51 lines
2.0 KiB
Python
51 lines
2.0 KiB
Python
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# Preferred settings for this model is:
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# Base Learning Rate = 0.001
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# Crop Size = 224
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from model import Tower
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from utils import model_property
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import tensorflow as tf
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import tensorflow.contrib.slim as slim
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import utils as digits
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class UserModel(Tower):
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@model_property
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def inference(self):
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x = tf.reshape(self.x, shape=[-1, self.input_shape[0], self.input_shape[1], self.input_shape[2]])
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with slim.arg_scope([slim.conv2d, slim.fully_connected],
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weights_initializer=tf.contrib.layers.xavier_initializer(),
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weights_regularizer=slim.l2_regularizer(1e-6)):
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model = slim.conv2d(x, 96, [11, 11], 4, padding='VALID', scope='conv1')
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model = slim.max_pool2d(model, [3, 3], 2, scope='pool1')
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model = slim.conv2d(model, 256, [5, 5], 1, scope='conv2')
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model = slim.max_pool2d(model, [3, 3], 2, scope='pool2')
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model = slim.conv2d(model, 384, [3, 3], 1, scope='conv3')
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model = slim.conv2d(model, 384, [3, 3], 1, scope='conv4')
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model = slim.conv2d(model, 256, [3, 3], 1, scope='conv5')
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model = slim.max_pool2d(model, [3, 3], 2, scope='pool5')
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model = slim.flatten(model)
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model = slim.fully_connected(model, 4096, activation_fn=None, scope='fc1')
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model = slim.dropout(model, 0.5, is_training=self.is_training, scope='do1')
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model = slim.fully_connected(model, 4096, activation_fn=None, scope='fc2')
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model = slim.dropout(model, 0.5, is_training=self.is_training, scope='do2')
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model = slim.fully_connected(model, self.nclasses, activation_fn=None, scope='fc3')
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return model
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@model_property
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def loss(self):
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model = self.inference
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loss = digits.classification_loss(model, self.y)
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accuracy = digits.classification_accuracy(model, self.y)
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self.summaries.append(tf.summary.scalar(accuracy.op.name, accuracy))
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return loss
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