Predictive Analytics with TensorFlow 8.6: CNN Predictive Model for Image Classification

The video tutorial covers the development of a CNN predictive model for image classification. It begins with an introduction to CNNs and the initial setup, followed by data preparation and preprocessing. The tutorial then delves into building the CNN architecture, including defining layers and parameters. The training process is explained, highlighting optimization techniques and accuracy measurement. Finally, the video discusses model evaluation, using performance metrics like confusion matrices and validation accuracy.

What is the first step in developing a CNN predictive model?

• Ignoring warnings

• Loading the dataset

• Setting the batch size

• Defining the learning rate

How many color channels are set for the images in the dataset preparation?

• 1

• 2

• 3

• 4

What is the purpose of the flatten layer in a CNN?

• To apply max pooling

• To convert the image into a single dimension

• To add non-linearity

• To increase the number of channels

Which activation function is used in the fully connected layers of the CNN?

• Sigmoid

• Tanh

• Softmax

• ReLU

What is the batch size used during the training of the CNN model?

• 16

• 32

• 64

• 128

Which function is used to initialize all variables in TensorFlow during training?

• init_all

• global_variables_initializer

• variable_initializer

• initialize_variables

What is the purpose of the plot example errors function?

• To plot the training loss

• To plot the validation accuracy

• To plot incorrectly classified images

• To plot correctly classified images

Which library is used to compute the confusion matrix?

• Pandas

• NumPy

• SKlearn

• Matplotlib

What is the main goal of fine-tuning the model?

• To increase the number of layers

• To improve model accuracy

• To reduce the dataset size

• To change the activation function

What is the final step after evaluating the model?

• Changing the dataset

• Adding more layers

• Re-training the model

• Closing the TensorFlow session