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)