The course update to NLP with Sequence Models removed a week from the online course materials–I’d finished the first 3 weeks and was working on the 4th week materials when the 4th week got renamed Week 3 as seen in the topic. My question is about the assignment “Question Duplicates” in the Siamese Networks week.
In GRADED FUNCTION Siamese, the earlier defined TextVectorization is to be used as “text_vectorizer”.
I followed the in-function guidance to define the input1 and input2 that was stated as “Be mindful of the data type and size”: per the TF document tf.keras.Input | TensorFlow v2.14.0 the shape was set to the depth of the model and the type for the Input followed the instructions for tf.keras.layers.Input appearing just before the GRADED FUNCTION.
However, I don’t understand why the following error message is generated AND what to do to resolve the error. Setting the shape to various other “reasonable” values generated worse error messages. I feel like I want to use the tf.squeeze function somewhere in Siamese but no location tried resolved the error.
I did get the input1 into branch1 and input2 into branch2–is it not that simple to call branch() with the respective inputs? Is that the source of the error?
From the instructions of tf.keras.layers.Input which appear before the GRADED FUNCTION: “Remember to set correctly the dimension and type of the input, which are batches of questions. For this, keep in mind that each question is a single string.”
I mistakenly read the TF documentation to mean that the shape was the depth of the model…oh well
Hi. I’m encountering the same issue, and unfortunately I can’t seem to solve it (or find the solution here)… To be clear: what should be the input shape? shape=(d_feature, )? Something else? Thanks
Thank you @Ankoor_Bhagat , however this still doesn’t work for me and I can’t figure out why. I followed closely C3W3_Siamese_Network.ipynb, and I really need some help with this. Can anyone suggest a correction to the following?
# GRADED FUNCTION: Siamese
def Siamese(text_vectorizer, vocab_size=36224, d_feature=128):
"""Returns a Siamese model.
#mentor edit: code removed - not allowed by the Code of Conduct
keep in mind that each question is a single string.
shape=(1,)
Also, “dtype=tf.int64” should be “dtype=tf.string”. Otherwise, you will have a problem when you run Exercise 03.
Thank you so much @ejint !! Now it works like a charm… One more related note: in order to pass w3_unittest.test_Siamese(Siamese) I also had to change round brackets to square ones, unlike C3W3_Siamese_Network.ipynb. The correct syntax seems to be: conc = tf.keras.layers.Concatenate(axis=1, name=‘conc_1_2’)([branch1, branch2])
Hi, may I ask what was the correct input of the shape for tf.keras.layers.Input()? I put (1,) in mine it still doesn’t work and showed error as follow: File /usr/local/lib/python3.8/dist-packages/keras/src/engine/input_spec.py:235, in assert_input_compatibility(input_spec, inputs, layer_name)
233 ndim = shape.rank
234 if ndim != spec.ndim:
→ 235 raise ValueError(
236 f’Input {input_index} of layer “{layer_name}” ’
237 “is incompatible with the layer: "
238 f"expected ndim={spec.ndim}, found ndim={ndim}. "
239 f"Full shape received: {tuple(shape)}”
240 )
241 if spec.max_ndim is not None:
242 ndim = x.shape.rank
ValueError: Input 0 of layer “mean” is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 128)
Hi. In my code, following this thread, I wrote the this code - and it worked:
input1 = tf.keras.layers.Input(shape=(1, ), dtype=tf.string, name=‘input_1’)
I hope this helps.
Hi just sharing a quick comments all the above help me fixe my own bug , one other hint might be to double check you re using GlobalAveragePooling1D and not the one from previous lab earlier in the week.
