def feature_extractor(rng, dim):
"""Feature extraction network."""
init_params, forward = stax.serial(
Conv(16, (8, 8), padding='SAME', strides=(2, 2)),
Relu,
MaxPool((2, 2), (1, 1)),
Conv(32, (4, 4), padding='VALID', strides=(2, 2)),
Relu,
MaxPool((2, 2), (1, 1)),
Flatten,
Dense(dim),
)
temp, rng = random.split(rng)
params = init_params(temp, (-1, 28, 28, 1))[1]
return params, forward
def ResNet50(num_classes):
return stax.serial(
GeneralConv(("HWCN", "OIHW", "NHWC"), 64, (7, 7), (2, 2), "SAME"),
BatchNorm(),
Relu,
MaxPool((3, 3), strides=(2, 2)),
ConvBlock(3, [64, 64, 256], strides=(1, 1)),
IdentityBlock(3, [64, 64]),
IdentityBlock(3, [64, 64]),
ConvBlock(3, [128, 128, 512]),
IdentityBlock(3, [128, 128]),
IdentityBlock(3, [128, 128]),
IdentityBlock(3, [128, 128]),
ConvBlock(3, [256, 256, 1024]),
IdentityBlock(3, [256, 256]),
IdentityBlock(3, [256, 256]),
IdentityBlock(3, [256, 256]),
IdentityBlock(3, [256, 256]),
IdentityBlock(3, [256, 256]),
ConvBlock(3, [512, 512, 2048]),
IdentityBlock(3, [512, 512]),
IdentityBlock(3, [512, 512]),
AvgPool((7, 7)),
Flatten,
Dense(num_classes),
LogSoftmax,
)
def __init__(self,
pool_size: Tuple[int, int],
padding: str = "valid") -> None:
self.layer: List = [
MaxPool(window_shape=pool_size,
padding=padding.upper(),
spec="NHWC")
]
def conv():
init_fun, predict = stax.serial(
Conv(16, (8, 8), padding='SAME', strides=(2, 2)),
Relu,
MaxPool((2, 2), (1, 1)),
Conv(32, (4, 4), padding='VALID', strides=(2, 2)),
Relu,
MaxPool((2, 2), (1, 1)),
Flatten,
Dense(32),
Relu,
Dense(10),
)
def init_params(rng):
return init_fun(rng, (-1, 28, 28, 1))[1]
return init_params, predict
def ResNet(num_classes):
return stax.serial(
GeneralConv(('HWCN', 'OIHW', 'NHWC'), 64, (7, 7), (2, 2), 'SAME'),
BatchNorm(), Relu, MaxPool((3, 3), strides=(2, 2)),
convBlock(3, [64, 64, 256]), identityBlock(3, [64, 64]),
identityBlock(3, [64, 64]), convBlock(3, [128, 128, 512]),
identityBlock(3, [128, 128]), identityBlock(3, [128, 128]),
identityBlock(3, [128, 128]), convBlock(3, [256, 256, 1024]),
identityBlock(3, [256, 256]), identityBlock(3, [256, 256]),
identityBlock(3, [256, 256]), identityBlock(3, [256, 256]),
identityBlock(3, [256, 256]), convBlock(3, [512, 512, 2048]),
identityBlock(3, [512, 512]), identityBlock(3, [512, 512]),
AvgPool((7, 7)), Flatten, Dense(num_classes), LogSoftmax)
def LeNet5(batch_size, num_particles):
input_shape = _input_shape(batch_size)
return make_model(
stax.serial(
GeneralConv(('NCHW', 'OIHW', 'NHWC'),
out_chan=6,
filter_shape=(5, 5),
strides=(1, 1),
padding="VALID"), Relu,
MaxPool(window_shape=(2, 2), strides=(2, 2), padding="VALID"),
Conv(out_chan=16,
filter_shape=(5, 5),
strides=(1, 1),
padding="SAME"), Relu,
MaxPool(window_shape=(2, 2), strides=(2, 2), padding="SAME"),
Conv(out_chan=120,
filter_shape=(5, 5),
strides=(1, 1),
padding="VALID"), Relu,
MaxPool(window_shape=(2, 2),
strides=(2, 2), padding="SAME"), Flatten, Dense(84), Relu,
Dense(10), LogSoftmax), input_shape, num_particles)
def ResNet(num_classes):
return stax.serial(
GeneralConv(("HWCN", "OIHW", "NHWC"), 64, (7, 7), (2, 2), "SAME"),
BatchNorm(),
Relu,
MaxPool((3, 3), strides=(2, 2)),
ConvBlock(3, [4, 4, 4], strides=(1, 1)),
IdentityBlock(3, [4, 4]),
AvgPool((3, 3)),
Flatten,
Dense(num_classes),
LogSoftmax,
)
def loss(params, batch):
inputs, targets = batch
preds = predict(params, inputs)
return -np.mean(np.sum(preds * targets, axis=1))
def accuracy(params, batch):
inputs, targets = batch
target_class = np.argmax(targets, axis=1)
predicted_class = np.argmax(predict(params, inputs), axis=1)
return np.mean(predicted_class == target_class)
init_random_params, predict = stax.serial(
Conv(10, (5, 5), (1, 1)), Activator,
MaxPool((4, 4)), Flatten,
Dense(10), LogSoftmax)
if __name__ == "__main__":
rng = random.PRNGKey(0)
step_size = 0.001
num_epochs = 10
batch_size = 128
momentum_mass = 0.9
# input shape for CNN
input_shape = (-1, 28, 28, 1)
# training/test split
(train_images, train_labels), (test_images, test_labels) = mnist_data.tiny_mnist(flatten=False)
def loss(params, batch):
inputs, targets = batch
preds = predict(params, inputs)
return -np.mean(np.sum(preds * targets, axis=1))
def accuracy(params, batch):
inputs, targets = batch
target_class = np.argmax(targets, axis=1)
predicted_class = np.argmax(predict(params, inputs), axis=1)
return np.mean(predicted_class == target_class)
init_random_params, predict = stax.serial(Conv(10, (5, 5), (1, 1)), Activator,
MaxPool((4, 4)), Flatten, Dense(24),
LogSoftmax)
if __name__ == "__main__":
rng = random.PRNGKey(0)
step_size = 0.001
num_epochs = 10
batch_size = 128
momentum_mass = 0.9
# input shape for CNN
input_shape = (-1, 28, 28, 1)
# training/test split
(train_images,
def ResNet50(num_classes,
batchnorm=True,
parameterization='standard',
nonlinearity='relu'):
# Define layer constructors
if parameterization == 'standard':
def MyGeneralConv(*args, **kwargs):
return GeneralConv(*args, **kwargs)
def MyDense(*args, **kwargs):
return Dense(*args, **kwargs)
elif parameterization == 'ntk':
def MyGeneralConv(*args, **kwargs):
return stax._GeneralConv(*args, **kwargs)[:2]
def MyDense(*args, **kwargs):
return stax.Dense(*args, **kwargs)[:2]
# Define nonlinearity
if nonlinearity == 'relu':
nonlin = Relu
elif nonlinearity == 'swish':
nonlin = Swish
elif nonlinearity == 'swishten':
nonlin = Swishten
elif nonlinearity == 'softplus':
nonlin = Softplus
return jax_stax.serial(
MyGeneralConv(('NHWC', 'HWIO', 'NHWC'),
64, (7, 7),
strides=(2, 2),
padding='SAME'),
BatchNorm() if batchnorm else Identity, nonlin,
MaxPool((3, 3), strides=(2, 2)),
ConvBlock(3, [64, 64, 256],
strides=(1, 1),
batchnorm=batchnorm,
parameterization=parameterization,
nonlin=nonlin),
IdentityBlock(3, [64, 64],
batchnorm=batchnorm,
parameterization=parameterization,
nonlin=nonlin),
IdentityBlock(3, [64, 64],
batchnorm=batchnorm,
parameterization=parameterization,
nonlin=nonlin),
ConvBlock(3, [128, 128, 512],
batchnorm=batchnorm,
parameterization=parameterization,
nonlin=nonlin),
IdentityBlock(3, [128, 128],
batchnorm=batchnorm,
parameterization=parameterization,
nonlin=nonlin),
IdentityBlock(3, [128, 128],
batchnorm=batchnorm,
parameterization=parameterization,
nonlin=nonlin),
IdentityBlock(3, [128, 128],
batchnorm=batchnorm,
parameterization=parameterization,
nonlin=nonlin),
ConvBlock(3, [256, 256, 1024],
batchnorm=batchnorm,
parameterization=parameterization,
nonlin=nonlin),
IdentityBlock(3, [256, 256],
batchnorm=batchnorm,
parameterization=parameterization,
nonlin=nonlin),
IdentityBlock(3, [256, 256],
batchnorm=batchnorm,
parameterization=parameterization,
nonlin=nonlin),
IdentityBlock(3, [256, 256],
batchnorm=batchnorm,
parameterization=parameterization,
nonlin=nonlin),
IdentityBlock(3, [256, 256],
batchnorm=batchnorm,
parameterization=parameterization,
nonlin=nonlin),
IdentityBlock(3, [256, 256],
batchnorm=batchnorm,
parameterization=parameterization,
nonlin=nonlin),
ConvBlock(3, [512, 512, 2048],
batchnorm=batchnorm,
parameterization=parameterization,
nonlin=nonlin),
IdentityBlock(3, [512, 512],
batchnorm=batchnorm,
parameterization=parameterization,
nonlin=nonlin),
IdentityBlock(3, [512, 512],
batchnorm=batchnorm,
parameterization=parameterization,
nonlin=nonlin),
stax.GlobalAvgPool()[:-1], MyDense(num_classes))
def loss(params, batch, rng):
inputs, targets = batch
preds = predict(params, inputs, rng=rng)
return -np.mean(np.sum(preds * targets, axis=1))
def accuracy(params, batch, rng):
inputs, targets = batch
target_class = np.argmax(targets, axis=1)
predicted_class = np.argmax(predict(params, inputs, rng=rng), axis=1)
return np.mean(predicted_class == target_class)
init_random_params, predict = stax.serial(Conv(10, (5, 5), (1, 1)), Activator,
Dropout(dropout_rate), MaxPool(
(4, 4)), Flatten, Dense(10),
LogSoftmax)
if __name__ == "__main__":
rng = random.PRNGKey(0)
step_size = 0.001
num_epochs = 10
batch_size = 128
momentum_mass = 0.9
# input shape for CNN
input_shape = (-1, 28, 28, 1)
# training/test split
(train_images,