| 209 | return self.f(conv_out + self.b.dimshuffle('x', 0, 'x', 'x')) |
| 210 | |
| 211 | class HiddenLayer: |
| 212 | def __init__(self, M1, M2, f=T.nnet.relu): |
| 213 | W = np.random.randn(M1, M2) * np.sqrt(2 / M1) |
| 214 | self.W = theano.shared(W.astype(np.float32)) |
| 215 | self.b = theano.shared(np.zeros(M2).astype(np.float32)) |
| 216 | self.params = [self.W, self.b] |
| 217 | self.f = f |
| 218 | |
| 219 | def forward(self, X): |
| 220 | a = X.dot(self.W) + self.b |
| 221 | return self.f(a) |
| 222 | |
| 223 | class DQN: |
| 224 | def __init__(self, K, conv_layer_sizes, hidden_layer_sizes): |