CoCalc Logo Icon
StoreFeaturesDocsShareSupportNewsAboutSign UpSign In
amanchadha

Real-time collaboration for Jupyter Notebooks, Linux Terminals, LaTeX, VS Code, R IDE, and more,
all in one place. Commercial Alternative to JupyterHub.

GitHub Repository: amanchadha/coursera-deep-learning-specialization
 Path: blob/master/C5 - Sequence Models/Week 2/Emojify/test_utils.py
Views: 4819
1
from termcolor import colored

2


3
from tensorflow.keras.layers import Input

4
from tensorflow.keras.layers import Conv2D

5
from tensorflow.keras.layers import MaxPooling2D

6
from tensorflow.keras.layers import Dropout 

7
from tensorflow.keras.layers import Conv2DTranspose

8
from tensorflow.keras.layers import concatenate

9
from tensorflow.keras.layers import ZeroPadding2D

10
from tensorflow.keras.layers import Dense

11
from tensorflow.keras.layers import LSTM

12
from tensorflow.keras.layers import RepeatVector

13


14


15
# Compare the two inputs

16
def comparator(learner, instructor):

17
    for a, b in zip(learner, instructor):

18
        if tuple(a) != tuple(b):

19
            print(colored("Test failed", attrs=['bold']),

20
                  "\n Expected value \n\n", colored(f"{b}", "green"), 

21
                  "\n\n does not match the input value: \n\n", 

22
                  colored(f"{a}", "red"))

23
            raise AssertionError("Error in test") 

24
    print(colored("All tests passed!", "green"))

25


26
# extracts the description of a given model

27
def summary(model):

28
    model.compile(optimizer='adam',

29
                  loss='categorical_crossentropy',

30
                  metrics=['accuracy'])

31
    result = []

32
    for layer in model.layers:

33
        descriptors = [layer.__class__.__name__, layer.output_shape, layer.count_params()]

34
        if (type(layer) == Conv2D):

35
            descriptors.append(layer.padding)

36
            descriptors.append(layer.activation.__name__)

37
            descriptors.append(layer.kernel_initializer.__class__.__name__)

38
        if (type(layer) == MaxPooling2D):

39
            descriptors.append(layer.pool_size)

40
            descriptors.append(layer.strides)

41
            descriptors.append(layer.padding)

42
        if (type(layer) == Dropout):

43
            descriptors.append(layer.rate)

44
        if (type(layer) == ZeroPadding2D):

45
            descriptors.append(layer.padding)

46
        if (type(layer) == Dense):

47
            descriptors.append(layer.activation.__name__)

48
        if (type(layer) == LSTM):

49
            descriptors.append(layer.input_shape)

50
            descriptors.append(layer.activation.__name__)

51
            descriptors.append(layer.return_sequences)

52
        if (type(layer) == RepeatVector):

53
            descriptors.append(layer.n)

54
        result.append(descriptors)

55
    return result

56


57