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GitHub Repository: y33-j3T/Coursera-Deep-Learning
 Path: blob/master/Custom Models, Layers, and Loss Functions with TensorFlow/Week 3 - Custom Layers/utils.py
Views: 13373
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import tensorflow as tf

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from tensorflow.python.ops.resource_variable_ops import ResourceVariable

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import numpy as np

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def test_loop(test_cases):

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    success = 0

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    fails = 0

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    for test_case in test_cases:

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        try:

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            assert test_case["result"] == test_case["expected"]

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            success += 1

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        except:

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            fails += 1

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            print(f'{test_case["name"]}: {test_case["error_message"]}\nExpected: {test_case["expected"]}\nResult: {test_case["result"]}\n')

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    if fails == 0:

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        print("\033[92m All public tests passed")

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    else:

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        print('\033[92m', success," Tests passed")

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        print('\033[91m', fails, " Tests failed")

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        raise Exception(test_case["error_message"])

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def test_simple_quadratic(SimpleQuadratic):

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    expected_units = 128

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    expected_activation_function = tf.keras.activations.relu

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    expected_activation_string = 'relu'

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    shape_0 = 8

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    shape_1 = 2

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    test_layer = SimpleQuadratic(units=expected_units, activation=expected_activation_string)

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    test_layer.build((shape_0, shape_1))

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    test_inputs = tf.random.uniform((shape_0, shape_1))

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    test_call_value = test_layer.call(test_inputs)

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    a_type = type(test_layer.a)

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    b_type = type(test_layer.b)

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    c_type = type(test_layer.c)

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    test_layer_forced_weights = SimpleQuadratic(units=1, activation=None)

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    test_layer_forced_weights.a = tf.constant([2.0], dtype='float32', shape=(1,1))

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    test_layer_forced_weights.b = tf.constant([2.0], dtype='float32', shape=(1,1))

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    test_layer_forced_weights.c = tf.constant([2.0], dtype='float32', shape=(1,1))

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    test_layer_forced_weights_inputs = tf.constant([4.0], dtype='float32', shape=(1,1))

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    test_layer_forced_weights_expected_output = 42.0

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    test_cases = [

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        {

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            "name": "units_check",

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            "result": test_layer.units,

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            "expected": expected_units,

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            "error_message": f'Incorrect number of units.'

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        },

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        {

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            "name": "activations_check",

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            "result": test_layer.activation,

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            "expected": tf.keras.activations.relu,

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            "error_message": "Got different activation function."

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        },

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        {

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            "name": "a_type_check",

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            "result": a_type,

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            "expected": ResourceVariable,

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            "error_message": f'State variable a is of different type. Expected ResourceVariable but got {a_type}'

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        },

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        {

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            "name": "b_type_check",

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            "result": b_type,

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            "expected": ResourceVariable,

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            "error_message": f'State variable b is of different type. Expected ResourceVariable but got {b_type}'

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        },

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        {

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            "name": "c_type_check",

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            "result": c_type,

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            "expected": ResourceVariable,

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            "error_message": f'State variable c is of different type. Expected ResourceVariable but got {c_type}'

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        },

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        {

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            "name": "a_initializer_check",

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            "result": test_layer.a.numpy().sum() != 0,

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            "expected": True,

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            "error_message": f'State variable a is not initialized randomly. Please check initializer used.'

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        },

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        {

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            "name": "b_initializer_check",

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            "result": test_layer.b.numpy().sum() != 0,

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            "expected": True,

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            "error_message": f'State variable b is not initialized randomly. Please check initializer used.'

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        },

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        {

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            "name": "c_initializer_check",

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            "result": test_layer.c.numpy().sum() == 0,

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            "expected": True,

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            "error_message": f'State variable c is not initialized to zeroes. Please check initializer used.'

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        },

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        {

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            "name": "output_check",

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            "result": test_layer_forced_weights.call(test_layer_forced_weights_inputs).numpy()[0][0],

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            "expected": test_layer_forced_weights_expected_output,

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            "error_message": f'Expected output is incorrect. Please check operations in the call() method.'

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        }

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    ]

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    test_loop(test_cases)

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