-
Notifications
You must be signed in to change notification settings - Fork 1
/
Models.py
161 lines (113 loc) · 5.38 KB
/
Models.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
np.random.seed(12)
import tensorflow
tensorflow.random.set_seed(12)
import time
from keras.layers import Input, Dense, Dropout, BatchNormalization, Flatten
from keras import optimizers
from keras.models import Model, load_model
from keras import regularizers
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D
from hyperas import optim
from hyperas.distributions import choice, uniform, loguniform
from hyperopt import Trials, STATUS_OK, tpe, hp
from keras.optimizers import RMSprop, Adadelta, Adagrad, Nadam, Adam
import global_config
from keras import callbacks
from sklearn.model_selection import train_test_split
def CNNPooling(params, input_shape, n_classes):
X_input = Input(input_shape)
X=Conv2D(32, (2, 2), activation=params['activation'], name = 'conv0')(X_input)
X=MaxPooling2D(pool_size=(1,2), padding='same')(X)
X=Dropout(params['dropout1'])(X)
X = Conv2D(64, (2, 2), activation=params['activation'], name='conv1')(X)
X=MaxPooling2D(pool_size=(2,2), padding='same')(X)
X=Dropout(params['dropout2'])(X)
X = Conv2D(128, (1, 2), activation=params['activation'], name='conv2')(X)
X =Flatten()(X)
X = Dense(256, activation='relu')(X)
X = Dense(1024, activation='relu')(X)
X = Dense(n_classes, activation='softmax')(X)
model=Model(input=X_input, output=X)
model.summary()
model.compile(loss=params['losses'],
optimizer=params['optimizer'] (lr=params['lr']),
metrics=['acc'])
return model
def CNN(params, input_shape, n_classes):
X_input = Input(input_shape)
X=Conv2D(32, (2, 2), activation=params['activation'], name = 'conv0', kernel_initializer='glorot_uniform')(X_input)
X=Dropout(params['dropout1'])(X)
X = Conv2D(64, (2, 2), activation=params['activation'], name='conv1', kernel_initializer='glorot_uniform')(X)
X=Dropout(params['dropout2'])(X)
X = Conv2D(128, (1, 2), activation=params['activation'], name='conv2', kernel_initializer='glorot_uniform')(X)
X =Flatten()(X)
X = Dense(256, activation='relu', kernel_initializer='glorot_uniform')(X)
X = Dense(1024, activation='relu', kernel_initializer='glorot_uniform')(X)
X = Dense(n_classes, activation='softmax')(X)
model=Model(input=X_input, output=X)
model.summary()
model.compile(loss=params['losses'],
optimizer=params['optimizer'] (lr=params['lr']),
metrics=['acc'])
return model
def Autoencoder(x_train, y_train, x_test, y_test):
input_shape = (x_train.shape[1],)
input2 = Input(input_shape)
# encoder_layer
# Dropoout?
# input1 = Dropout(.2)(input)
encoded = Dense(80, activation='relu',
kernel_initializer='glorot_uniform',
name='encod0')(input2)
encoded = Dense(30, activation='relu',
kernel_initializer='glorot_uniform',
name='encod1')(encoded)
encoded = Dense(10, activation='relu',
kernel_initializer='glorot_uniform',
name='encod2')(encoded)
encoded= Dropout({{uniform(0, 1)}})(encoded)
decoded = Dense(30, activation='relu',
kernel_initializer='glorot_uniform',
name='decoder1')(encoded)
decoded = Dense(80, activation='relu',
kernel_initializer='glorot_uniform',
name='decoder2')(decoded)
decoded = Dense(x_train.shape[1], activation='linear',
kernel_initializer='glorot_uniform',
name='decoder3')(decoded)
model = Model(inputs=input2, outputs=decoded)
model.summary()
adam=Adam(lr={{uniform(0.0001, 0.01)}})
model.compile(loss='mse', metrics=['acc'],
optimizer=adam)
callbacks_list = [
callbacks.EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=10,
restore_best_weights=True),
]
XTraining, XValidation, YTraining, YValidation = train_test_split(x_train, y_train, stratify=y_train,
test_size=0.2) # before model building
tic = time.time()
history= model.fit(XTraining, YTraining,
batch_size={{choice([32,64, 128,256,512])}},
epochs=150,
verbose=1,
callbacks=callbacks_list,
validation_data=(XValidation,YValidation))
toc = time.time()
# get the highest validation accuracy of the training epochs
score = np.amin(history.history['val_loss'])
print('Best validation loss of epoch:', score)
scores = [history.history['val_loss'][epoch] for epoch in range(len(history.history['loss']))]
score = min(scores)
print('Score',score)
print('Best score',global_config.best_score)
if global_config.best_score > score:
global_config.best_score = score
global_config.best_model = model
global_config.best_numparameters = model.count_params()
best_time = toc - tic
return {'loss': score, 'status': STATUS_OK, 'n_epochs': len(history.history['loss']), 'n_params': model.count_params(), 'model': global_config.best_model, 'time':toc - tic}