def build(self, input_shape, classes=10):
filters = 16
inputs = keras.Input(shape=input_shape)
outputs = batch_normalization()(inputs)
outputs = relu()(outputs)
outputs = conv2d(filters=filters, kernel_size=(3, 3))(outputs)
filters = filters * self.widening
for i in range(3):
for j in range(self.blocks):
down_sampling = True if (i > 0 and j == 0) else False
outputs = self.residual(outputs,
filters=filters,
down_sampling=down_sampling)
filters = 2 * filters
outputs = batch_normalization()(outputs)
outputs = relu()(outputs)
outputs = average_pooling2d(pool_size=(8, 8))(outputs)
outputs = dense(10)(outputs)
outputs = softmax()(outputs)
model = keras.Model(inputs, outputs)
model.summary()
return model
def classifier_main(self, inputs, classes):
outputs = average_pooling2d(pool_size=(7, 7),
strides=1,
padding='valid')(inputs)
outputs = flatten()(outputs)
outputs = dropout(0.4)(outputs)
outputs = dense(classes)(outputs)
outputs = softmax()(outputs)
return outputs
def transition_block(self, inputs):
filters = keras.backend.int_shape(inputs)[
self.batch_normalization_axis]
if self.compression:
filters = int(filters * (1 - self.reduction_rate))
outputs = batch_normalization()(inputs)
outputs = relu()(outputs)
outputs = conv2d(filters=filters, kernel_size=(1, 1))(outputs)
outputs = average_pooling2d(pool_size=(2, 2), strides=2)(outputs)
return outputs
def classifier_aux(self, inputs, classes):
filters = 128
outputs = average_pooling2d(pool_size=(5, 5),
strides=3,
padding='valid')(inputs)
outputs = conv2d(filters=filters,
kernel_size=(1, 1),
strides=1,
padding='same')(outputs)
outputs = flatten()(outputs)
outputs = relu()(outputs)
outputs = dense(1024)(outputs)
outputs = relu()(outputs)
outputs = dropout(0.7)(outputs)
outputs = dense(classes)(outputs)
outputs = softmax()(outputs)
return outputs
def __init__(
self,
blocks,
filters,
bottleneck=False,
input_layers=[
batch_normalization(),
relu(),
conv2d(filters=64, kernel_size=(7, 7), strides=2),
],
output_layers=[average_pooling2d(pool_size=(2, 2)),
flatten()]):
self.blocks = blocks
self.filters = filters
self.bottleneck = bottleneck
self.bn_axis = -1 if keras.backend.image_data_format(
) == 'channels_last' else 1
self.input_layers = input_layers
self.output_layer = output_layers
def __init__(self,
input_layers=None,
outputs_layers=None,
inception_layers=None):
self.inception_version = 1
if input_layers is None:
self.input_layers = [
conv2d(filters=64,
kernel_size=(7, 7),
strides=2,
padding='same'),
max_pooling2d(pool_size=(3, 3), strides=2, padding='same'),
batch_normalization(),
conv2d(filters=192,
kernel_size=(1, 1),
strides=1,
padding='valid'),
conv2d(filters=192,
kernel_size=(3, 3),
strides=1,
padding='same'),
batch_normalization(),
max_pooling2d(pool_size=(3, 3), strides=2, padding='same')
]
else:
self.input_layers = input_layers
if outputs_layers is None:
self.output_layers = [
average_pooling2d(pool_size=(7, 7), strides=1,
padding='valid'),
flatten(),
dropout(0.4),
dense(1000),
softmax()
]
else:
self.output_layers = outputs_layers