Important Keras Questions for Cracking Deep Learning Interviews

In Keras, convolutional layers are a type of layer where most of the computation happens in convolutional neural networks. These layers apply a convolution operation to the input, a mathematical operation that takes two inputs (a matrix and a kernel) and produces an output (a matrix).

Pooling layers in Keras are used to reduce the dimensionality of the data while retaining important information. Pooling layers are classified into two types: average pooling andmaximum pooling. Average pooling takes the average value
from each pool, whereas max pooling takes the maximum value from each pool.

Dropout is a deep learning technique widely used to prevent overfitting, which occurs when a model gets highly trained on training data and fails to perform well on unseen data.

Dropout is a Keras layer that randomly removes a particular fraction of inputs during training. Dropout’s main goal is to keep neurons from becoming overly specialized and to encourage the network to acquire more broad properties. When inputs are dropped out at random, the network is forced to learn multiple representations of the same data, which can improve its overall performance when dealing with new data.

The batch size is a hyperparameter that determines the number of samples processed before the model is updated during training. Choosing the right batch size can significantly impact the training process and the model performance.

In general, larger batch sizes can result in faster training times but may also lead to overfitting or poorer generalization performance. On the other hand, smaller batch sizes can lead to slower training times but may result in better generalization performance.

We can select an optimal batch size using different factors, such as dataset size, model complexity, and the available hardware resources like CPU, GPU, RAM. We can experiment with a recommended batch size of 32 or 64 and try with larger or smaller batch sizes to find the optimal value.

Compiling a neural network in Keras requires us to define the optimizer, loss function, and metrics used during training. This can be done using the compile() function, which uses different arguments like an optimizer, loss function,
metrics, etc. For example:

model.compile(
  loss = 'categorical_crossentropy', 
  optimizer = 'sgd', 
  metrics = ['accuracy']
)

Making predictions using a neural network in Keras involves using the predict() function, which takes in the input data as an argument. The function will return the predicted output values. For example:

ypred = model.predict(X)

In Keras, the performance of a model can be monitored during training using callbacks. Callbacks are functions that are called during the training process at defined time spans (e.g., at the end of each epoch).

Keras provides several built-in callbacks that can be used to monitor the performance of a model, like the ModelCheckpoint callback, which saves the model weights after each epoch, and the ReduceLROnPlateau callback, which reduces the learning rate if the validation loss does not improve for a certain number of epochs.

callback = [
    tf.keras.callbacks.ModelCheckpoint(filepath='model.{epoch:02d}-{val_loss:.4f}.h5')
    ]

In addition to the built-in callbacks, we can define custom callbacks in Keras, which can be used to perform different tasks. For example, logging the performance of the model to a file, stopping the training process early if the model overfits, or
sending an email notification upon completion of the training process.

Early stopping is a technique that can help prevent overfitting and optimize the performance of a model during training. This technique involves monitoring the model’s performance on a validation dataset and stopping the training process early if the performance stops improving. In Keras, the EarlyStopping callback can be used to implement early stopping during training.

callback = [
    tf.keras.callbacks.EarlyStopping(patience=2)
    ]

The EarlyStopping callback can be added to a model’s fit() method, allowing the user to select a monitoring metric and a patience value. The patience value determines the number of epochs the model should wait before stopping the training process. Early stopping can help ensure that the model is trained for the optimal number of epochs while minimizing the risk of overfitting or underfitting.

The Transfer learning technique uses a pre-trained model to build a new model. By using the pre-trained weights and architecture of an existing model, transfer learning reduces the amount of data and the model training time
necessary to achieve a good model fit.

We can use transfer learning in Keras by importing a pre-trained model and adding further layers on top of it. The newly added layers can then be trained on a new dataset. For example, with Keras, we can apply transfer learning to fine-tune
pre-trained models such as VGG16 or ResNet for image classification tasks.

Data augmentation technique is used to improve the model performance, especially in cases where the dataset is small or imbalanced. It involves generating new training data by applying random transformations to existing data. With new training data, data augmentation can help the model generalize to new data and reduce the risk of overfitting.

We can implement data augmentation using the ImageDataGenerator() class, which provides several built-in transformations like rotation, scaling, and flipping, etc. For example:

from keras.preprocessing.image import ImageDataGenerator
datagen = ImageDataGenerator(
          rotation_range=30,
          rescale=1./255,
          horizontal_flip=True)