(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model')
| 153 | |
| 154 | |
| 155 | def resnext(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'): |
| 156 | net = input_data(shape=[None, width, height, 3], name='input') |
| 157 | net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001) |
| 158 | net = tflearn.layers.conv.resnext_block(net, n, 16, 32) |
| 159 | net = tflearn.resnext_block(net, 1, 32, 32, downsample=True) |
| 160 | net = tflearn.resnext_block(net, n-1, 32, 32) |
| 161 | net = tflearn.resnext_block(net, 1, 64, 32, downsample=True) |
| 162 | net = tflearn.resnext_block(net, n-1, 64, 32) |
| 163 | net = tflearn.batch_normalization(net) |
| 164 | net = tflearn.activation(net, 'relu') |
| 165 | net = tflearn.global_avg_pool(net) |
| 166 | # Regression |
| 167 | net = tflearn.fully_connected(net, output, activation='softmax') |
| 168 | opt = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True) |
| 169 | net = tflearn.regression(net, optimizer=opt, |
| 170 | loss='categorical_crossentropy') |
| 171 | |
| 172 | model = tflearn.DNN(net, |
| 173 | max_checkpoints=0, tensorboard_verbose=0, tensorboard_dir='log') |
| 174 | |
| 175 | return model |
| 176 | |
| 177 | |
| 178 | def sentnet_color_2d(width, height, frame_count, lr, output=9, model_name = 'sentnet_color.model'): |
nothing calls this directly
no outgoing calls
no test coverage detected