes an error when we try to fit the model.
Also I don’t get why specifying the axis=-1 causes the problems, i tried printing the shapes and type of the “total matches” and “total valid values”, and we can see that they are proper tensors, then why does this problem arises if we specify the axis=-1, i mean we can see that for these tensors axis=-1 is there (104).
