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GitHub Repository: amanchadha/coursera-deep-learning-specialization
 Path: blob/master/C4 - Convolutional Neural Networks/Week 2/ResNets/test_utils.py
Views: 4818
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from termcolor import colored

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from tensorflow.keras.layers import Input

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from tensorflow.keras.layers import Conv2D

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from tensorflow.keras.layers import MaxPooling2D

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from tensorflow.keras.layers import Dropout 

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from tensorflow.keras.layers import Conv2DTranspose

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from tensorflow.keras.layers import concatenate

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from tensorflow.keras.layers import ZeroPadding2D

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from tensorflow.keras.layers import Dense

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# Compare the two inputs

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def comparator(learner, instructor):

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    if len(learner) != len(instructor):

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        raise AssertionError(f'Models does not have the same number of layers {len(learner)} != {len(instructor)}')

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

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

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

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

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

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

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            raise AssertionError("Error in test") 

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

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# extracts the description of a given model

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def summary(model):

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    model.compile(optimizer='adam',

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                  loss='categorical_crossentropy',

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                  metrics=['accuracy'])

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    result = []

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    for layer in model.layers:

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

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        if (type(layer) == Conv2D):

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            descriptors.append(layer.padding)

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            descriptors.append(layer.activation.__name__)

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

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        if (type(layer) == MaxPooling2D):

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            descriptors.append(layer.pool_size)

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            descriptors.append(layer.strides)

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            descriptors.append(layer.padding)

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        if (type(layer) == Dropout):

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            descriptors.append(layer.rate)

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        if (type(layer) == ZeroPadding2D):

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            descriptors.append(layer.padding)

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        if (type(layer) == Dense):

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            descriptors.append(layer.activation.__name__)

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        result.append(descriptors)

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    return result

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