(image)
| 14 | |
| 15 | |
| 16 | def tower_func(image): |
| 17 | # img: 227x227x3 |
| 18 | with argscope([Conv2D, FullyConnected], activation=tf.nn.relu): |
| 19 | l = Conv2D('conv1', image, filters=96, kernel_size=11, strides=4, padding='VALID') |
| 20 | l = tf.nn.lrn(l, 2, bias=1.0, alpha=2e-5, beta=0.75, name='norm1') |
| 21 | l = MaxPooling('pool1', l, 3, strides=2, padding='VALID') |
| 22 | |
| 23 | l = Conv2D('conv2', l, filters=256, kernel_size=5, split=2) |
| 24 | l = tf.nn.lrn(l, 2, bias=1.0, alpha=2e-5, beta=0.75, name='norm2') |
| 25 | l = MaxPooling('pool2', l, 3, strides=2, padding='VALID') |
| 26 | |
| 27 | l = Conv2D('conv3', l, filters=384, kernel_size=3) |
| 28 | l = Conv2D('conv4', l, filters=384, kernel_size=3, split=2) |
| 29 | l = Conv2D('conv5', l, filters=256, kernel_size=3, split=2) |
| 30 | l = MaxPooling('pool3', l, 3, strides=2, padding='VALID') |
| 31 | |
| 32 | # This is just a script to load model, so we ignore the dropout layer |
| 33 | l = FullyConnected('fc6', l, 4096) |
| 34 | l = FullyConnected('fc7', l, 4096) |
| 35 | logits = FullyConnected('fc8', l, 1000) |
| 36 | tf.nn.softmax(logits, name='prob') |
| 37 | |
| 38 | |
| 39 | def run_test(path, input): |
nothing calls this directly
no test coverage detected