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Flashcard 7625622031628

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#tensorflow #tensorflow-certificate

Question

y_test.shape, y_pred.shape, y_pred.reshape((10, )).shape, tf.squeeze(y_pred).shape


(TensorShape([10]), (10, 1), (10,), [...])

Answer

TensorShape([10])

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Different shapes of the tensors
y_test.shape, y_pred.shape, y_pred.reshape((10, )).shape, tf.squeeze(y_pred).shape (TensorShape([10]), (10, 1), (10,), TensorShape([10]))

Flashcard 7626518826252

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#tensorflow #tensorflow-certificate

Question

Preprocessing data

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

Answer

passthrough

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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 7626520923404

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#tensorflow #tensorflow-certificate

Question

Preprocessing data

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

Answer

fit

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Annotation 7626525904140

[unknown IMAGE 7626420784396]

#has-images #tensorflow #tensorflow-certificate

Deep Learning mantras: ;)

Building model: experiment
Evaluation model: visualize

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TfC 01 regression
more epochs, more data ### How? # from smaller model to larger model Evaluating models 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 set) tf.random.set_seed(999) X_train, X_test = tf.spli

Flashcard 7627463068940

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

Question

For example, we can define an LSTM hidden layer with 2 memory cells followed by a Dense output layer with 1 neuron as follows:

model = Sequential()
model.add([...])
model.add(Dense(1))

Answer

LSTM(2)

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For example, we can define an LSTM hidden layer with 2 memory cells followed by a Dense output layer with 1 neuron as follows: model = Sequential() model.add(LSTM(2)) model.add(Dense(1))

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Flashcard 7627466476812

Tags

#tensorflow #tensorflow-certificate

Question

import tensorflow as tf

#stop training after reaching accuract of 0.99
class MyCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, [...]):
    if logs.get('accuracy')>=0.99:
      print('\nAccuracy 0.99 achieved')
      self.model.stop_training = True

Answer

logs={}

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Tensorflow - callbacks
import tensorflow as tf #stop training after reaching accuract of 0.99 class MyCallback(tf.keras.callbacks.Callback): def on_epoch_end(self, epoch, logs={}): if logs.get('accuracy')>=0.99: print('\nAccuracy 0.99 achieved') self.model.stop_training = True

Flashcard 7627468311820

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#tensorflow #tensorflow-certificate

Question

Preprocessing data

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

Answer

X_train

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Flashcard 7627469360396

Tags

#tensorflow #tensorflow-certificate

Question

Preprocessing data

ct = make_column_transformer(([...](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)

Answer

OneHotEncoder

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Annotation 7627470671116

#tensorflow #tensorflow-certificate

F1-score

Combination of precision and recall, ususally a good overall metric for classification models.

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TfC_02_classification-PART_2
anced classes Precision For imbalanced class problems. Higher precision leads to less false positives. Recall Higher recall leads to less false negatives. Tradeoff between recall and precision. F1-score Combination of precision and recall, ususally a good overall metric for classification models. keyboard_arrow_down Confusion matrix Can be hard to use whith large numbers of classes. y-axis -> true label x-axis -> predicted label # Create confusion metrics from sklearn.metr

Annotation 7627472243980

#tensorflow #tensorflow-certificate

# Get the patterns of a layer in our network

weights, biases = model_35.layers[1].get_weights()

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TfC_02_classification-PART_2
tant: This time there is a problem with loss function. In case of categorical_crossentropy the labels have to be one-hot encoded In case of labels as integeres use SparseCategoricalCrossentropy # Get the patterns of a layer in our network weights, biases = model_35.layers[1].get_weights()

Annotation 7627473816844

#tensorflow #tensorflow-certificate

Confusion matrix

Can be hard to use whith large numbers of classes.

y-axis -> true label
x-axis -> predicted label

# Create confusion metrics

from sklearn.metrics import confusion_matrix

y_preds = model_8.predict(X_test)

confusion_matrix(y_test, y_preds)

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TfC_02_classification-PART_2
leads to less false negatives. Tradeoff between recall and precision. F1-score Combination of precision and recall, ususally a good overall metric for classification models. keyboard_arrow_down Confusion matrix Can be hard to use whith large numbers of classes. y-axis -> true label x-axis -> predicted label # Create confusion metrics from sklearn.metrics import confusion_matrix y_preds = model_8.predict(X_test) confusion_matrix(y_test, y_preds) important: This time there is a problem with loss function. In case of categorical_crossentropy the labels have to be one-hot encoded In case of labels as integeres use SparseCategorica

Flashcard 7627475651852

Tags

#has-images #tensorflow #tensorflow-certificate

[unknown IMAGE 7626420784396]

Question

How we can improve model (in the particular stage of the process)?

# 1. [...] model: add more layers, increase numbers of hidden neurons, change activation functions

Answer

Creating

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How we can improve model (in the particular stage of the process)? # 1. Creating model: add more layers, increase numbers of hidden neurons, change activation functions

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TfC 01 regression
#### How we can improve model # 1. Creating model: add more layers, increase numbers of hidden neurons, change 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 Typical workflow: build a model -> fit it -> evaulate -> tweak -> fit > evaluate -> .... Building model: experiment Evaluation model: visualize What

Annotation 7627477486860

#tensorflow #tensorflow-certificate

Multiclass image classificaton: pizza, steak, sushi

Input_shape = [None, 224, 224, 3] - single image

Input shape = [32, 224, 224, 3] - common batch size of images

32 is a common batch size

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TfC_02_classification-PART_1
ssification - a sample can be assigned to more than one label from more than 2 label options Multiclass classification - a sample can be assigned to one label but from more than 2 label options Multiclass image classificaton: pizza, steak, sushi Input_shape = [None, 224, 224, 3] - single image Input shape = [32, 224, 224, 3] - common batch size of images 32 is a common batch size How to generate such data? from sklearn.datasets import make_circles # Make 1000 examples n_samples=1000 # Create circles X, y = make_circles(n_samples, noise=0.03, random_state=42) How

Flashcard 7627480108300

Tags

#has-images #tensorflow #tensorflow-certificate

[unknown IMAGE 7626420784396]

Question

Deep Learning mantras: ;)

Building model: [...]
Evaluation model: visualize

Answer

experiment

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Deep Learning mantras: ;) Building model: experiment Evaluation model: visualize

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Flashcard 7627481943308

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#tensorflow #tensorflow-certificate

Question

Recall

Higher recall leads to less false [...].

Answer

negatives

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Recall Higher recall leads to less false negatives.

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TfC_02_classification-PART_2
ccuracy tf.keras.metrics.Accuracy() sklearn.metrics.accuracy_score() Not the best for imbalanced classes Precision For imbalanced class problems. Higher precision leads to less false positives. Recall Higher recall leads to less false negatives. Tradeoff between recall and precision. F1-score Combination of precision and recall, ususally a good overall metric for classification models. keyboard_arrow_down Confusion matrix Can b

Flashcard 7627483778316

Tags

#tensorflow #tensorflow-certificate

Question

Precision

For [...] problems. Higher precision leads to less false positives.

Answer

imbalanced class

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Precision For imbalanced class problems. Higher precision leads to less false positives.

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TfC_02_classification-PART_2
Classification evaluation methods Accuracy tf.keras.metrics.Accuracy() sklearn.metrics.accuracy_score() Not the best for imbalanced classes Precision For imbalanced class problems. Higher precision leads to less false positives. Recall Higher recall leads to less false negatives. Tradeoff between recall and precision. F1-score Combination of precision and recall, ususally a good overall metric for classificatio

Annotation 7627488759052

[unknown IMAGE 7626420784396]

#has-images #tensorflow #tensorflow-certificate

)

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(figsize=(10, 7)) 
   plt.scatter(train_data, train_labels, c="blue", label='Training data') 
   plt.scatter(test_data, test_labels, c="green", label="Testing data") 
   plt.scatter(test_data, predictions, c="red", label="Predictions") 
   plt.legend();

C

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TfC 01 regression
ing of a model predictions ## 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(figsize=(10, 7)) plt.scatter(train_data, train_labels, c="blue", label='Training data') plt.scatter(test_data, test_labels, c="green", label="Testing data") plt.scatter(test_data, predictions, c="red", label="Predictions") plt.legend(); Common regression evaluation metrics keyboard_arrow_down Introduction For regression problems: MAE tf.keras.losses.MAE() tf.metrics.mean_absolute_error() great starter metrics for any reg

Annotation 7627490331916

[unknown IMAGE 7626420784396]

#has-images #tensorflow #tensorflow-certificate

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(figsize=(10, 7)) plt.scatter(train_data, train_labels, c="blue", label='Training data') plt.scatter(test_data, test_labels, c="green", label="Testing data") plt.scatter(test_data, predictions, c="red", label="Predictions") plt.legend();

Common regression evaluation metrics

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TfC 01 regression
iment 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 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(figsize=(10, 7)) plt.scatter(train_data, train_labels, c="blue", label='Training data') plt.scatter(test_data, test_labels, c="green", label="Testing data") plt.scatter(test_data, predictions, c="red", label="Predictions") plt.legend(); Common regression evaluation metrics keyboard_arrow_down Introduction For regression problems: MAE tf.keras.losses.MAE() tf.metrics.mean_absolute_error() great starter metrics for any regression problem MSE tf.keras.losses

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on 19-May-2024 (Sun)

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How we can improve model (in the particular stage of the process)?

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C

Common regression evaluation metrics