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on 26-May-2024 (Sun)

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Flashcard 7626519874828

Tags
#tensorflow #tensorflow-certificate
Question
Preprocessing data 

ct = make_column_transformer((OneHotEncoder(dtype="int32"), ['Sex']), [...]="passthrough") #other columns unchangaed
ct.fit(X_train) X_train_transformed = ct.transform(X_train)
X_test_transformed = ct.transform(X_test)
Answer
remainder

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Preprocessing data ct = make_column_transformer((OneHotEncoder(dtype="int32"), ['Sex']), remainder="passthrough") #other columns unchangaed ct.fit(X_train) X_train_transformed = ct.transform(X_train) X_test_transformed = ct.transform(X_test)

Original toplevel document

TfC_01_ADDITIONAL_01_Abalone.ipynb
Preprocessing data ct = make_column_transformer((OneHotEncoder(dtype="int32"), ['Sex']), remainder="passthrough") #other columns unchangaed ct.fit(X_train) X_train_transformed = ct.transform(X_train) X_test_transformed = ct.transform(X_test) Predictions valuation_predicts = model.predict(X_valuation_transformed) (array([[ 9.441547], [10.451973], [10.48082 ], ..., [10.401164], [13.13452 ], [ 8.081818]], dtype=float32), (6041






Flashcard 7627737009420

Tags
#has-images #tensorflow #tensorflow-certificate
[unknown IMAGE 7626420784396]
Question

Saving and loading models

Two formats:

  • SavedModel format (including optimizer's [...])
  • HDF5 format
Answer
step

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

Parent (intermediate) annotation

Open it
Saving and loading models Two formats: SavedModel format (including optimizer's step) HDF5 format

Original toplevel document

TfC 01 regression
is to track the results of your experiments. There are tools to help us! Resource: Try: Tensorboard - a component of Tensorflow library to help track modelling experiments Weights & Biases <span>Saving and loading models Two formats: SavedModel format (including optimizer's step) HDF5 format What about TensorFlow Serving format? # Save the entire model using SavedModel model_3.save("best_model_3_SavedModel") # SavedModel is in principle protobuff)pb file # Save model in HDF5 format: model_3.save("best_model_3_HDF5.h5") Load model loaded_model_SM = tf.keras.models.load_model('/content/best_model_3_SavedModel') loaded_model_SM.summary() <span>