Regression with Tensorflow using KeraAPI Practical Assignment

Play an AI-generated podcast conversation about this lesson

What is the purpose of the 'mean_squared_error' function in the provided TensorFlow code?

To calculate the mean absolute error between true and predicted values

To calculate the mean of the squared differences between true and predicted values (correct)

To calculate the sum of the squared differences between true and predicted values

To calculate the sum of absolute differences between true and predicted values

In the linear regression model presented, what does the 'w' variable represent?

Weight matrix (correct)

Bias matrix

Loss function

Activation function

What is the purpose of 'optimizer.apply_gradients(zip(gradients,[w,b]))' in the TensorFlow code?

Updates the weights and biases based on gradients calculated during backpropagation (correct)

Initializes the weights and biases of the neural network

Implements stochastic gradient descent algorithm

Calculates the gradients of the loss function with respect to weights and biases

What does 'plt.scatter(X,Y,label='Original data')' do in the provided code snippet?

Plots the original data points as a scatter plot Signup and view all the answers

What is the purpose of 'model.compile(optimizer='adam',loss='mean_squared_error',metrics=[' accuracy'])' in the TensorFlow code?

Configures the model for training by specifying optimizer, loss function, and metrics to monitor Signup and view all the answers

What does 'y_train = tf.keras.utils.to_categorical(y_train , num_classes =10)' accomplish in the TensorFlow code?

Converts class labels into one-hot encoded vectors for multi-class classification Signup and view all the answers

What is the purpose of using 'relu' as the activation function in this TensorFlow model?

To introduce non-linearity to the model Signup and view all the answers

Why is the 'adam' optimizer chosen for compiling the TensorFlow model?

It helps efficiently minimize the loss function Signup and view all the answers

What is the purpose of scaling the input data using StandardScaler in this TensorFlow model?

To ensure all input features have a similar scale Signup and view all the answers

What does 'model.evaluate()' return when evaluating the TensorFlow model on test data?

Loss value for the test data Signup and view all the answers

In what format are the predictions stored after using 'model.predict()' on x_test?

Array Signup and view all the answers

Why is it important to split data into training and test sets when training a machine learning model?

To evaluate how well the model generalizes to new, unseen data Signup and view all the answers

What is the purpose of using 'StandardScaler' in the provided code snippet?

To scale the features to have a mean of 0 and a variance of 1 Signup and view all the answers

What activation function is used in the last layer of the neural network model in the provided code snippet?

Softmax Signup and view all the answers

What metric is being optimized according to the 'model.compile' function in the provided TensorFlow code?

Mean Squared Error (MSE) Signup and view all the answers

What is the purpose of the 'validation_split' parameter in the 'model.fit' function?

To split the training data for validation during model training Signup and view all the answers

In the provided code snippet, if the final training accuracy was 0.85, what would be a typical value for the final validation accuracy?

0.82 Signup and view all the answers

What does 'history.history['loss'][-1]' represent in the print statement 'print(f"Final training loss: {history.history['loss'][-1]}")'?

The final training loss after the last epoch Signup and view all the answers