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GitHub Repository: amanchadha/coursera-deep-learning-specialization
 Path: blob/master/C5 - Sequence Models/Week 2/Emojify/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 copy 

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# from keras.callbacks import History 

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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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12
from grader_support import stdout_redirector

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from grader_support import util

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os.environ['TF_CPP_MIN_LOG_LEVEL']='3'

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# This grader is for the Emojify assignment

18


19
mFiles = [

20
    "sentence_to_avg.py",

21
    "model.py",

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    "sentences_to_indices.py",

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    "pretrained_embedding_layer.py",

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    "Emojify_V2.py"

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]

26


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np.random.seed(3)

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29


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# generating test cases for sentence_to_avg

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X = np.asarray(['I am going to the bar tonight', 'I love you', 'miss you my dear',

33
 'Lets go party and drinks','Congrats on the new job','Congratulations',

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 'I am so happy for you', 'Why are you feeling bad', 'What is wrong with you',

35
 'You totally deserve this prize', 'Let us go play football',

36
 'Are you down for football this afternoon', 'Work hard play harder',

37
 'It is suprising how people can be dumb sometimes',

38
 'I am very disappointed','It is the best day in my life',

39
 'I think I will end up alone','My life is so boring','Good job',

40
 'Great so awesome'])

41


42
Y = np.asarray([4, 0, 0, 3, 4, 4, 4, 3, 2, 4, 1, 1, 2, 3, 2, 1, 1, 3, 4, 4])

43


44
diw = {69: 'i', 1: 'am', 2: 'going', 3: 'to', 4: 'the', 5: 'bar', 6: 'tonight', 7: 'love', 8: 'you', 9: 'miss', 10: 'my', 11: 'dear', 12: 'lets', 13: 'go', 14: 'party', 15: 'and', 16: 'drinks', 17: 'congrats', 18: 'on', 19: 'new', 20: 'job', 21: 'congratulations', 22: 'so', 23: 'happy', 24: 'for', 25: 'why', 26: 'are', 27: 'feeling', 28: 'bad', 29: 'what', 30: 'is', 31: 'wrong', 32: 'with', 33: 'totally', 34: 'deserve', 35: 'this', 36: 'prize', 37: 'let', 38: 'us', 39: 'play', 40: 'football', 41: 'down', 42: 'afternoon', 43: 'work', 44: 'hard', 45: 'harder', 46: 'it', 47: 'suprising', 48: 'how', 49: 'people', 50: 'can', 51: 'be', 52: 'dumb', 53: 'sometimes', 54: 'very', 55: 'disappointed', 56: 'best', 57: 'day', 58: 'in', 59: 'life', 60: 'think', 61: 'will', 62: 'end', 63: 'up', 64: 'alone', 65: 'boring', 66: 'good', 67: 'great', 68: 'awesome'}

45
# dwi = {'i': 185457, 'am': 52943, 'going': 163745, 'to': 360915, 'the': 357266, 'bar': 68431, 'tonight': 361859, 'love': 226278, 'you': 394475, 'miss': 246253, 'my': 254258, 'dear': 118309, 'lets': 220930, 'go': 163237, 'party': 278093, 'and': 54718, 'drinks': 129414, 'congrats': 107277, 'on': 269798, 'new': 259972, 'job': 198213, 'congratulations': 107283, 'so': 336114, 'happy': 173081, 'for': 151349, 'why': 386984, 'are': 58997, 'feeling': 146352, 'bad': 65963, 'what': 386307, 'is': 192973, 'wrong': 390470, 'with': 388711, 'totally': 362776, 'deserve': 121794, 'this': 358160, 'prize': 292631, 'let': 220870, 'us': 374021, 'play': 286375, 'football': 151266, 'down': 128527, 'afternoon': 47835, 'work': 389836, 'hard': 173236, 'harder': 173320, 'it': 193716, 'suprising': 348287, 'how': 182540, 'people': 280944, 'can': 90548, 'be': 71090, 'dumb': 130637, 'sometimes': 337305, 'very': 377946, 'disappointed': 124906, 'best': 74390, 'day': 117874, 'in': 188481, 'life': 222138, 'think': 357970, 'will': 387696, 'end': 136979, 'up': 373317, 'alone': 52315, 'boring': 81044, 'good': 164328, 'great': 166369, 'awesome': 64354}

46
dwi = {'happy': 23, 'to': 3, 'harder': 45, 'feeling': 27, 'alone': 64, 'think': 60, 'bad': 28, 'i': 69, 'dear': 11, 'it': 46, 'down': 41, 'be': 51, 'work': 43, 'great': 67, 'play': 39, 'this': 35, 'the': 4, 'love': 7, 'job': 20, 'sometimes': 53, 'and': 15, 'day': 57, 'best': 56, 'bar': 5, 'disappointed': 55, 'on': 18, 'congratulations': 21, 'let': 37, 'awesome': 68, 'can': 50, 'good': 66, 'new': 19, 'life': 59, 'us': 38, 'what': 29, 'up': 63, 'going': 2, 'will': 61, 'miss': 9, 'totally': 33, 'with': 32, 'very': 54, 'so': 22, 'my': 10, 'tonight': 6, 'are': 26, 'am': 1, 'suprising': 47, 'deserve': 34, 'lets': 12, 'football': 40, 'is': 30, 'go': 13, 'drinks': 16, 'congrats': 17, 'you': 8, 'party': 14, 'people': 49, 'prize': 36, 'in': 58, 'hard': 44, 'why': 25, 'for': 24, 'end': 62, 'afternoon': 42, 'how': 48, 'wrong': 31, 'dumb': 52, 'boring': 65}

47


48
d = {'i': np.asarray([  1.18910000e-01,   1.52550000e-01,  -8.20730000e-02,

49
        -7.41440000e-01,   7.59170000e-01,  -4.83280000e-01,

50
        -3.10090000e-01,   5.14760000e-01,  -9.87080000e-01,

51
         6.17570000e-04,  -1.50430000e-01,   8.37700000e-01,

52
        -1.07970000e+00,  -5.14600000e-01,   1.31880000e+00,

53
         6.20070000e-01,   1.37790000e-01,   4.71080000e-01,

54
        -7.28740000e-02,  -7.26750000e-01,  -7.41160000e-01,

55
         7.52630000e-01,   8.81800000e-01,   2.95610000e-01,

56
         1.35480000e+00,  -2.57010000e+00,  -1.35230000e+00,

57
         4.58800000e-01,   1.00680000e+00,  -1.18560000e+00,

58
         3.47370000e+00,   7.78980000e-01,  -7.29290000e-01,

59
         2.51020000e-01,  -2.61560000e-01,  -3.46840000e-01,

60
         5.58410000e-01,   7.50980000e-01,   4.98300000e-01,

61
        -2.68230000e-01,  -2.74430000e-03,  -1.82980000e-02,

62
        -2.80960000e-01,   5.53180000e-01,   3.77060000e-02,

63
         1.85550000e-01,  -1.50250000e-01,  -5.75120000e-01,

64
        -2.66710000e-01,   9.21210000e-01]), 'am': np.asarray([ 0.34664 ,  0.39805 ,  0.4897  , -0.51421 ,  0.54574 , -1.2005  ,

65
        0.32107 ,  0.74004 , -1.4979  , -0.19651 , -0.12631 , -0.37703 ,

66
       -0.62569 ,  0.038792,  1.0579  ,  0.77199 , -0.18589 ,  1.3032  ,

67
       -0.72128 ,  0.40231 ,  0.066442,  1.2315  ,  0.93956 ,  1.3903  ,

68
        1.5334  , -1.473   , -0.34997 ,  0.31562 ,  0.90691 ,  0.45498 ,

69
        2.5481  ,  0.1641  , -0.607   ,  0.27061 , -0.79072 , -1.146   ,

70
        0.91795 , -0.11797 ,  0.23526 , -0.12659 ,  0.66527 , -0.91816 ,

71
        0.10048 ,  0.70457 , -0.21777 ,  0.52479 , -0.54452 ,  0.086576,

72
        0.34037 ,  1.3588  ]), 'going': np.asarray([  1.42120000e-02,  -1.80110000e-01,   2.34780000e-01,

73
        -2.50380000e-01,   2.68160000e-01,  -5.48870000e-01,

74
        -7.44360000e-01,   5.20340000e-01,  -2.85710000e-01,

75
         1.53760000e-01,  -5.59160000e-01,   1.04230000e-01,

76
        -1.01790000e+00,   1.06340000e-02,   9.52800000e-01,

77
         5.18110000e-01,   7.86240000e-01,  -2.00990000e-01,

78
        -2.88160000e-01,  -6.57350000e-01,   1.83070000e-02,

79
         3.19300000e-01,   3.44560000e-01,   2.65160000e-01,

80
         7.20610000e-01,  -1.86990000e+00,  -2.53840000e-01,

81
        -3.00760000e-03,   1.22030000e+00,  -9.20190000e-01,

82
         3.40570000e+00,   1.13510000e+00,  -5.84060000e-01,

83
        -1.26110000e-01,  -3.50170000e-02,  -2.99410000e-01,

84
         3.14750000e-02,   3.38830000e-01,   3.89100000e-01,

85
        -4.29990000e-01,  -8.52640000e-01,  -3.17510000e-01,

86
        -8.91630000e-02,   5.31760000e-01,   4.37730000e-02,

87
         6.09980000e-02,   2.47620000e-01,  -1.66790000e-01,

88
         6.15950000e-02,   6.99270000e-01]), 'to': np.asarray([ 0.68047 , -0.039263,  0.30186 , -0.17792 ,  0.42962 ,  0.032246,

89
       -0.41376 ,  0.13228 , -0.29847 , -0.085253,  0.17118 ,  0.22419 ,

90
       -0.10046 , -0.43653 ,  0.33418 ,  0.67846 ,  0.057204, -0.34448 ,

91
       -0.42785 , -0.43275 ,  0.55963 ,  0.10032 ,  0.18677 , -0.26854 ,

92
        0.037334, -2.0932  ,  0.22171 , -0.39868 ,  0.20912 , -0.55725 ,

93
        3.8826  ,  0.47466 , -0.95658 , -0.37788 ,  0.20869 , -0.32752 ,

94
        0.12751 ,  0.088359,  0.16351 , -0.21634 , -0.094375,  0.018324,

95
        0.21048 , -0.03088 , -0.19722 ,  0.082279, -0.09434 , -0.073297,

96
       -0.064699, -0.26044 ]), 'the': np.asarray([  4.18000000e-01,   2.49680000e-01,  -4.12420000e-01,

97
         1.21700000e-01,   3.45270000e-01,  -4.44570000e-02,

98
        -4.96880000e-01,  -1.78620000e-01,  -6.60230000e-04,

99
        -6.56600000e-01,   2.78430000e-01,  -1.47670000e-01,

100
        -5.56770000e-01,   1.46580000e-01,  -9.50950000e-03,

101
         1.16580000e-02,   1.02040000e-01,  -1.27920000e-01,

102
        -8.44300000e-01,  -1.21810000e-01,  -1.68010000e-02,

103
        -3.32790000e-01,  -1.55200000e-01,  -2.31310000e-01,

104
        -1.91810000e-01,  -1.88230000e+00,  -7.67460000e-01,

105
         9.90510000e-02,  -4.21250000e-01,  -1.95260000e-01,

106
         4.00710000e+00,  -1.85940000e-01,  -5.22870000e-01,

107
        -3.16810000e-01,   5.92130000e-04,   7.44490000e-03,

108
         1.77780000e-01,  -1.58970000e-01,   1.20410000e-02,

109
        -5.42230000e-02,  -2.98710000e-01,  -1.57490000e-01,

110
        -3.47580000e-01,  -4.56370000e-02,  -4.42510000e-01,

111
         1.87850000e-01,   2.78490000e-03,  -1.84110000e-01,

112
        -1.15140000e-01,  -7.85810000e-01]), 'bar': np.asarray([ -9.45310000e-01,   3.96860000e-01,  -8.06050000e-01,

113
        -3.02150000e-01,   2.77360000e-01,  -1.00190000e-01,

114
        -4.05000000e-01,  -1.00950000e-01,  -6.59340000e-02,

115
        -4.72580000e-02,  -2.08280000e-01,  -2.57210000e-01,

116
         6.87500000e-02,   9.37510000e-01,  -8.14830000e-02,

117
         1.34600000e-01,   2.73020000e-02,  -1.80960000e-01,

118
        -3.56380000e-01,  -8.81040000e-01,   1.19510000e+00,

119
         5.55560000e-02,  -3.17410000e-01,   1.02440000e+00,

120
        -8.47680000e-01,  -1.59590000e+00,   2.16570000e-02,

121
         4.36280000e-01,   8.83880000e-04,  -4.18200000e-01,

122
         2.12470000e+00,  -4.33320000e-01,  -1.08160000e+00,

123
         3.36160000e-01,   3.33990000e-01,  -2.00640000e-01,

124
         5.86330000e-01,   9.01860000e-02,   7.50540000e-01,

125
         4.85000000e-01,   1.73700000e-01,   6.81290000e-01,

126
        -1.68100000e-01,   6.12650000e-01,   7.68750000e-02,

127
        -1.97970000e-01,  -9.95550000e-02,  -1.02310000e+00,

128
         9.53940000e-01,  -6.35000000e-02]), 'tonight': np.asarray([-0.24077  ,  0.64182  , -0.24113  ,  0.75178  ,  0.53864  ,

129
       -1.1493   , -0.61891  ,  0.9137   , -0.53606  ,  0.17756  ,

130
       -0.76413  ,  0.016193 , -0.51358  ,  0.93094  ,  1.2053   ,

131
        0.4189   ,  0.16474  , -0.13126  , -1.0023   ,  0.007583 ,

132
        0.0035247,  0.52336  ,  0.68604  ,  0.35461  ,  0.54968  ,

133
       -0.59051  , -0.26113  ,  0.61433  ,  0.7793   , -1.1467   ,

134
        2.0951   ,  1.39     , -0.30046  , -0.18278  , -0.45401  ,

135
       -0.13385  ,  0.90233  , -0.28826  ,  0.030881 , -0.63533  ,

136
       -0.50969  , -0.15814  , -1.1477   , -0.16897  , -0.42309  ,

137
        0.67139  ,  0.0269   , -0.026077 , -0.3064   ,  1.6869   ]), 'love': np.asarray([-0.13886  ,  1.1401   , -0.85212  , -0.29212  ,  0.75534  ,

138
        0.82762  , -0.3181   ,  0.0072204, -0.34762  ,  1.0731   ,

139
       -0.24665  ,  0.97765  , -0.55835  , -0.090318 ,  0.83182  ,

140
       -0.33317  ,  0.22648  ,  0.30913  ,  0.026929 , -0.086739 ,

141
       -0.14703  ,  1.3543   ,  0.53695  ,  0.43735  ,  1.2749   ,

142
       -1.4382   , -1.2815   , -0.15196  ,  1.0506   , -0.93644  ,

143
        2.7561   ,  0.58967  , -0.29473  ,  0.27574  , -0.32928  ,

144
       -0.201    , -0.28547  , -0.45987  , -0.14603  , -0.69372  ,

145
        0.070761 , -0.19326  , -0.1855   , -0.16095  ,  0.24268  ,

146
        0.20784  ,  0.030924 , -1.3711   , -0.28606  ,  0.2898   ]), 'you': np.asarray([ -1.09190000e-03,   3.33240000e-01,   3.57430000e-01,

147
        -5.40410000e-01,   8.20320000e-01,  -4.93910000e-01,

148
        -3.25880000e-01,   1.99720000e-03,  -2.38290000e-01,

149
         3.55540000e-01,  -6.06550000e-01,   9.89320000e-01,

150
        -2.17860000e-01,   1.12360000e-01,   1.14940000e+00,

151
         7.32840000e-01,   5.11820000e-01,   2.92870000e-01,

152
         2.83880000e-01,  -1.35900000e+00,  -3.79510000e-01,

153
         5.09430000e-01,   7.07100000e-01,   6.29410000e-01,

154
         1.05340000e+00,  -2.17560000e+00,  -1.32040000e+00,

155
         4.00010000e-01,   1.57410000e+00,  -1.66000000e+00,

156
         3.77210000e+00,   8.69490000e-01,  -8.04390000e-01,

157
         1.83900000e-01,  -3.43320000e-01,   1.07140000e-02,

158
         2.39690000e-01,   6.67480000e-02,   7.01170000e-01,

159
        -7.37020000e-01,   2.08770000e-01,   1.15640000e-01,

160
        -1.51900000e-01,   8.59080000e-01,   2.26200000e-01,

161
         1.65190000e-01,   3.63090000e-01,  -4.56970000e-01,

162
        -4.89690000e-02,   1.13160000e+00]), 'miss': np.asarray([ -3.22730000e-01,   5.61820000e-01,  -6.63630000e-01,

163
         3.88830000e-01,  -4.65580000e-02,   2.23280000e-01,

164
        -7.56910000e-01,   7.08530000e-01,   5.57140000e-01,

165
        -5.99960000e-02,   3.12350000e-01,   1.67410000e-01,

166
        -5.45680000e-01,  -3.87650000e-01,   1.23090000e+00,

167
         3.47660000e-01,  -5.00170000e-02,  -4.98040000e-02,

168
        -6.62820000e-01,   2.28540000e-01,  -7.84430000e-01,

169
         6.58230000e-01,   5.60990000e-01,   3.32180000e-01,

170
         5.30490000e-01,  -1.36110000e+00,  -4.94520000e-01,

171
         2.77110000e-01,  -2.29820000e-01,  -1.14920000e+00,

172
         1.50280000e+00,   1.09160000e+00,  -9.84640000e-02,

173
         3.93490000e-04,   2.57530000e-01,  -1.54700000e-01,

174
         2.75950000e-01,   6.47500000e-01,  -5.65370000e-02,

175
        -1.30460000e+00,  -5.82000000e-01,   1.28380000e-01,

176
        -1.14160000e-01,  -8.08360000e-01,  -8.39210000e-01,

177
         2.56090000e-01,   1.56290000e-01,  -9.72990000e-01,

178
         1.11300000e-01,   4.45000000e-01]), 'my': np.asarray([-0.27279  ,  0.77515  , -0.10181  , -0.9166   ,  0.90477  ,

179
       -0.070501 , -0.47569  ,  0.44608  ,  0.1697   ,  0.072352 ,

180
       -0.16306  ,  0.86852  , -0.76634  , -0.016103 ,  0.78492  ,

181
        0.2952   , -0.74859  ,  0.2099   ,  0.65537  , -0.62334  ,

182
       -0.43711  ,  1.1854   ,  0.47519  ,  0.0093866,  1.1377   ,

183
       -2.4394   , -1.5619   ,  0.49001  ,  1.0985   , -0.97371  ,

184
        3.4628   ,  1.0408   , -0.65138  ,  0.57189  , -0.12523  ,

185
        0.26705  ,  0.16373  ,  0.41105  ,  0.7509   , -0.77923  ,

186
        0.03638  , -0.28609  , -0.72365  ,  0.63511  ,  0.089441 ,

187
       -0.30133  ,  0.36518  , -0.73367  ,  0.040383 ,  0.26657  ]), 'dear': np.asarray([-0.29946 ,  1.172   ,  0.3289  , -0.74413 ,  1.0811  , -0.1525  ,

188
       -0.19838 ,  0.052137, -0.34856 , -0.20578 , -0.70299 ,  1.4999  ,

189
        0.23187 , -0.22932 ,  0.45783 , -0.057641, -0.42777 ,  0.35264 ,

190
        1.0389  ,  0.59088 , -0.49418 ,  1.0761  ,  0.58225 ,  0.5137  ,

191
        1.2708  , -1.0246  , -1.0251  , -0.25016 ,  0.27673 , -0.39602 ,

192
        0.95889 ,  0.43321 , -0.098083,  0.54379 , -0.39457 , -0.27019 ,

193
        0.15541 , -0.94886 ,  0.64448 , -0.75184 ,  0.24743 , -0.17944 ,

194
       -0.16587 , -0.045956,  0.47237 ,  0.69745 , -0.43758 , -0.27562 ,

195
        0.49816 ,  0.83257 ]), 'lets': np.asarray([ 0.30423 , -0.24405 ,  1.0303  ,  0.039387,  0.23262 , -0.39686 ,

196
       -0.36583 , -0.18573 ,  0.31051 ,  0.48486 , -0.035048,  0.76138 ,

197
        0.42608 ,  0.58651 ,  0.35601 ,  0.73256 , -0.092297, -0.1974  ,

198
        0.7983  , -1.1569  ,  0.43523 ,  0.2619  , -0.45631 ,  1.1645  ,

199
        0.7541  , -0.89915 ,  0.068323, -0.40341 ,  0.53135 , -1.1687  ,

200
        1.8254  ,  0.40052 , -0.76422 , -0.076685, -0.26669 ,  0.2565  ,

201
       -0.048207, -0.46385 , -0.037947, -0.94777 ,  0.9061  ,  0.28996 ,

202
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397
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398
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399
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401
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402
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403
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404
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405
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406
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407
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408
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410
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411
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412
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414
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415
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416
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417
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419
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420
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421
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422
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423
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424
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426
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427
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428
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429
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430
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431
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432
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433
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434
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438
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439
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440
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441
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442
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443
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445
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446
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447
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448
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449
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450
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451
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452
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454
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455
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457
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458
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459
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460
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461
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462
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463
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464
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465
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466
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467
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468
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469
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470
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471
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472
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473
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474
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475
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476
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477
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478
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479
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480
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481
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482
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483
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484
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485
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486
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487
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488
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489
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490
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491
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492
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493
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494
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495
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496
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497
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498
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499
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500
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501
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502
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503
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504
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505
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506
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507
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508
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509
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510
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511
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512
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513
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514
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515
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516
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517
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518
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519
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520
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521
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522
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523
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524
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525
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526
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527
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528
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529
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530
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531
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532
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533
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534
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535
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536
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537
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538
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539
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540
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541
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542
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543
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544
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545
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546
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547
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548
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549
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550
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551
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552
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553
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554
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555
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556
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557
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558
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559
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560
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561
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562
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563
        0.046639 , -0.0059467,  1.0374   ,  0.16819  ,  0.25654  ]), 'how': np.asarray([  6.89380000e-01,  -1.06440000e-01,   1.70830000e-01,

564
        -3.75830000e-01,   7.51700000e-01,   7.81490000e-04,

565
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566
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567
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568
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569
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570
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571
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572
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573
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574
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575
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576
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577
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578
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579
        -2.25040000e-01,   6.14120000e-01]), 'people': np.asarray([ 0.95281  , -0.20608  ,  0.55618  , -0.46323  ,  0.73354  ,

580
        0.029137 , -0.19367  , -0.090066 , -0.22958  , -0.19058  ,

581
       -0.34857  , -1.0231   ,  0.743    , -0.5489   ,  0.88484  ,

582
       -0.14051  ,  0.0040139,  0.58448  ,  0.10767  , -0.44657  ,

583
       -0.43205  ,  0.9868   ,  0.78288  ,  0.51513  ,  0.85788  ,

584
       -1.7713   , -0.88259  , -0.59728  ,  0.084934 , -0.48112  ,

585
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586
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587
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588
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590
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591
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592
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593
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594
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595
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596
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597
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598
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611
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612
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613
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614
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615
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616
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617
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618
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619
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620
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621
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636
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637
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639
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649
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650
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652
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653
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655
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658
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659
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660
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663
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666
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667
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668
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669
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670
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671
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672
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673
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674
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675
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676
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677
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678
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679
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680
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681
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682
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683
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684
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685
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686
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687
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688
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689
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690
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691
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692
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693
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694
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695
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696
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697
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698
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699
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700
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701
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702
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703
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704
       -0.3468  ,  0.052568,  0.87153 , -0.036582, -0.056057,  0.08516 ,

705
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706
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707
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708
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709
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710
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711
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712
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713
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714
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715
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716
        3.0569   ,  0.75397  , -0.34325  , -0.60568  , -0.4142   ,

717
       -0.8      , -0.17019  ,  0.35416  ,  0.29577  , -0.51743  ,

718
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719
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720
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721
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722
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723
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724
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725
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726
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727
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728
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729
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730
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731
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732
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733
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734
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735
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736
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737
       -1.0737  , -0.4156  ,  0.81837 ,  0.47902 ,  0.078869, -0.60318 ,

738
       -0.5732  ,  0.065613,  0.21122 , -0.20912 , -0.27305 , -0.21332 ,

739
       -0.077237, -1.4695  ,  0.46413 ,  0.1222  ,  0.80235 ,  0.25796 ,

740
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741
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742
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743
       -0.37332 , -0.18039 ]), 'up': np.asarray([ 0.032286 , -0.27071  ,  0.68108  , -0.27942  ,  0.5797   ,

744
       -0.0081097, -0.82792  , -0.53342  , -0.47851  , -0.068256 ,

745
       -0.46964  , -0.31717  , -0.49372  ,  0.09808  ,  0.49961  ,

746
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747
        0.59084  ,  0.62031  ,  0.30467  , -0.41578  , -0.0222   ,

748
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749
        3.9129   ,  0.80075  , -0.18839  ,  0.42435  ,  0.039207 ,

750
       -0.093939 , -0.39516  ,  0.20976  ,  0.59488  , -0.3907   ,

751
       -0.31555  ,  0.24074  ,  0.41694  ,  0.10415  , -0.044305 ,

752
       -0.09516  ,  0.25464  , -0.56699  ,  0.033216 , -0.58123  ]), 'alone': np.asarray([ 0.5357  , -0.10067 ,  0.53897 , -0.36807 ,  0.67909 ,  0.31371 ,

753
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754
       -0.030773, -0.51339 ,  1.0718  ,  0.4463  ,  0.037857,  0.3219  ,

755
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756
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757
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758
        0.018789,  0.148   ,  0.28147 , -0.16007 , -0.53388 ,  0.10427 ,

759
        0.51568 , -0.025971, -0.60817 , -0.077016, -0.16988 , -0.41623 ,

760
       -0.22232 ,  0.16686 ]), 'boring': np.asarray([-0.035674, -0.41765 , -0.44104 , -0.35455 , -0.28748 , -0.25642 ,

761
        0.13326 ,  0.020065, -0.66321 ,  0.12049 , -1.0205  , -0.029451,

762
       -0.25333 ,  0.56927 ,  0.51631 ,  0.11365 ,  0.33299 ,  1.074   ,

763
        0.070584, -0.75984 , -0.2908  ,  0.56506 ,  0.54392 ,  0.66245 ,

764
        1.1402  , -0.56075 , -0.99398 ,  1.1002  ,  1.1428  , -0.1684  ,

765
        1.3748  , -0.45543 ,  0.38396 , -0.011839, -0.087978,  0.32026 ,

766
        0.28684 ,  0.88902 , -0.18163 , -0.54099 , -0.16129 , -0.38781 ,

767
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768
        0.37791 ,  0.98184 ]), 'good': np.asarray([ -3.55860000e-01,   5.21300000e-01,  -6.10700000e-01,

769
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770
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771
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772
        -4.73400000e-01,  -7.58950000e-02,   3.82470000e-01,

773
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774
        -8.37640000e-03,  -7.66200000e-01,  -5.62530000e-01,

775
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776
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777
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778
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779
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780
        -1.55870000e-01,   8.65830000e-01,   2.63090000e-01,

781
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782
        -1.77040000e-01,   2.70320000e-01,   1.10260000e-01,

783
         1.41330000e-01,  -5.73220000e-02,   2.72070000e-01,

784
         3.13050000e-01,   9.27710000e-01]), 'great': np.asarray([-0.026567,  1.3357  , -1.028   , -0.3729  ,  0.52012 , -0.12699 ,

785
       -0.35433 ,  0.37824 , -0.29716 ,  0.093894, -0.034122,  0.92961 ,

786
       -0.14023 , -0.63299 ,  0.020801, -0.21533 ,  0.96923 ,  0.47654 ,

787
       -1.0039  , -0.24013 , -0.36325 , -0.004757, -0.5148  , -0.4626  ,

788
        1.2447  , -1.8316  , -1.5581  , -0.37465 ,  0.53362 ,  0.20883 ,

789
        3.2209  ,  0.64549 ,  0.37438 , -0.17657 , -0.024164,  0.33786 ,

790
       -0.419   ,  0.40081 , -0.11449 ,  0.051232, -0.15205 ,  0.29855 ,

791
       -0.44052 ,  0.11089 , -0.24633 ,  0.66251 , -0.26949 , -0.49658 ,

792
       -0.41618 , -0.2549  ]), 'awesome': np.asarray([-0.35848 , -0.1155  ,  0.11371 ,  0.46814 ,  0.7495  , -0.61523 ,

793
        0.47639 ,  0.090754,  0.3689  ,  0.50331 , -0.22467 ,  0.234   ,

794
       -0.64901 ,  0.055667,  0.30192 , -0.13536 ,  0.93473 ,  0.88677 ,

795
       -0.70756 , -0.48408 , -0.90625 ,  0.62314 , -0.18793 , -0.5102  ,

796
        1.2565  , -0.28897 , -1.2819  ,  0.30284 ,  1.0423  , -0.54885 ,

797
        1.0054  ,  0.62053 ,  0.31879 , -0.060822, -0.24919 ,  0.5019  ,

798
        0.41171 ,  0.13648 , -0.49815 , -0.59822 , -0.16876 , -0.26096 ,

799
       -0.53283 ,  0.20083 , -0.19095 , -0.028693,  0.090843, -0.11063 ,

800
       -0.040858,  0.88439 ])}

801


802
diw[70] = 'cucumber'

803
dwi['cucumber'] = 70

804


805
d['cucumber'] = np.asarray([ 0.68224 , -0.31608 , -0.95201 ,  0.47108 ,  0.56571 ,  0.13151 ,

806
        0.22457 ,  0.094995, -1.3237  , -0.51545 , -0.39337 ,  0.88488 ,

807
        0.93826 ,  0.22931 ,  0.088624, -0.53908 ,  0.23396 ,  0.73245 ,

808
       -0.019123, -0.26552 , -0.40433 , -1.5832  ,  1.1316  ,  0.4419  ,

809
       -0.48218 ,  0.4828  ,  0.14938 ,  1.1245  ,  1.0159  , -0.50213 ,

810
        0.83831 , -0.31303 ,  0.083242,  1.7161  ,  0.15024 ,  1.0324  ,

811
       -1.5005  ,  0.62348 ,  0.54508 , -0.88484 ,  0.53279 , -0.085119,

812
        0.02141 , -0.56629 ,  1.1463  ,  0.6464  ,  0.78318 , -0.067662,

813
        0.22884 , -0.042453])

814


815
class suppress_stdout_stderr(object):

816
    '''

817
    A context manager for doing a "deep suppression" of stdout and stderr in 

818
    Python, i.e. will suppress all print, even if the print originates in a 

819
    compiled C/Fortran sub-function.

820
       This will not suppress raised exceptions, since exceptions are printed

821
    to stderr just before a script exits, and after the context manager has

822
    exited (at least, I think that is why it lets exceptions through).      

823


824
    '''

825
    def __init__(self):

826
        # Open a pair of null files

827
        self.null_fds =  [os.open(os.devnull,os.O_RDWR) for x in range(2)]

828
        # Save the actual stdout (1) and stderr (2) file descriptors.

829
        self.save_fds = [os.dup(1), os.dup(2)]

830


831
    def __enter__(self):

832
        # Assign the null pointers to stdout and stderr.

833
        os.dup2(self.null_fds[0],1)

834
        os.dup2(self.null_fds[1],2)

835


836
    def __exit__(self, *_):

837
        # Re-assign the real stdout/stderr back to (1) and (2)

838
        os.dup2(self.save_fds[0],1)

839
        os.dup2(self.save_fds[1],2)

840
        # Close all file descriptors

841
        for fd in self.null_fds + self.save_fds:

842
            os.close(fd)

843


844


845
# with suppress_stdout_stderr():

846
with stdout_redirector.stdout_redirected():

847
       sta = sentence_to_avg('I am going to the bar tonight',d)

848
       # generating the the model 

849
       p,w,b = model(X, Y, d, learning_rate = 0.01, num_iterations = 300)

850
       # generating the sentences_to_indices

851
       a = sentences_to_indices(X, dwi, 20)

852
       from keras.models import Sequential

853
       from keras.initializers import glorot_uniform

854
       # generating the test cases for pretrain_embedding_layer

855
       # model2 = Sequential()

856
       # model2.add(pretrained_embedding_layer(d, dwi))

857
       # # compile the model

858
       # model2.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])

859
       # ct = model2.count_params()

860
       # summarize the model

861


862


863
       embedding_layer = pretrained_embedding_layer(d, dwi)

864
       el = embedding_layer.get_weights()[0][1][3]

865
       

866


867
       np.random.seed(3)

868


869
       m = Emojify_V2((10,), d, dwi)

870
       cp = m.count_params()

871
       ml = len(m.layers)

872
       mi = len(m.inputs)

873
       mo = len(m.outputs)

874


875


876
def generateTestCases():

877
	testCases = {

878
	    'sentence_to_avg': {

879
	        'partId': 'Ub0em',

880
	        'testCases': [

881
	            {

882
	                'testInput': ('I am going to the bar tonight',d),

883
	                'testOutput': sta

884
	            }

885
	        ]

886
	    },

887
	    'model': { 

888
	        'partId': 'EDw3y',

889
	        'testCases': [

890
	            {

891
	                'testInput': (X, Y, d, 0.01, 300),

892
	                'testOutput': (p,w,b)

893
	            }

894
	        ]

895
	    },

896
	    'sentences_to_indices': { 

897
	        'partId': 'ELQ6c',

898
	        'testCases': [

899
	            {

900
	                'testInput': (X, dwi, 20),

901
	                'testOutput': a

902
	            }

903
	        ]

904
	    },

905
	    'pretrained_embedding_layer': { 

906
	        'partId': 'pEJr9',

907
	        'testCases': [

908
	            {

909
	                'testInput': (d, dwi),

910
	                'testOutput': el

911
	            }

912
	        ]

913
	    },

914
      'Emojify_V2': { 

915
          'partId': 'VRhCD',

916
          'testCases': [

917
              {

918
                  'testInput': (d, dwi),

919
                  'testOutput': (cp, ml, mi, mo)

920
              }

921
          ]

922
       }

923
       }

924
	return testCases

925


926


927