C3W4-siamese model training

[Miranda_Lv]

I tried to work on the Siamese network in Week4’s assignment, after creating the model, loss function, I received below errors for the train_model function.

My code passed the testcode of w3_unittest.test_train_model(train_model, Siamese, TripletLoss), but if I run the train_model function, it got below errors. Not sure what was the issue…

Epoch 1/2

UnimplementedError Traceback (most recent call last)
Cell In[36], line 9
3 train_generator = train_dataset.shuffle(len(train_Q1),
4 seed=7,
5 reshuffle_each_iteration=True).batch(batch_size=batch_size)
6 val_generator = val_dataset.shuffle(len(val_Q1),
7 seed=7,
8 reshuffle_each_iteration=True).batch(batch_size=batch_size)
----> 9 model = train_model(Siamese, TripletLoss,text_vectorization,
10 train_generator,
11 val_generator,
12 train_steps=train_steps,)

Cell In[35], line 29, in train_model(Siamese, TripletLoss, text_vectorizer, train_dataset, val_dataset, d_feature, lr, train_steps)
25 model.compile(loss=TripletLoss,
26 optimizer = tf.keras.optimizers.Adam(lr)
27 )
28 # Train the model
—> 29 model.fit(train_dataset,
30 epochs = train_steps,
31 validation_data = val_dataset,
32 )
34 ### END CODE HERE ###
36 return model

File /usr/local/lib/python3.8/dist-packages/keras/src/utils/traceback_utils.py:70, in filter_traceback..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

File /usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py:53, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
51 try:
52 ctx.ensure_initialized()
—> 53 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
54 inputs, attrs, num_outputs)
55 except core._NotOkStatusException as e:
56 if name is not None:

UnimplementedError: Graph execution error:

Detected at node ‘SiameseModel/Cast’ defined at (most recent call last):
File “/usr/lib/python3.8/runpy.py”, line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File “/usr/lib/python3.8/runpy.py”, line 87, in _run_code
exec(code, run_globals)
File “/usr/local/lib/python3.8/dist-packages/ipykernel_launcher.py”, line 17, in
app.launch_new_instance()
File “/usr/local/lib/python3.8/dist-packages/traitlets/config/application.py”, line 1043, in launch_instance
app.start()
File “/usr/local/lib/python3.8/dist-packages/ipykernel/kernelapp.py”, line 728, in start
self.io_loop.start()
File “/usr/local/lib/python3.8/dist-packages/tornado/platform/asyncio.py”, line 195, in start
self.asyncio_loop.run_forever()
File “/usr/lib/python3.8/asyncio/base_events.py”, line 570, in run_forever
self._run_once()
File “/usr/lib/python3.8/asyncio/base_events.py”, line 1859, in _run_once
handle._run()
File “/usr/lib/python3.8/asyncio/events.py”, line 81, in _run
self._context.run(self._callback, *self._args)
File “/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py”, line 516, in dispatch_queue
await self.process_one()
File “/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py”, line 505, in process_one
await dispatch(*args)
File “/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py”, line 412, in dispatch_shell
await result
File “/usr/local/lib/python3.8/dist-packages/ipykernel/kernelbase.py”, line 740, in execute_request
reply_content = await reply_content
File “/usr/local/lib/python3.8/dist-packages/ipykernel/ipkernel.py”, line 422, in do_execute
res = shell.run_cell(
File “/usr/local/lib/python3.8/dist-packages/ipykernel/zmqshell.py”, line 540, in run_cell
return super().run_cell(*args, **kwargs)
File “/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py”, line 3009, in run_cell
result = self._run_cell(
File “/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py”, line 3064, in _run_cell
result = runner(coro)
File “/usr/local/lib/python3.8/dist-packages/IPython/core/async_helpers.py”, line 129, in pseudo_sync_runner
coro.send(None)
File “/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py”, line 3269, in run_cell_async
has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
File “/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py”, line 3448, in run_ast_nodes
if await self.run_code(code, result, async=asy):
File “/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py”, line 3508, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File “/tmp/ipykernel_3072/1427930616.py”, line 9, in
model = train_model(Siamese, TripletLoss,text_vectorization,
File “/tmp/ipykernel_3072/3611102567.py”, line 29, in train_model
model.fit(train_dataset,
File “/usr/local/lib/python3.8/dist-packages/keras/src/utils/traceback_utils.py”, line 65, in error_handler
return fn(*args, **kwargs)
File “/usr/local/lib/python3.8/dist-packages/keras/src/engine/training.py”, line 1742, in fit
tmp_logs = self.train_function(iterator)
File “/usr/local/lib/python3.8/dist-packages/keras/src/engine/training.py”, line 1338, in train_function
return step_function(self, iterator)
File “/usr/local/lib/python3.8/dist-packages/keras/src/engine/training.py”, line 1322, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File “/usr/local/lib/python3.8/dist-packages/keras/src/engine/training.py”, line 1303, in run_step
outputs = model.train_step(data)
File “/usr/local/lib/python3.8/dist-packages/keras/src/engine/training.py”, line 1080, in train_step
y_pred = self(x, training=True)
File “/usr/local/lib/python3.8/dist-packages/keras/src/utils/traceback_utils.py”, line 65, in error_handler
return fn(*args, **kwargs)
File “/usr/local/lib/python3.8/dist-packages/keras/src/engine/training.py”, line 569, in call
return super().call(*args, **kwargs)
File “/usr/local/lib/python3.8/dist-packages/keras/src/utils/traceback_utils.py”, line 65, in error_handler
return fn(*args, **kwargs)
File “/usr/local/lib/python3.8/dist-packages/keras/src/engine/base_layer.py”, line 1150, in call
outputs = call_fn(inputs, *args, **kwargs)
File “/usr/local/lib/python3.8/dist-packages/keras/src/utils/traceback_utils.py”, line 96, in error_handler
return fn(*args, **kwargs)
File “/usr/local/lib/python3.8/dist-packages/keras/src/engine/functional.py”, line 512, in call
return self._run_internal_graph(inputs, training=training, mask=mask)
File “/usr/local/lib/python3.8/dist-packages/keras/src/engine/functional.py”, line 651, in _run_internal_graph
y = self._conform_to_reference_input(y, ref_input=x)
File “/usr/local/lib/python3.8/dist-packages/keras/src/engine/functional.py”, line 748, in _conform_to_reference_input
tensor = tf.cast(tensor, dtype=ref_input.dtype)
Node: ‘SiameseModel/Cast’
2 root error(s) found.
(0) UNIMPLEMENTED: Cast string to float is not supported
[[{{node SiameseModel/Cast}}]]
(1) CANCELLED: Function was cancelled before it was started
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_38675]

[Deepti_Prasad]

hi @Miranda_Lv

The updated course 3 of NLP has only 3 weeks and 3 graded assignment.

Kindly make sure you updated your labs notebooks before you start any assignment for course 3 and course 4 of NLP specialisation.

assignment name
week 1 Deep N-grams
week 2 NER
week 3 Question Duplicates

perhaps check if you chose the right category??

Regards
DP

[Miranda_Lv]

Hi DP, my mistake, I was working on different HW at the same time. I meant to say the course 3 and week 3 HW, question duplicates. The errors come from the W3 HM.

[Deepti_Prasad]

please send screenshot of the codes from grade cell where you stated you passed the unittest by personal DM

Regards
DP

[Deepti_Prasad]

hi @Miranda_Lv

Based on mistakes found in your codes I have shared few links which will help you resolve your issue

refer the below links to debug your codes

Is train_model of Siamise has correct loss?

hi @Ndachimya_Edward Make sure you have defined inputs of type tf.string Check exercise 1 or where input1 and input2 need to be of tf.string and not cast string. Regards DP

C3W3 NLP - Ex 2 - Struggling with data types

The hint is a bit misleading, even I was stuck because of the same, arvyzukai NLP mentor guided me through So based on the instruction previous shared, axis should be 1 For the mask exclude positives, your code is correct. Next thing, check if the score codes is recalled as (v2, v1, transpose_b=true), note v2 is placed before v1

C3W3 Triplet Loss Function

Hi @roses_r_red Your code requires correction for triplet_loss1 and triplet_loss2 While calculating the A and B A = subtract positive from margin and add closest_negative B = subtract positive from margin and add mean_negative You have used correctly the codes but because you have missed tuppling in these codes caused these error. Remember the instructions mentions first to subtract (margin-positive) and then add closest negative for A and mean_negative for B Also while recalling triplet …

in case you still have any error, please let me know or use search tool to find similar threads stating Siamese, there re many threads already in the discourse community for your issue.

Regards
DP

[Miranda_Lv]

Hi DP, thank you for those links! I’ve got all errors solved, thanks a lot!