public/subjects/ai/keras/audit/README.md

Exercise 0: Environment and libraries

The exercise is validated if all questions of the exercise are validated

Activate the virtual environment. If you used conda run conda activate your_env

Run python --version

Does it print Python 3.x? x >= 8

Does import jupyter, import numpy, import pandas, and import keras run without any error?

---

---

Exercise 1: Sequential

For question 1, does the output end with keras.engine.sequential.Sequential object at xxx?

---

---

Exercise 2: Dense

The exercise is validated if all questions of the exercise are validated

For question 1, do the fields batch_input_shape, units and activation match this output?

{'name': 'dense_7',
'trainable': True,
'batch_input_shape': (None, 5),
'dtype': 'float32',
'units': 8,
'activation': 'sigmoid',
'use_bias': True,
'kernel_initializer': {'class_name': 'GlorotUniform',
'config': {'seed': None}},
'bias_initializer': {'class_name': 'Zeros', 'config': {}},
'kernel_regularizer': None,
'bias_regularizer': None,
'activity_regularizer': None,
'kernel_constraint': None,
'bias_constraint': None}

For question 2, do the fields units and activation match this output?

{'name': 'dense_8',
'trainable': True,
'dtype': 'float32',
'units': 4,
'activation': 'sigmoid',
'use_bias': True,
'kernel_initializer': {'class_name': 'GlorotUniform',
'config': {'seed': None}},
'bias_initializer': {'class_name': 'Zeros', 'config': {}},
'kernel_regularizer': None,
'bias_regularizer': None,
'activity_regularizer': None,
'kernel_constraint': None,
'bias_constraint': None}

For question 3, do the fields units and activation match this output?

{'name': 'dense_9',
'trainable': True,
'dtype': 'float32',
'units': 1,
'activation': 'sigmoid',
'use_bias': True,
'kernel_initializer': {'class_name': 'GlorotUniform',
'config': {'seed': None}},
'bias_initializer': {'class_name': 'Zeros', 'config': {}},
'kernel_regularizer': None,
'bias_regularizer': None,
'activity_regularizer': None,
'kernel_constraint': None,
'bias_constraint': None}

---

---

Exercise 3: Architecture

For question 1, is code that creates the neural network the following?

model = keras.Sequential()
model.add(Dense(8, input_shape=(5,), activation= 'sigmoid'))
model.add(Dense(4, activation= 'sigmoid'))
model.add(Dense(1, activation= 'linear'))

The first two layers could use another activation function that sigmoid (eg: relu)

---

---

Exercise 4: Optimize

For question 1, does the output of model.get_config()['layers'] match the fields batch_input_shape, units and activation?

[{'class_name': 'InputLayer',
  'config': {'batch_input_shape': (None, 30),
   'dtype': 'float32',
   'sparse': False,
   'ragged': False,
   'name': 'dense_134_input'}},
 {'class_name': 'Dense',
  'config': {'name': 'dense_134',
   'trainable': True,
   'batch_input_shape': (None, 30),
   'dtype': 'float32',
   'units': 10,
   'activation': 'sigmoid',
   'use_bias': True,
   'kernel_initializer': {'class_name': 'GlorotUniform',
    'config': {'seed': None}},
   'bias_initializer': {'class_name': 'Zeros', 'config': {}},
   'kernel_regularizer': None,
   'bias_regularizer': None,
   'activity_regularizer': None,
   'kernel_constraint': None,
   'bias_constraint': None}},
 {'class_name': 'Dense',
  'config': {'name': 'dense_135',
   'trainable': True,
   'dtype': 'float32',
   'units': 5,
   'activation': 'sigmoid',
   'use_bias': True,
   'kernel_initializer': {'class_name': 'GlorotUniform',
    'config': {'seed': None}},
   'bias_initializer': {'class_name': 'Zeros', 'config': {}},
   'kernel_regularizer': None,
   'bias_regularizer': None,
   'activity_regularizer': None,
   'kernel_constraint': None,
   'bias_constraint': None}},
 {'class_name': 'Dense',
  'config': {'name': 'dense_136',
   'trainable': True,
   'dtype': 'float32',
   'units': 1,
   'activation': 'sigmoid',
   'use_bias': True,
   'kernel_initializer': {'class_name': 'GlorotUniform',
    'config': {'seed': None}},
   'bias_initializer': {'class_name': 'Zeros', 'config': {}},
   'kernel_regularizer': None,
   'bias_regularizer': None,
   'activity_regularizer': None,
   'kernel_constraint': None,
   'bias_constraint': None}}]

You should notice that the neural network is struggling to learn. By luck the initialization of the weights might have led to an accuracy close of 90%. But when I trained the neural network, with batch_size=300 on the data here is the output of the last epoch (50):

Epoch 50/50 2/2 [==============================] - 0s 1ms/step - loss: 0.6559 - accuracy: 0.6274

For question 2, is the the accuracy at epoch 50 higher than 95%?