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GitHub Repository: amanchadha/coursera-deep-learning-specialization
 Path: blob/master/C5 - Sequence Models/Week 1/Jazz improvisation with LSTM/test_utils.py
Views: 4819
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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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from tensorflow.keras.layers import LSTM

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from tensorflow.keras.layers import RepeatVector

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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("Error in test. The lists contain a different number of elements") 

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    for index, a in enumerate(instructor):

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        b = learner[index]

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

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            print(colored(f"Test failed at index {index}", 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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        if (type(layer) == LSTM):

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            descriptors.append(layer.input_shape)

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            descriptors.append(layer.activation.__name__)

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        if (type(layer) == RepeatVector):

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            descriptors.append(layer.n)

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        result.append(descriptors)

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    return result

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