API Dataset do TensorFlow avaliações

Vikram M. · Revisado há over 2 years

I had some difficulty following along with this lab since the previous lab exercise was not included in this course. I would recommend adding the first lab in that folder to the course since future labs reference it.

Charles B. · Revisado há over 2 years

Olha B. · Revisado há over 2 years

Need more clarity on where to run the commands - on Terminal or just click on the arrow of the instructions window?

ep m. · Revisado há over 2 years

Alejandro A. · Revisado há over 2 years

Adán T. · Revisado há over 2 years

Santosh S. · Revisado há over 2 years

Rafael G. · Revisado há over 2 years

Stepan G. · Revisado há over 2 years

Pablo Leonardo L. · Revisado há over 2 years

Tomas B. · Revisado há over 2 years

Sonali R. · Revisado há over 2 years

Николай К. · Revisado há over 2 years

Gaurav P. · Revisado há over 2 years

Tejas T. · Revisado há over 2 years

Siwatchara S. · Revisado há over 2 years

Sharad Kumar G. · Revisado há over 2 years

quite messy unclear how to do tutorials with this part, and functions are not working as expected. lab task #2 --------------------------------------------------------------------------- AssertionError Traceback (most recent call last) Cell In[66], line 26 23 loss =loss_mse(X_batch, Y_batch, w0, w1) # TODO -- Your code here. 24 print(MSG.format(step=step, loss=loss, w0=w0.numpy(), w1=w1.numpy())) ---> 26 assert loss < 0.0001 27 assert abs(w0 - 2) < 0.001 28 assert abs(w1 - 10) < 0.001 AssertionError: part 4b AttributeError Traceback (most recent call last) Cell In[108], line 5 1 BATCH_SIZE = 2 3 tempds = create_dataset('../toy_data/taxi-train*', batch_size=2) ----> 5 for X_batch, Y_batch in tempds.take(2): 6 pprint({k: v.numpy() for k, v in X_batch.items()}) 7 print(Y_batch.numpy(), "\n") AttributeError: 'tuple' object has no attribute 'take' part 4c --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[112], line 1 ----> 1 tempds = create_dataset('../toy_data/taxi-train*', 2, 'train') 2 print(list(tempds.take(1))) Cell In[111], line 6, in create_dataset(pattern, batch_size, mode) 2 def create_dataset(pattern, batch_size=1, mode='eval'): 3 dataset = tf.data.experimental.make_csv_dataset( 4 pattern, batch_size, CSV_COLUMNS, DEFAULTS) ----> 6 dataset = tf.data(pattern) # TODO -- Your code here. 8 if mode == 'train': 9 dataset = dataset.shuffle() # TODO -- Your code here. TypeError: 'module' object is not callable

Mika K. · Revisado há over 2 years

Walter D. · Revisado há over 2 years

Alfredo B. · Revisado há over 2 years

Marc N. · Revisado há over 2 years

W T. · Revisado há over 2 years

Anagha B. · Revisado há over 2 years

Sometimes during coding you just do not know what you have to do if you are not familiar with Tensorflow syntax. But still small enough steps to test and try!

Daniel S. · Revisado há over 2 years

Good practice, some instructions could've been more clear ( e.g. about desired way of implementing things like filtering feature columns, buffer size for shuffling ). Also it instructed to set "batch_size, column_names and column_defaults" in the first create_dataset which apparently wasn't actually desired ( batch_size shouldn't be set yet, only in the second time, otherwise there'll be an assertion error as it's expecting just scalars in the aserts )

Jasper v. · Revisado há over 2 years