Hey, I'm reading through the book and thoroughly enjoying it, thanks @lakshmanok!

I have some trouble viewing the notebooks here on GitHub though (it keeps saying "Sorry, something went wrong", likely hitting some size limits).

I work at Deepnote, and we try to build better data science notebooks. The proposed button opens the repo as a project in Deepnote, and you can view and execute all of the files – it might be helpful for other readers too :)

Dear authors, the evaluate component of the pipeline fails due to the lack of pyarrow module.

Solved by changing the module request in the pipeline definition :

dsl.pipeline(
    name='Cascade pipeline on SF bikeshare',
    description='Cascade pipeline on SF bikeshare'
)

def cascade_pipeline(
    project_id = PROJECT_ID
):
    ddlop = comp.func_to_container_op(run_bigquery_ddl, packages_to_install=['google-cloud-bigquery'])
        
    c1 = train_classification_model(ddlop, PROJECT_ID)
    c1_model_name = c1.outputs['created_table']
    
    c2a_input = create_training_data(ddlop, PROJECT_ID, c1_model_name, 'Typical')
    c2b_input = create_training_data(ddlop, PROJECT_ID, c1_model_name, 'Long')
    
    c3a_model = train_distance_model(ddlop, PROJECT_ID, c2a_input.outputs['created_table'], 'Typical')
    c3b_model = train_distance_model(ddlop, PROJECT_ID, c2b_input.outputs['created_table'], 'Long')
    
    evalop = comp.func_to_container_op(evaluate, packages_to_install=['google-cloud-bigquery[bqstorage,pandas]', 'pandas'])
    error = evalop(PROJECT_ID, c1_model_name, c3a_model.outputs['created_table'], c3b_model.outputs['created_table'])
    print(error.output)

Best Regards

Jerome

shap.DeepExplainer call fails with error "Can't convert non-rectangular Python sequence to Tensor". tensorflow version 2.1.1 shap version 0.37.0

seems similar to: https://github.com/slundberg/shap/issues/850 thanks, jim

The order of the publications is inconsistent between the original CLASSES definition and the source name function:

CLASSES = { 'github': 0, 'nytimes': 1, 'techcrunch': 2 }

labels = tf.constant(['github', 'techcrunch', 'nytimes'], dtype=tf.string)

Suggest:

labels = tf.constant(['github', 'nytimes','techcrunch'], dtype=tf.string)

1. The munn-sandbox is not publically available so the txtcls is not available.

I created using code from: https://datalab.office.datisan.com.au/notebooks/training-data-analyst/blogs/textclassification/txtcls.ipynb

as:

query=""" SELECT source, REGEXP_REPLACE(title, '[^a-zA-Z0-9 $.-]', ' ') AS title FROM (SELECT ARRAY_REVERSE(SPLIT(REGEXP_EXTRACT(url, '.://(.[^/]+)/'), '.'))[OFFSET(1)] AS source, title FROM bigquery-public-data.hacker_news.stories WHERE REGEXP_CONTAINS(REGEXP_EXTRACT(url, '.://(.[^/]+)/'), '.com$') AND LENGTH(title) > 10 ) WHERE (source = 'github' OR source = 'nytimes' OR source = 'techcrunch') """

from google.cloud import bigquery client = bigquery.Client() df = client.query(query).to_dataframe() df.to_csv('titles_full.csv', header=False, index=False, encoding='utf-8', sep=',')

I had to swap the column order: COLUMNS = ['source', 'title']

2. With EarlyStopping enabled training finished after just 2 Epochs

callbacks=[EarlyStopping(), TensorBoard(model_dir)],

without it loss was minimised after 20.

3. Evaluation job section is 'to-do':

"some stuff here about setting up Eval jobs"

From the continuous_eval.ipynb notebook:

However, I see the option on "Evaluation" is not available (disabled):

What's the recommended course of action here?

Hi everyone,

I have no access to thte bucket to get the dataset. AccessDeniedException: 403 [email protected] does not have storage.objects.list access to the Google Cloud Storage bucket.

Best Regards

Jerome

• "audio" path should be "audio_spectros".
• "image_batch" variable not found.
• fit_generator() method deprecated, prefer fit().
• The spectrometer png files get moved, so need to redownload or copy them.

Hello, I am studying this book. Thank you for writing such a well-structured textbook.

I have a question about the Bridged Schema pattern in section 23. How should I determine the amount of old data to be added to the training data.

In this repository, it is stated that adding 60,000 old data is best. However, in the line graph of the number of data and the R2 value, 60,000 is rather at the bottom of the R2 value. The higher the R2 value, the better the prediction, so it looks rather like the prediction accuracy is decreasing as older data is added. From the results of this graph, I may conclude that the prediction accuracy is higher when learning only with new data.

I would be glad if anyone tell me why you decide that adding 60,000 old data was the best.

@munnm, in the 05_resilience/continuous_eval.ipynb notebook, please set some value for the patience (preferably > 1) argument in the EarlyStopping() callback. Otherwise, it'd only run for a single epoch. Users not familiar with the EarlyStopping callback API of Keras might not understand what's wrong at the very first glance.

Dear authors,

it seems to me that the Section 2 of the continuous evaluation notebook needs to be updated. Indeed, the continuous evaluation mode is not more straightforward and requires more setup information to be used flawlessly,

Thanks Best Regards

Jerome

Hi authors team,

just to inform you that the checkpointing callback is not called in the fit() method. I guess that it is a typo as the callback is used in the code snippet in the book.

Best regards

Jerome