TensorFlow and Keras -1

 Q1. Binary classification

In order to perform binary classification on a dataset (class 0 and 1) using a neural network, which of the options is correct regarding the outcomes of code snippets a and b? Here the labels of observation are in the form : [0, 0, 1...].

Common model:

import tensorflow
from keras.models import Sequential
from keras.layers import Dense
from tensorflow.keras.optimizers import SGD
model = Sequential()
model.add(Dense(50, input_dim=2, activation='relu', kernel_initializer='he_uniform'))
opt = SGD(learning_rate=0.01)

Code snippet a:

model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])

Code snippet b:

mode.add(Dense(1, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])

The term "Required results" in the options means that the accuracy of the model should be above 60%.

Note: 40% of the dataset is from class 0.

Choose the correct answer from below:

A.     Both a and b will give required results.

B.     Only b will give the required results.

C.     Only a will give the required results.

D.     Both a and b will fail to give required results.

Ans: C

Correct option: only a will give the required results.

Explanation :

• The task requires that the output layer is configured with a single node and a ‘sigmoid‘ activation function in order to predict the probability for the required class. For applying the softmax function for binary classification, the output layer should have 2 neurons for predicting the probability of the two classes individually.

• In order to get the required results using the softmax function we need to have 2 neurons in the output layer and also the labels should be in one-hot encoded format.

 

Q2. Sequential classification model

import numpy as np
from tensorflow.keras import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dense, Dropout, Activation
from tensorflow.keras.optimizers import SGD

model = Sequential()
model.add(Dense(64, activation = 'y', input_dim=50))
model.add(Dense(64, activation = 'y'))
model.add(Dense(x, activation = 'z'))

model.compile(loss ='categorical_crossentropy',
 optimizer = SGD(lr = 0.01),
 metrics = ['accuracy'])

model.fit(X_train, y_train,
 epochs=20)

Ram wants to create a model for the classification of types of malware in 10 different categories. He asked for help from Shyam, and he helped him with the incomplete code as shown above in the snippet. Help Ram in completing the code for classification if the data used has 50 input features. Choose the best-suited option for filling out x, y, and z.

Choose the correct answer from below:

A.     x = len(np.unique(y_train)), y = softmax, z = softmax

B.     x = 2 * len(np.unique(y_train)), y = relu, z = relu

C.     x = len(np.unique(y_train)), y = relu, z = softmax

D.     x = 0.5 * len(np.unique(y_train)), y = relu, z = relu

Ans: C

Correct option :

• x = len(np.unique(y_train))
• y = relu
• z = softmax

Explanation :

• z : For multiclass classification, softmax activation is used.
• x : For the softmax activation, the output layer has the same number of neurons as the number of different classes.

• y : ReLu activation function can definitely be used in the intermediate layers. ReLU is not used in the output layer of classification. Because of it's unbounded range, it's difficult to determine thresholds. Though ReLu can be used in regression tasks where negative values don't make sense like predicting prices.

Q3. Multi target output

For a multi-output regression model:

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

def get_model(n_inputs):
  model = keras.Sequential()
  model.add(Dense(20, input_dim = n_inputs, kernel_initializer='he_uniform', activation='relu'))
  model.add(______)
  model.compile(loss = 'mae', optimizer = 'adam')
  return model

We want to build a neural network for a multi-output regression problem. For each observation, we have 2 outputs. Complete the code snippet to get the desired output.

Choose the correct answer from below:

A.     Dense(2)

B.     Dense(3)

C.     activation('sigmoid')

D.     activation('relu')

Ans:  A

Correct option: Dense(2).

Explanation:
As we have 2 outputs therefore our output layer of model should have 2 neurons.

 

Q4. Number of parameters

Consider the following neural network model :

model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

The number of parameters in this model is:

Choose the correct answer from below:

A.     120

B.     96

C.     108

D.     121

Ans: D

Correct option : 121

Explanation :

Number of nodes in the input layer(i) = 8
Number of nodes in the hidden layer(h) = 12
Number of nodes in the output layer(o) = 1
So,
Number of parameters = (8×12+12×1)+12+1 = 121

 

Q5. Model summary

Complete the following code snippet in order to get a model with the attached model summary.

import tensorflow as tf
model = tf.keras.models.Sequential()

# Create model
model.add(tf.keras.layers.Input(shape=(_a_, )))
model.add(tf.keras.layers._b_( 512 , activation='relu'))
model.add(tf.keras.layers.Dense( _c_, activation='softmax'))

model.summary()

Choose the correct answer from below:

A.     a - 32, b - Dense, c - 10

B.     a - 12, b - Dense, c - 10

C.     a - 10, b - Dense, c - 5

D.     a - Dense(33), b - Dense, c – 50

Ans: Correct Option:
a - 32, b - Dense, c - 10

Explanation:

• The key for getting a is that in the first layer we will have the number of parameters equal to (no. of features in input * neurons in the first layer) + neurons in the first layer, i.e. 32 x 512 + 512 = 16896
• As from the first layer, we got the info from summary as dense. Similarly, for the second layer (i.e. c), we can get the number of neurons from output shape from dense_1.

 

Q6. Logistic regression model

Which of these neural networks would be most appropriately representing a logistic regression model structure for binary classification?

a.

model = Sequential()
model.add(Dense(units=32 input_shape=(2,), activation = ‘relu’))
model.add(Dense(units=64, activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

b.

model = Sequential()
model.add(Dense(units=1, input_shape=(2,), activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

c.

model = Sequential()
model.add(Dense(units=1, input_shape=(2,), activation='sigmoid'))
model.add(Dense(units=1, input_shape=(2,), activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

d.

model = Sequential()
model.add(Dense(units=16))
model.add(Dense(units=32, activation=’relu’))
model.add(Dense(units=64,activation='sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

 

Choose the correct answer from below:

A.     a

B.     b

C.     c

D.     d

Ans: B

Correct Option: b

Explanation:

• Option B would be most appropriate for representing a logistic regression model structure for binary classification. This is because it has a single input layer with only one neuron and a sigmoid activation function. The sigmoid function maps the output to a probability value between 0 and 1, which is ideal for binary classification problems.
  
  
• Option A has two layers, with the second layer using the sigmoid activation function. While this could work for binary classification, the use of the ReLU activation function in the first layer is more commonly used in multi-class classification problems.
  
  
• Option C has two sigmoid layers, which would be more appropriate for a deeper neural network structure for more complex problems.
  
  
• Option D has a similar structure to Option A, with an additional hidden layer. While this could also work for binary classification, the use of ReLU activation in the second layer may make it more suitable for multi-class classification problems.

 

Q7. Model hyperparameters

Complete the following model to get the training output attached to the image.

model.compile(optimizer='sgd',
  loss='sparse_categorical_crossentropy',
  metrics=[‘_a_’])

# train model
model.fit(x=X_train,
          y=y_train,
          epochs = _b_ ,
          validation_data=(X_test, y_test))

Choose the correct answer from below:

A.     a - loss, b - 5

B.     a - accuracy, b - 100

C.     a - loss, b - 25

D.     a - val_acc, b - 100

Ans: B

Correct option:
a - accuracy, b - 100

Explanation:
As the image shows the accuracy, therefore metrics has to be accuracy.
Also in the image, the no. of epochs is showing 100.

 

Q8. Model prediction

We want to use our trained binary classification (trained with binary cross entropy and sigmoid activation function) model 'model', in order to get the label for the first observation in our test dataset of shape (m x n).

Mark the correct option which has the code to meet our requirements.

Note: m represents the number of observations and n represents the number of independent variables.

Choose the correct answer from below:

A.     model.predict(test_data[0])

B.     1 if model.predict(test_data[0].reshape(1,-1)) < 0.5 else 0

C.     model.predict(test_data[0].reshape(1,-1))

D.     1 if model.predict(test_data[0].reshape(1,-1)) > 0.5 else 0

Ans: D

Correct Answer: 1 if model.predict(test_data[0].reshape(1,-1)) > 0.5 else 0

Explanation:

• As the model is trained with sigmoid activation function it’ll give the output with probability between 0 and 1, therefore we need to use the ternary operator.
• Also we need to reshape the test_data[0] otherwise the api will throw an error mentioning reshaping the data if it has single sample.