Creazione di un DNN con l'API funzionale Keras recensioni

Christopher D. · Recensione inserita 8 mesi fa

Martin L. · Recensione inserita 8 mesi fa

Odar M. · Recensione inserita 8 mesi fa

Harsh B. · Recensione inserita 8 mesi fa

Issues with defining features: def features_and_labels(row_data):     for unwanted_col in ['pickup_datetime', 'key']:         row_data.pop(unwanted_col)     label = row_data.pop(LABEL_COLUMN)     return row_data, label  # features, label # load the training data def load_dataset(pattern, batch_size=1, mode=tf.estimator.ModeKeys.EVAL):   dataset = (tf.data.experimental.make_csv_dataset(pattern, batch_size, CSV_COLUMNS, DEFAULTS)              .map(features_and_labels) # features, label              )   if mode == tf.estimator.ModeKeys.TRAIN:         dataset = dataset.shuffle(1000).repeat()   dataset = dataset.prefetch(1) # take advantage of multi-threading; 1=AUTOTUNE   return dataset ---------------------------------------------------------------------------AttributeError Traceback (most recent call last) Cell In[11], line 8 5 return row_data, label # features, label 7 # load the training data----> 8 def load_dataset(pattern, batch_size=1, mode=tf.estimator.ModeKeys.EVAL): 9 dataset = (tf.data.experimental.make_csv_dataset(pattern, batch_size, CSV_COLUMNS, DEFAULTS) 10 .map(features_and_labels) # features, label 11 ) 12 if mode == tf.estimator.ModeKeys.TRAIN:AttributeError: module 'tensorflow' has no attribute 'estimator' --------------- Per Gemini - this worked The tf.estimator module is outdated and no longer the recommended way to build models in TensorFlow. It's been largely superseded by Keras, which provides a more user-friendly and flexible API for building and training models. Here's how you can update your code to use Keras instead of tf.estimator: ------- # ... (your features_and_labels function remains the same) # Load the training data using tf.data.Dataset def load_dataset(pattern, batch_size=1, mode='eval'): # Use strings for mode dataset = (tf.data.experimental.make_csv_dataset(pattern, batch_size, CSV_COLUMNS, DEFAULTS) .map(features_and_labels) # features, label ) if mode 1 == 'train': dataset = dataset.shuffle(1000).repeat() dataset = dataset.prefetch(1) # take advantage of multi-threading; 1=AUTOTUNE return dataset ----- mode parameter: Instead of using tf.estimator.ModeKeys, we'll simply use strings like 'train' or 'eval' to represent the mode. No tf.estimator: We've removed the dependency on tf.estimator altogether.

Rod M. · Recensione inserita 8 mesi fa

Great Lab!

Francisco A. · Recensione inserita 8 mesi fa

Ioana B. · Recensione inserita 8 mesi fa

Thomas N. · Recensione inserita 8 mesi fa

David L. · Recensione inserita 8 mesi fa

Ronny d. · Recensione inserita 8 mesi fa

Anderson I. · Recensione inserita 8 mesi fa

good

Nikhitha K. · Recensione inserita 8 mesi fa

David O. · Recensione inserita 8 mesi fa

敬源 黃. · Recensione inserita 8 mesi fa

Harry M. · Recensione inserita 8 mesi fa

Sakshi Nagare .. · Recensione inserita 8 mesi fa

ok

Raul H. · Recensione inserita 8 mesi fa

ok

Raul H. · Recensione inserita 8 mesi fa

Víctor P. · Recensione inserita 8 mesi fa

Paulo C. · Recensione inserita 8 mesi fa

Valeria A. · Recensione inserita 8 mesi fa

Pablo J. · Recensione inserita 8 mesi fa

Saulo R. · Recensione inserita 8 mesi fa

Xiomara O. · Recensione inserita 8 mesi fa

Paulo C. · Recensione inserita 8 mesi fa