resnet(v1/v1.5/v2).py - academic (idiomatic)
resnet_cifar10(v1/v2).py - academic (idiomatic)
resnet(v1/v1.5/v2)_c.py - production (composable)
In v2.0, the BatchNormalization and ReLU activation function is moved from after the convolution to before.
In v1.5, the strided convolution is moved from the 1x1 convolution to the 3x3 bottleneck convolution.
In v2.0, the BatchNormalization and ReLU activation function is moved from after the convolution to before.
Example: Instantiate a stock ResNet model
from resnet_v1_c import ResNetV1
# ResNet50 v1.0 from research paper
resnet = ResNetV1(50)
# ResNet50 v1.0 custom input shape/classes
resnet = ResNetV1(50, input_shape=(128, 128, 3), n_classes=50)
# getter for the tf.keras model
model = resnet.modelExample: Compose and Train a ResNet model
''' Example for constructing/training a ResNet V1 model on CIFAR-10
'''
# Example of constructing a mini-ResNet
groups = [ { 'n_filters' : 64, 'n_blocks': 1 },
{ 'n_filters': 128, 'n_blocks': 2 },
{ 'n_filters': 256, 'n_blocks': 2 }]
resnet = ResNetV1(groups, input_shape=(32, 32, 3), n_classes=10)
resnet.model.summary()
resnet.cifar10()
```