Regression with Tensorflow using KeraAPI Practical Assignment
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Questions and Answers

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?

    <p>Plots the original data points as a scatter plot</p> 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?

    <p>Configures the model for training by specifying optimizer, loss function, and metrics to monitor</p> 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?

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

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

    <p>To introduce non-linearity to the model</p> Signup and view all the answers

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

    <p>It helps efficiently minimize the loss function</p> Signup and view all the answers

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

    <p>To ensure all input features have a similar scale</p> Signup and view all the answers

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

    <p>Loss value for the test data</p> Signup and view all the answers

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

    <p>Array</p> Signup and view all the answers

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

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

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

    <p>To scale the features to have a mean of 0 and a variance of 1</p> 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?

    <p>Softmax</p> Signup and view all the answers

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

    <p>Mean Squared Error (MSE)</p> Signup and view all the answers

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

    <p>To split the training data for validation during model training</p> 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?

    <p>0.82</p> 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]}")'?

    <p>The final training loss after the last epoch</p> Signup and view all the answers

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