35 lines
1.5 KiB
Python
35 lines
1.5 KiB
Python
from model import Tower
|
|
from utils import model_property
|
|
import tensorflow as tf
|
|
import tensorflow.contrib.slim as slim
|
|
import utils as digits
|
|
|
|
|
|
class UserModel(Tower):
|
|
|
|
@model_property
|
|
def inference(self):
|
|
x = tf.reshape(self.x, shape=[-1, self.input_shape[0], self.input_shape[1], self.input_shape[2]])
|
|
# scale (divide by MNIST std)
|
|
x = x * 0.0125
|
|
with slim.arg_scope([slim.conv2d, slim.fully_connected],
|
|
weights_initializer=tf.contrib.layers.xavier_initializer(),
|
|
weights_regularizer=slim.l2_regularizer(0.0005)):
|
|
model = slim.conv2d(x, 20, [5, 5], padding='VALID', scope='conv1')
|
|
model = slim.max_pool2d(model, [2, 2], padding='VALID', scope='pool1')
|
|
model = slim.conv2d(model, 50, [5, 5], padding='VALID', scope='conv2')
|
|
model = slim.max_pool2d(model, [2, 2], padding='VALID', scope='pool2')
|
|
model = slim.flatten(model)
|
|
model = slim.fully_connected(model, 500, scope='fc1')
|
|
model = slim.dropout(model, 0.5, is_training=self.is_training, scope='do1')
|
|
model = slim.fully_connected(model, self.nclasses, activation_fn=None, scope='fc2')
|
|
return model
|
|
|
|
@model_property
|
|
def loss(self):
|
|
model = self.inference
|
|
loss = digits.classification_loss(model, self.y)
|
|
accuracy = digits.classification_accuracy(model, self.y)
|
|
self.summaries.append(tf.summary.scalar(accuracy.op.name, accuracy))
|
|
return loss
|