Roan Gylberth fatalure

## wines.csv

          
            class
            alcohol
            malic acid
            ash
            alcalinity
            magnesium
            phenols
            flavanoids
            nonflavanoid phenols
            proanthocyanins
            color intensity
            hue
            od280
            proline

            
              1
              14.23
              1.71
              2.43
              15.6
              127
              2.8
              3.06
              .28
              2.29
              5.64
              1.04
              3.92
              1065

            
              1
              13.2
              1.78
              2.14
              11.2
              100
              2.65
              2.76
              .26
              1.28
              4.38
              1.05
              3.4
              1050

            
              1
              13.16
              2.36
              2.67
              18.6
              101
              2.8
              3.24
              .3
              2.81
              5.68
              1.03
              3.17
              1185

            
              1
              14.37
              1.95
              2.5
              16.8
              113
              3.85
              3.49
              .24
              2.18
              7.8
              .86
              3.45
              1480

            
              1
              13.24
              2.59
              2.87
              21
              118
              2.8
              2.69
              .39
              1.82
              4.32
              1.04
              2.93
              735

            
              1
              14.2
              1.76
              2.45
              15.2
              112
              3.27
              3.39
              .34
              1.97
              6.75
              1.05
              2.85
              1450

            
              1
              14.39
              1.87
              2.45
              14.6
              96
              2.5
              2.52
              .3
              1.98
              5.25
              1.02
              3.58
              1290

            
              1
              14.06
              2.15
              2.61
              17.6
              121
              2.6
              2.51
              .31
              1.25
              5.05
              1.06
              3.58
              1295

            
              1
              14.83
              1.64
              2.17
              14
              97
              2.8
              2.98
              .29
              1.98
              5.2
              1.08
              2.85
              1045

## inverted_dropout.py
keep_prob = 0.5

def train_step(X):
    hidden_layer_1 = np.maximum(0, np.dot(W1, X) + b1)
    dropout_mask_1 = np.random.binomial(1, keep_prob, hidden_layer_1.shape) / keep_prob
    hidden_layer_1 *= dropout_mask_1
    hidden_layer_2 = np.maximum(0, np.dot(W2, hidden_layer_1) + b2)
    dropout_mask_2 = np.random.binomial(1, keep_prob, hidden_layer_2.shape) / keep_prob
    hidden_layer_2 *= dropout_mask_2
    out = np.dot(W3, hidden_layer_2) + b3

## dropout.py
keep_prob = 0.5

def train_step(X):
    hidden_layer_1 = np.maximum(0, np.dot(W1, X) + b1)
    dropout_mask_1 = np.random.binomial(1, keep_prob, hidden_layer_1.shape)
    hidden_layer_1 *= dropout_mask_1
    hidden_layer_2 = np.maximum(0, np.dot(W2, hidden_layer_1) + b2)
    dropout_mask_2 = np.random.binomial(1, keep_prob, hidden_layer_2.shape)
    hidden_layer_2 *= dropout_mask_2
    out = np.dot(W3, hidden_layer_2) + b3
class	alcohol	malic acid	ash	alcalinity	magnesium	phenols	flavanoids	nonflavanoid phenols	proanthocyanins	color intensity	hue	od280	proline
1	14.23	1.71	2.43	15.6	127	2.8	3.06	.28	2.29	5.64	1.04	3.92	1065
1	13.2	1.78	2.14	11.2	100	2.65	2.76	.26	1.28	4.38	1.05	3.4	1050
1	13.16	2.36	2.67	18.6	101	2.8	3.24	.3	2.81	5.68	1.03	3.17	1185
1	14.37	1.95	2.5	16.8	113	3.85	3.49	.24	2.18	7.8	.86	3.45	1480
1	13.24	2.59	2.87	21	118	2.8	2.69	.39	1.82	4.32	1.04	2.93	735
1	14.2	1.76	2.45	15.2	112	3.27	3.39	.34	1.97	6.75	1.05	2.85	1450
1	14.39	1.87	2.45	14.6	96	2.5	2.52	.3	1.98	5.25	1.02	3.58	1290
1	14.06	2.15	2.61	17.6	121	2.6	2.51	.31	1.25	5.05	1.06	3.58	1295
1	14.83	1.64	2.17	14	97	2.8	2.98	.29	1.98	5.2	1.08	2.85	1045
	keep_prob = 0.5

	def train_step(X):
	hidden_layer_1 = np.maximum(0, np.dot(W1, X) + b1)
	dropout_mask_1 = np.random.binomial(1, keep_prob, hidden_layer_1.shape) / keep_prob
	hidden_layer_1 *= dropout_mask_1
	hidden_layer_2 = np.maximum(0, np.dot(W2, hidden_layer_1) + b2)
	dropout_mask_2 = np.random.binomial(1, keep_prob, hidden_layer_2.shape) / keep_prob
	hidden_layer_2 *= dropout_mask_2
	out = np.dot(W3, hidden_layer_2) + b3