Tensorflow implementation of ResNeXt using Cifar10
If you want to see the original author's code, please refer to this link
- Tensorflow 1.x
- Python 3.x
- tflearn (If you are easy to use global average pooling, you should install tflearn)
- If not enough GPU memory, Please edit the code
with tf.Session() as sess : NO
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess : OK
- I implemented (b)
- (b) is split + transform(bottleneck) + concatenate + transition + merge
def split_layer(self, input_x, stride, layer_name):
with tf.name_scope(layer_name) :
layers_split = list()
for i in range(cardinality) :
splits = self.transform_layer(input_x, stride=stride, scope=layer_name + '_splitN_' + str(i))
layers_split.append(splits)
return Concatenation(layers_split) - Cardinality means how many times you want to split.
def transform_layer(self, x, stride, scope):
with tf.name_scope(scope) :
x = conv_layer(x, filter=depth, kernel=[1,1], stride=stride, layer_name=scope+'_conv1')
x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')
x = Relu(x)
x = conv_layer(x, filter=depth, kernel=[3,3], stride=1, layer_name=scope+'_conv2')
x = Batch_Normalization(x, training=self.training, scope=scope+'_batch2')
x = Relu(x)
return x def transition_layer(self, x, out_dim, scope):
with tf.name_scope(scope):
x = conv_layer(x, filter=out_dim, kernel=[1,1], stride=1, layer_name=scope+'_conv1')
x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')
return x
Junho Kim