network =max_pool_2d(network,3) network = conv_2d(network,128,4, activation="relu")else: print("ERROR: exiting, unknown layer type for neural net")# classify as road or not roadsoftmax = tflearn.fully_connected(network,2, activation="softmax")# hyperparameters based on www.cs.toronto....
defforward(self, x):x = F.relu(F.max_pool2d(self.conv1(x),2)) x = F.relu(F.max_pool2d(self.conv2(x),2)) x = x.view(-1,320) x = F.relu(self.fc1(x)) x = self.fc2(x)returnx 开发者ID:lewisKit,项目名称:pyro,代码行数:7,代码来源:sv-dkl.py 示例7: forward ▲点赞...
2,strides=2,name='maxpool1')x=tflearn.conv_2d(x,128,3,activation='relu',scope='conv2_1')x=tflearn.conv_2d(x,128,3,activation='relu',scope='conv2_2')x=tflearn.max_pool_2d(x,
ignore_border=self.ignore_border)#max_pool_2dX and Y (with X constant)output = downsample.max_pool_2d(input=output.dimshuffle(0,4,2,3,1), ds=(1, self.pool_size[0]), ignore_border=self.ignore_border)else:#cpu order:(batch, row, column, time, inchannel) from cpu convolution#max_...