-
Notifications
You must be signed in to change notification settings - Fork 2
/
model.py
49 lines (38 loc) · 1.09 KB
/
model.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
import numpy as np
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # Disable GPU
from keras.models import Sequential
from keras.layers import Dense
class Seq(Sequential):
'''
Wraps Keras Sequential model for ease of use
'''
def __init__(self):
super(Seq, self).__init__()
# input layer
self.add(Dense(100, input_dim=12, activation='relu'))
# hidden layer
self.add(Dense(20, activation='relu'))
# output layer
self.add(Dense(2, activation='softmax'))
self.summary()
self.n_params = self.count_params()
def pred(self, inputs):
'''
Calls model.predict
- Args:
inputs (array-like):
see game.py for inputs
Returns:
int, 0 for left, 1 for right
'''
out = self.predict(inputs, verbose=0)[0]
return np.where(out==np.max(out))[0][0]
def _set_weights(self, ind):
new_parameters = []
param_idx = 0
for i, layer in enumerate(self.get_weights()):
num_parameters_taken = np.prod(layer.shape)
new_parameters.append(ind[param_idx:param_idx+num_parameters_taken].reshape(layer.shape))
param_idx += num_parameters_taken
self.set_weights(new_parameters)