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GitHub Repository: amanchadha/coursera-deep-learning-specialization
 Path: blob/master/C5 - Sequence Models/Week 1/Building a Recurrent Neural Network - Step by Step/generateTestCases.py
Views: 4819
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# New Generate Test Cases 

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from solutions import *

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import numpy as np 

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import math 

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import os,sys

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# import tensorflow as tf

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sys.path.append('../')

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sys.path.append('../../')

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from grader_support import stdout_redirector

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from grader_support import util

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mFiles = [

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    "rnn_cell_forward.py",

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    "rnn_forward.py",

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    "lstm_cell_forward.py",

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    "lstm_forward.py"

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]

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# generating test cases for rnn_cell_forward 

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xt = np.random.randn(3,6)

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a_prev = np.random.randn(4,6)

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Waa = np.random.randn(4,4)

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Wax = np.random.randn(4,3)

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Wya = np.random.randn(2,4)

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ba = np.random.randn(4,1)

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by = np.random.randn(2,1)

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parameters = {"Waa": Waa, "Wax": Wax, "Wya": Wya, "ba": ba, "by": by}

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a_next, yt_pred, cache = rnn_cell_forward(xt, a_prev, parameters)

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# -------------------------------------------------------

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# generating test cases for rnn_forward

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x1 = np.random.randn(3,6,4)

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a01 = np.random.randn(4,6)

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parameters1 = {"Waa": Waa, "Wax": Wax, "Wya": Wya, "ba": ba, "by": by}

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a, y_pred, caches = rnn_forward(x1, a01, parameters1)

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# -------------------------------------------------------

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# generate test cases for lstm_cell_forward

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xt1 = np.random.randn(3,4)

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a_prev1 = np.random.randn(5,4)

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c_prev1 = np.random.randn(5,4)

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Wf = np.random.randn(5, 5+3)

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bf = np.random.randn(5,1)

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Wi = np.random.randn(5, 5+3)

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bi = np.random.randn(5,1)

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Wo = np.random.randn(5, 5+3)

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bo = np.random.randn(5,1)

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Wc = np.random.randn(5, 5+3)

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bc = np.random.randn(5,1)

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Wy = np.random.randn(2,5)

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by = np.random.randn(2,1)

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parameters2 = {"Wf": Wf, "Wi": Wi, "Wo": Wo, "Wc": Wc, "Wy": Wy, "bf": bf, "bi": bi, "bo": bo, "bc": bc, "by": by}

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a_next_lstm, c_next_lstm, yt_lstm, cache_lstm = lstm_cell_forward(xt1, a_prev1, c_prev1, parameters2)

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# -------------------------------------------------------

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# generate test cases for lstm_cell_forward

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# lstm_forward

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np.random.seed(1)

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x2 = np.random.randn(3,10,4)

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a02 = np.random.randn(5,10)

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Wf = np.random.randn(5, 5+3)

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bf = np.random.randn(5,1)

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Wi = np.random.randn(5, 5+3)

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bi = np.random.randn(5,1)

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Wo = np.random.randn(5, 5+3)

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bo = np.random.randn(5,1)

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Wc = np.random.randn(5, 5+3)

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bc = np.random.randn(5,1)

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Wy = np.random.randn(2,5)

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by = np.random.randn(2,1)

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parameters3 = {"Wf": Wf, "Wi": Wi, "Wo": Wo, "Wc": Wc, "Wy": Wy, "bf": bf, "bi": bi, "bo": bo, "bc": bc, "by": by}

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af, yf, cf, cachesf = lstm_forward(x2, a02, parameters3)

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def generateTestCases():

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	testCases = {

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	    'rnn_cell_forward': { 

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	        'partId': 'KrqbT',

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	        'testCases': [

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	            {

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	                'testInput': (xt, a_prev, parameters),

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	                'testOutput': (a_next, yt_pred, cache)

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	            }

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	        ]

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	    },

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	    'rnn_forward': { 

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	        'partId': 'CzGAI',

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	        'testCases': [

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	            {

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	                'testInput': (x1, a01, parameters1),

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	                'testOutput': (a, y_pred, caches)

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	            }

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	        ]

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	    },

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	    'lstm_cell_forward': { 

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	        'partId': '7tvdt',

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	        'testCases': [

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	            {

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	                'testInput': (xt1, a_prev1, c_prev1, parameters2),

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	                'testOutput': (a_next_lstm, c_next_lstm, yt_lstm, cache_lstm)

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	            }

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	        ]

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	    },

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	    'lstm_forward': { 

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	        'partId': 'SAQvR',

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	        'testCases': [

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	            {

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	                'testInput': (x2, a02, parameters3),

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	                'testOutput': (af, yf, cf, cachesf)

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	            }

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	        ]

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	    }

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	}

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	return testCases

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