def __calc_fc7(self):
# 7th Layer: FC (w ReLu)
self.fc7 = tf.nn.relu(
fc(self.fc6,
4096,
4096,
name='fc7',
data=self.weights_data,
retrain_layers=self.retrain_layers))
def __calc_fc6(self):
# 6th Layer: Flatten -> FC (w ReLu)
flattened = tf.reshape(self.pool5, [-1, 6 * 6 * 256])
self.fc6 = tf.nn.relu(
fc(flattened,
6 * 6 * 256,
4096,
name='fc6',
data=self.weights_data,
retrain_layers=self.retrain_layers))
def __calc_fc6(self):
# 6th Layer: Flatten -> FC (w ReLu)
self.pool_6 = avg_pool(self.conv5_3,
7,
7,
7,
7,
padding='SAME',
name='pool6')
flattened = tf.reshape(self.pool_6, [-1, 1 * 1 * 2048])
self.fc6 = fc(flattened,
1 * 1 * 2048,
1000,
name='fc1000',
data=self.weights_data,
retrain_layers=self.retrain_layers)
def __calc_fc8(self):
self.fc8 = fc(self.fc7, 4096, 1000, name='fc8', data=self.weights_data,
retrain_layers=self.retrain_layers)
def __calc_fc6(self):
flattened = tf.reshape(self.pool5, [-1, 7 * 7 * 512])
self.fc6 = tf.nn.relu(fc(flattened, 7 * 7 * 512, 4096, name='fc6', data=self.weights_data,
retrain_layers=self.retrain_layers))
def __calc_fc8(self):
# 8th Layer: FC and return unscaled activations
self.fc8 = fc(self.fc7, 4096, 1000, name='fc8', data=self.weights_data)