def build(self, input_shape=(28, 28, 1), classes=10):
inputs = keras.Input(shape=input_shape)
outputs = conv2d(filters=6, kernel_size=(6, 6))(inputs)
outputs = max_pooling2d(pool_size=(2, 2), strides=(2, 2))(outputs)
outputs = sigmoid()(outputs)
outputs = conv2d(filters=16, kernel_size=(6, 6))(inputs)
outputs = max_pooling2d(pool_size=(2, 2), strides=(2, 2))(outputs)
outputs = sigmoid()(outputs)
outputs = flatten()(outputs)
outputs = dense(120)(outputs)
outputs = sigmoid()(outputs)
outputs = dense(64)(outputs)
outputs = sigmoid()(outputs)
outputs = dense(classes)(outputs)
outputs = softmax()(outputs)
model = keras.Model(inputs, outputs)
model.summary()
return model
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
def build(self, input_shape=(224, 224, 3), classes=1000):
inputs = keras.Input(shape=input_shape)
outputs = inputs
for i, b in enumerate(self.blocks):
outputs = self.conv_block(outputs, self.filters[i], b)
outputs = max_pooling2d(pool_size=(2, 2))(outputs)
outputs = dense(4096)(outputs)
outputs = dense(4096)(outputs)
outputs = dense(1000)(outputs)
outputs = softmax()(outputs)
model = keras.Model(inputs, outputs)
model.summary()
return model
def inception(self, inputs, filters, layers=None):
"""
Inception Module (default is inception v1)
If you want to use another inception module,
set `layers' appropriate layer function.
Below is inception v2 (Module A) example:
inception_layers = [[conv2d(filters=filters, kernel_size=(1, 1))],
[conv2d(filters=filters, kernel_size=(1, 1)),
conv2d(filters=filters, kernel_size=(3, 3))],
[conv2d(filters=filters, kernel_size=(1, 1)),
conv2d(filters=filters, kernel_size=(3, 3))
conv2d(filters=filters, kernel_size=(3, 3))],
[max_pooling2d(pool_size=(3, 3), strides=1),
conv2d(filters=filters, kernel_size=(1, 1))]]
outputs = self.inception(inputs, filters, layers=inception_layers)
"""
if layers is None:
layers = [[conv2d(filters=filters, kernel_size=(1, 1))],
[
conv2d(filters=filters, kernel_size=(1, 1)),
conv2d(filters=filters, kernel_size=(3, 3))
],
[
conv2d(filters=filters, kernel_size=(1, 1)),
conv2d(filters=filters, kernel_size=(5, 5))
],
[
max_pooling2d(pool_size=(3, 3), strides=1),
conv2d(filters=filters, kernel_size=(1, 1))
]]
outputs = [inputs] * len(layers)
for i in range(len(layers)):
for layer in layers[i]:
outputs[i] = layer(outputs[i])
outputs = concat()(outputs)
return outputs
def build(self, input_shape, classes):
# input
inputs = keras.Input(shape=input_shape)
filters = 64
outputs = conv2d(filters=filters,
kernel_size=(7, 7),
strides=2,
padding='same')(inputs)
outputs = max_pooling2d(pool_size=(3, 3), strides=2,
padding='same')(outputs)
filters = 192
# using batch norm instead of local response norm
outputs = batch_normalization()(outputs)
outputs = conv2d(filters=filters,
kernel_size=(1, 1),
strides=1,
padding='valid')(outputs)
outputs = conv2d(filters=filters,
kernel_size=(3, 3),
strides=1,
padding='same')(outputs)
# using batch norm instead of local response norm
outputs = batch_normalization()(outputs)
outputs = max_pooling2d(pool_size=(3, 3), strides=2,
padding='same')(outputs)
# inception (3a)
filters = 256
outputs = self.inception(inputs=outputs, filters=filters)
# inception (3b)
filters = 480
outputs = self.inception(inputs=outputs, filters=filters)
outputs = max_pooling2d(pool_size=(3, 3), strides=2,
padding='same')(outputs)
# inception (4a)
filters = 512
outputs = self.inception(inputs=outputs, filters=filters)
# if K.learning_phase() == 1:
outputs2 = self.classifier_aux(outputs, classes=classes)
# inception (4b)
outputs = self.inception(inputs=outputs, filters=filters)
# inception (4c)
outputs = self.inception(inputs=outputs, filters=filters)
# inception (4d)
filters = 528
outputs = self.inception(inputs=outputs, filters=filters)
# if K.learning_phase() == 1:
outputs1 = self.classifier_aux(outputs, classes=classes)
# inception (4e)
filters = 832
outputs = self.inception(inputs=outputs, filters=filters)
outputs = max_pooling2d(pool_size=(2, 2), strides=2,
padding='same')(outputs)
# inception (5a)
outputs = self.inception(inputs=outputs, filters=filters)
# inception (5b)
filters = 1024
outputs = self.inception(inputs=outputs, filters=filters)
# classifier
outputs0 = self.classifier_main(inputs=outputs, classes=1000)
model = keras.Model(inputs=inputs,
outputs=[outputs0, outputs1, outputs2])
model.summary()
return model
def build(self, input_shape, classes):
inputs = keras.Input(shape=input_shape)
# Entry flow
filters = 32
outputs = conv_relu_block(inputs, filters=filters, strides=(2, 2))
filters = 64
outputs = conv_relu_block(inputs, filters=filters)
residual = outputs
filters = 128
outputs = separable_conv_block(outputs, filters=filters)
outputs = relu_separable_conv_block(outputs, filters=filters)
outputs = max_pooling2d(pool_size=(3, 3), strides=(2, 2))(outputs)
residual = conv_block(residual,
filters=filters,
kernel_size=(1, 1),
strides=(2, 2))
outputs = add()([outputs, residual])
filters = 256
outputs = relu_separable_conv_block(outputs, filters=filters)
outputs = relu_separable_conv_block(outputs, filters=filters)
outputs = max_pooling2d(pool_size=(3, 3), strides=(2, 2))(outputs)
residual = conv_block(residual,
filters=filters,
kernel_size=(1, 1),
strides=(2, 2))
outputs = add()([outputs, residual])
filters = 728
outputs = relu_separable_conv_block(outputs, filters=filters)
outputs = relu_separable_conv_block(outputs, filters=filters)
outputs = max_pooling2d(pool_size=(3, 3), strides=(2, 2))(outputs)
residual = conv_block(residual,
filters=filters,
kernel_size=(1, 1),
strides=(2, 2))
outputs = add()([outputs, residual])
# Middle flow
filters = 728
for _ in range(8):
residual = outputs
outputs = relu_separable_conv_block(outputs, filters=filters)
outputs = relu_separable_conv_block(outputs, filters=filters)
outputs = relu_separable_conv_block(outputs, filters=filters)
outputs = add()([outputs, residual])
# Exit flow
residual = outputs
outputs = relu_separable_conv_block(outputs, filters=filters)
filters = 1024
outputs = relu_separable_conv_block(outputs, filters=filters)
outputs = add()([outputs, residual])
filters = 1536
outputs = separable_conv_relu_block(outputs, filters=filters)
filters = 2048
outputs = separable_conv_relu_block(outputs, filters=filters)
outputs = global_average_pooling2d()(outputs)
outputs = dense(filters)(outputs)
outputs = relu()(outputs)
outputs = dense(1000)(outputs)
outputs = softmax()(outputs)
model = keras.Model(inputs, outputs)
model.summary()
return model