Opinie (TensorFlow Dataset API)

Vikram M. · Sprawdzono ponad 2 lata temu

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. · Sprawdzono ponad 2 lata temu

Olha B. · Sprawdzono ponad 2 lata temu

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

ep m. · Sprawdzono ponad 2 lata temu

Alejandro A. · Sprawdzono ponad 2 lata temu

Adán T. · Sprawdzono ponad 2 lata temu

Santosh S. · Sprawdzono ponad 2 lata temu

Rafael G. · Sprawdzono ponad 2 lata temu

Stepan G. · Sprawdzono ponad 2 lata temu

Pablo Leonardo L. · Sprawdzono ponad 2 lata temu

Tomas B. · Sprawdzono ponad 2 lata temu

Sonali R. · Sprawdzono ponad 2 lata temu

Николай К. · Sprawdzono ponad 2 lata temu

Gaurav P. · Sprawdzono ponad 2 lata temu

Tejas T. · Sprawdzono ponad 2 lata temu

Siwatchara S. · Sprawdzono ponad 2 lata temu

Sharad Kumar G. · Sprawdzono ponad 2 lata temu

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. · Sprawdzono ponad 2 lata temu

Walter D. · Sprawdzono ponad 2 lata temu

Alfredo B. · Sprawdzono ponad 2 lata temu

Marc N. · Sprawdzono ponad 2 lata temu

W T. · Sprawdzono ponad 2 lata temu

Anagha B. · Sprawdzono ponad 2 lata temu

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. · Sprawdzono ponad 2 lata temu

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. · Sprawdzono ponad 2 lata temu