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Annotation 7626430745868

#tensorflow #tensorflow-certificate

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)
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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 7626542943500

Tags
#has-images #recurrent-neural-networks #rnn
[unknown IMAGE 7101511240972]
Question
Note that the model is completely [...] about further extensions: all individual-level, cohort-level, time-varying, or time-invariant covariates are simply encoded as categorical input variables, and are handled equally by the model
Answer
agnostic

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Note that the model is completely agnostic about further extensions: all individual-level, cohort-level, time-varying, or time-invariant covariates are simply encoded as categorical input variables, and are handled equally by th

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Flashcard 7626604809484

Tags
#deep-learning #keras #lstm #python #sequence
Question

One Hot Encoding

For categorical variables where no such [...] relationship exists, the integer encoding is not enough.

Answer
ordinal

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One Hot Encoding For categorical variables where no such ordinal relationship exists, the integer encoding is not enough.

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Annotation 7626827369740

#recurrent-neural-networks #rnn
Each prediction is generated by drawing a sample from the multinomial output distribution calculated by the bottom network layer; our model therefore does not produce point or interval estimates, each output is a simulated draw
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Each prediction is generated by drawing a sample from the multinomial output distribution calculated by the bottom network layer; our model therefore does not produce point or interval estimates, each output is a simulated draw. To make the predicted transaction sequences robust against sampling noise, we repeat this process for each customer several times and take the mean expected number of transactions in a

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Flashcard 7626829991180

Tags
#tensorflow #tensorflow-certificate
Question
changeable_tensor = tf.Variable([10, 7])

changeable_tensor[0] = 77

Output:
TypeError: 'ResourceVariable' object does not support item assignment


changeable_tensor[0].assign(77)

Output:
<tf.[...] 'UnreadVariable' shape=(2,) dtype=int32, numpy=array([77,  7], dtype=int32)>

Answer
Variable

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Tensorflow basics
> changeable_tensor = tf.Variable([10, 7]) changeable_tensor[0] = 77 Output: TypeError: 'ResourceVariable' object does not support item assignment changeable_tensor[0].assign(77) Output: <tf.<span>Variable 'UnreadVariable' shape=(2,) dtype=int32, numpy=array([77, 7], dtype=int32)> <span>






Flashcard 7626831039756

Tags
#has-images #tensorflow #tensorflow-certificate
[unknown IMAGE 7626420784396]
Question
Typical workflow: build a model -> fit it -> evaulate -> [...] -> fit > evaluate -> ....
Answer
tweak

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Typical workflow: build a model -> fit it -> evaulate -> tweak -> fit > evaluate -> ....

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TfC 01 regression
activation functions # 2. Compiling: change optimizer or its parameters (eg. learning rate) # 3. Fitting: more epochs, more data ### How? # from smaller model to larger model Evaluating models <span>Typical workflow: build a model -> fit it -> evaulate -> tweak -> fit > evaluate -> .... Building model: experiment Evaluation model: visualize What can visualize? the data model itself the training of a model predictions ## The 3 sets (or actually 2 sets: training and test






Flashcard 7626833661196

Tags
#tensorflow #tensorflow-certificate
Question

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([...])
Answer
X_test

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sformer((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(<span>X_test) <span>

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 7626835234060

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

## The 3 sets (or actually 2 sets: training and test set) - USING ONLY TensorFlow

tf.random.set_seed(999)

X_train, X_test = [...](tf.random.shuffle(X, seed=42), num_or_size_splits=[40, 10])

Answer
tf.split

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## The 3 sets (or actually 2 sets: training and test set) - USING ONLY TensorFlow tf.random.set_seed(999) X_train, X_test = tf.split(tf.random.shuffle(X, seed=42), num_or_size_splits=[40, 10])

Original toplevel document

TfC 01 regression
> evaulate -> tweak -> fit > evaluate -> .... Building model: experiment Evaluation model: visualize What can visualize? the data model itself the training of a model predictions <span>## The 3 sets (or actually 2 sets: training and test set) tf.random.set_seed(999) X_train, X_test = tf.split(tf.random.shuffle(X, seed=42), num_or_size_splits=[40, 10]) def plot_predictions(train_data = X_train, train_labels = y_train, test_data = X_test, test_labels = y_test, predictions = y_pred): """ Plots training data, testing_data """ plt.figure(






Annotation 7626836806924

[unknown IMAGE 7626420784396] #has-images #tensorflow #tensorflow-certificate
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")
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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>



Annotation 7626838379788

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

Load model

loaded_model_SM = tf.keras.models.load_model('/content/best_model_3_SavedModel')
loaded_model_SM.summary()

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TfC 01 regression
# 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") <span>Load model loaded_model_SM = tf.keras.models.load_model('/content/best_model_3_SavedModel') loaded_model_SM.summary() <span>



Annotation 7626839952652

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

Saving and loading models

Two formats:

  • SavedModel format (including optimizer's step)
  • HDF5 format
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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 HD

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>



Flashcard 7626842311948

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

Saving and loading models

Two formats:

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

statusnot learnedmeasured difficulty37% [default]last interval [days]               
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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>






Flashcard 7626844146956

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

Saving and loading models

Two formats:

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

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

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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>






Flashcard 7626845719820

Tags
#tensorflow #tensorflow-certificate
Question
# Calculate MSE "by hand" in steps - identify functions

abs_err = tf.abs(tf.subtract(tf.cast(y_test, dtype=tf.float32), [...](y_pred)))
sq_abs_err = tf.multiply(abs_err, abs_err)
sq_abs_err
tf.math.reduce_mean(sq_abs_err)



<tf.Tensor: shape=(), dtype=float32, numpy=155.11417>

Answer
tf.squeeze

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Calculate MSE &quot;by hand&quot; in steps - identify functions
# Calculate MSE "by hand" in steps - identify functions abs_err = tf.abs(tf.subtract(tf.cast(y_test, dtype=tf.float32), tf.squeeze(y_pred))) sq_abs_err = tf.multiply(abs_err, abs_err) sq_abs_err tf.math.reduce_mean(sq_abs_err) <tf.Tensor: shape=(), dtype=float32, numpy=155.11417>