In the first submission for week 3, I couldn't submit. Here is the error: AttributeError: module 'grading_utils' has no attribute 'model_total_params'

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-4-856143fffc33> in <module>()
      8 #Those attributes will be required for the final part of the assignment (applying smiles), so please keep them in mind
      9 from lfw_dataset import load_lfw_dataset
---> 10 data,attrs = load_lfw_dataset(dimx=36,dimy=36)
     11 
     12 #preprocess faces

~/GitHub/intro-to-dl/week4/lfw_dataset.py in load_lfw_dataset(use_raw, dx, dy, dimx, dimy)
     52 
     53     # preserve photo_ids order!
---> 54     all_attrs = photo_ids.merge(df_attrs, on=('person', 'imagenum')).drop(["person", "imagenum"], axis=1)
     55 
     56     return all_photos, all_attrs

~/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py in merge(self, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)
   6377                      right_on=right_on, left_index=left_index,
   6378                      right_index=right_index, sort=sort, suffixes=suffixes,
-> 6379                      copy=copy, indicator=indicator, validate=validate)
   6380 
   6381     def round(self, decimals=0, *args, **kwargs):

~/anaconda3/lib/python3.6/site-packages/pandas/core/reshape/merge.py in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)
     58                          right_index=right_index, sort=sort, suffixes=suffixes,
     59                          copy=copy, indicator=indicator,
---> 60                          validate=validate)
     61     return op.get_result()
     62 

~/anaconda3/lib/python3.6/site-packages/pandas/core/reshape/merge.py in __init__(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, copy, indicator, validate)
    552         # validate the merge keys dtypes. We may need to coerce
    553         # to avoid incompat dtypes
--> 554         self._maybe_coerce_merge_keys()
    555 
    556         # If argument passed to validate,

~/anaconda3/lib/python3.6/site-packages/pandas/core/reshape/merge.py in _maybe_coerce_merge_keys(self)
    976             # incompatible dtypes GH 9780, GH 15800
    977             elif is_numeric_dtype(lk) and not is_numeric_dtype(rk):
--> 978                 raise ValueError(msg)
    979             elif not is_numeric_dtype(lk) and is_numeric_dtype(rk):
    980                 raise ValueError(msg)

ValueError: You are trying to merge on int64 and object columns. If you wish to proceed you should use pd.concat

def link_all_keras_resources():
    link_all_files_from_dir("../readonly/keras/datasets/", os.path.expanduser("~/.keras/datasets"))
    link_all_files_from_dir("../readonly/keras/models/", os.path.expanduser("~/.keras/models"))

which datas are belong to the datasets and models dir ? (with name).

def link_week_6_resources():
    link_all_files_from_dir("../readonly/week6/", ".")

which datas are belong to the week6 dir ? (with name).

Please, explain this two function. I want to run week-6 image_captionong_project into my local jupyter-notebook.

Please help me . THANKS

Hi,

It seems like

from util import eval_numerical_gradient

not working. (week 2 honor assignment)

It can work by manually adding eval_numerical_gradientm function but it would be better if linked.

Cheers, Nan

The Kernel dies after epoch 2 and the callbacks doesn't work, both in Colab & Jupyter notebooks. The result is always 6 out of 9 because the progress halts after that. Please help me complete the work and submit the results.

It's an earnest request to the mentors, tutors , instructors to please consider those students facing such issues and provide assistance.

As for my case , it's the only project left in the entire specialization and it's completion.

I will be extremely grateful for the opportunity for the peer review to be made accessible to all the learners whether they are undergoing the same issue for a long span of time or otherwise.

Will be eagerly awaiting a response.

Regards,

Saheli Basu

Original: So far our model is staggeringly inefficient. There is something wring with it. Guess, what?

Changed to: So far, our model is staggeringly inefficient. There is something wrong with it. Guess, what?

I tried running on my local computer

model.fit( x_train2, y_train2, # prepared data batch_size=BATCH_SIZE, epochs=EPOCHS, callbacks=[keras.callbacks.LearningRateScheduler(lr_scheduler), LrHistory(), keras_utils.TqdmProgressCallback(), keras_utils.ModelSaveCallback(model_filename)], validation_data=(x_test2, y_test2), shuffle=True, verbose=0, initial_epoch=last_finished_epoch or 0 )

But it returned me this error

~\Documents\kkbq\Coursera\Intro to Deep Learning\intro-to-dl\keras_utils.py in _set_prog_bar_desc(self, logs) 27 28 def _set_prog_bar_desc(self, logs): ---> 29 for k in self.params['metrics']: 30 if k in logs: 31 self.log_values_by_metric[k].append(logs[k])

KeyError: 'metrics'

Does anyone know why this happened? Thanks.

In one of the last cells,

model.compile(
    loss='categorical_crossentropy',  # we train 102-way classification
    optimizer=keras.optimizers.adamax(lr=1e-2),  # we can take big lr here because we fixed first layers
    metrics=['accuracy']  # report accuracy during training
)

AttributeError: module 'keras.optimizers' has no attribute 'adamax'

This can be fixed by changing "adamax" to "Adamax". However, after that the second next cell:

# fine tune for 2 epochs (full passes through all training data)
# we make 2*8 epochs, where epoch is 1/8 of our training data to see progress more often
model.fit_generator(
    train_generator(tr_files, tr_labels), 
    steps_per_epoch=len(tr_files) // BATCH_SIZE // 8,
    epochs=2 * 8,
    validation_data=train_generator(te_files, te_labels), 
    validation_steps=len(te_files) // BATCH_SIZE // 4,
    callbacks=[keras_utils.TqdmProgressCallback(), 
               keras_utils.ModelSaveCallback(model_filename)],
    verbose=0,
    initial_epoch=last_finished_epoch or 0
)

throws the following error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-183-faf1b24645ff> in <module>()
     10                keras_utils.ModelSaveCallback(model_filename)],
     11     verbose=0,
---> 12     initial_epoch=last_finished_epoch or 0
     13 )

2 frames
/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
     85                 warnings.warn('Update your `' + object_name +
     86                               '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 87             return func(*args, **kwargs)
     88         wrapper._original_function = func
     89         return wrapper

/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, initial_epoch)
   1723 
   1724         do_validation = bool(validation_data)
-> 1725         self._make_train_function()
   1726         if do_validation:
   1727             self._make_test_function()

/usr/local/lib/python3.6/dist-packages/keras/engine/training.py in _make_train_function(self)
    935                 self._collected_trainable_weights,
    936                 self.constraints,
--> 937                 self.total_loss)
    938             updates = self.updates + training_updates
    939             # Gets loss and metrics. Updates weights at each call.

TypeError: get_updates() takes 3 positional arguments but 4 were given

keras.optimizers.Adamax() inherits the get_updates() method from keras.optimizers.Optimizer(), and that method takes only three arguments (self, loss, params), but _make_train_function is trying to pass four arguments to it.

As I understand it, the issue here is compatibility between tf 1.x and tf 2. I'm using colab and running the %tensorflow_version 1.x line, as well as the setup cell with week 3 setup uncommented at the start of the notebook.

All checkpoints up to this point have been passed succesfully.

Hi there, I face a lot of problem to create the environment. I want to use my GPU as I used to do but here, to run your environment I face a lot a package conflicts. I spent 4 hours trying to to make working tensorflow==1.2.1 & Keras==2.0.6 (with theano ).

(nvidia-docker does not work on my Debian so I would use a stable conda environment) Please update the co-lab with tensflow 2+

The original code does not work fine in the Google colab. Please add following code: !pip install q keras==2.0.6 to these lines of codes: ! shred -u setup_google_colab.py ! wget https://raw.githubusercontent.com/hse-aml/intro-to-dl/master/setup_google_colab.py -O setup_google_colab.py import setup_google_colab please, uncomment the week you're working on setup_google_colab.setup_week1() setup_google_colab.setup_week2() setup_google_colab.setup_week2_honor() setup_google_colab.setup_week3() setup_google_colab.setup_week4() setup_google_colab.setup_week5() setup_google_colab.setup_week6()