def create_alexnet():
# Input variables denoting the features and label data
feature_var = input_variable((num_channels, image_height, image_width))
label_var = input_variable((num_classes))
# apply model to input
# remove mean value
input = minus(feature_var, constant(114), name='mean_removed_input')
with default_options(activation=None, pad=True, bias=True):
z = Sequential([
# we separate Convolution and ReLU to name the output for feature extraction (usually before ReLU)
Convolution2D((11,11), 96, init=normal(0.01), pad=False, strides=(4,4), name='conv1'),
Activation(activation=relu, name='relu1'),
LocalResponseNormalization(1.0, 2, 0.0001, 0.75, name='norm1'),
MaxPooling((3,3), (2,2), name='pool1'),
Convolution2D((5,5), 192, init=normal(0.01), init_bias=0.1, name='conv2'),
Activation(activation=relu, name='relu2'),
LocalResponseNormalization(1.0, 2, 0.0001, 0.75, name='norm2'),
MaxPooling((3,3), (2,2), name='pool2'),
Convolution2D((3,3), 384, init=normal(0.01), name='conv3'),
Activation(activation=relu, name='relu3'),
Convolution2D((3,3), 384, init=normal(0.01), init_bias=0.1, name='conv4'),
Activation(activation=relu, name='relu4'),
Convolution2D((3,3), 256, init=normal(0.01), init_bias=0.1, name='conv5'),
Activation(activation=relu, name='relu5'),
MaxPooling((3,3), (2,2), name='pool5'),
Dense(4096, init=normal(0.005), init_bias=0.1, name='fc6'),
Activation(activation=relu, name='relu6'),
Dropout(0.5, name='drop6'),
Dense(4096, init=normal(0.005), init_bias=0.1, name='fc7'),
Activation(activation=relu, name='relu7'),
Dropout(0.5, name='drop7'),
Dense(num_classes, init=normal(0.01), name='fc8')
])(input)
# loss and metric
ce = cross_entropy_with_softmax(z, label_var)
pe = classification_error(z, label_var)
pe5 = classification_error(z, label_var, topN=5)
log_number_of_parameters(z) ; print()
return {
'feature': feature_var,
'label': label_var,
'ce' : ce,
'pe' : pe,
'pe5': pe5,
'output': z
}
def create_model(self):
mean_removed_features = minus(self.input,
constant(114),
name='mean_removed_input')
with default_options(activation=None, pad=True, bias=True):
self.model = Sequential([
Convolution2D((11, 11),
96,
init=normal(0.01),
pad=False,
name='conv1'),
Activation(activation=relu, name='relu1'),
self.__local_response_normalization(1.0,
2,
0.0001,
0.75,
name='norm1'),
MaxPooling((3, 3), (2, 2), name='pool1'),
Convolution2D((5, 5),
192,
init=normal(0.01),
init_bias=0.1,
name='conv2'),
Activation(activation=relu, name='relu2'),
self.__local_response_normalization(1.0,
2,
0.0001,
0.75,
name='norm2'),
MaxPooling((3, 3), (2, 2), name='pool2'),
Convolution2D((3, 3), 384, init=normal(0.01), name='conv3'),
Activation(activation=relu, name='relu3'),
Convolution2D((3, 3),
384,
init=normal(0.01),
init_bias=0.1,
name='conv4'),
Activation(activation=relu, name='relu4'),
Convolution2D((3, 3),
256,
init=normal(0.01),
init_bias=0.1,
name='conv5'),
Activation(activation=relu, name='relu5'),
MaxPooling((3, 3), (2, 2), name='pool5'),
Dense(4096, init=normal(0.005), init_bias=0.1, name='fc6'),
Activation(activation=relu, name='relu6'),
Dropout(0.5, name='drop6'),
Dense(4096, init=normal(0.005), init_bias=0.1, name='fc7'),
Activation(activation=relu, name='relu7'),
Dropout(0.5, name='drop7'),
Dense(self.number_labels, init=normal(0.01), name='fc8')
])(mean_removed_features)
def create_vgg16(feature_var, num_classes, dropout=0.9):
with default_options(activation=None, pad=True, bias=True):
z = Sequential([
# we separate Convolution and ReLU to name the output for feature
# extraction (usually before ReLU)
For(
range(2), lambda i: [
Convolution2D((3, 3), 64, name='conv1_{}'.format(i)),
Activation(activation=relu, name='relu1_{}'.format(i)),
]),
MaxPooling((2, 2), (2, 2), name='pool1'),
For(
range(2), lambda i: [
Convolution2D((3, 3), 128, name='conv2_{}'.format(i)),
Activation(activation=relu, name='relu2_{}'.format(i)),
]),
MaxPooling((2, 2), (2, 2), name='pool2'),
For(
range(3), lambda i: [
Convolution2D((3, 3), 256, name='conv3_{}'.format(i)),
Activation(activation=relu, name='relu3_{}'.format(i)),
]),
MaxPooling((2, 2), (2, 2), name='pool3'),
For(
range(3), lambda i: [
Convolution2D((3, 3), 512, name='conv4_{}'.format(i)),
Activation(activation=relu, name='relu4_{}'.format(i)),
]),
MaxPooling((2, 2), (2, 2), name='pool4'),
For(
range(3), lambda i: [
Convolution2D((3, 3), 512, name='conv5_{}'.format(i)),
Activation(activation=relu, name='relu5_{}'.format(i)),
]),
MaxPooling((2, 2), (2, 2), name='pool5'),
Dense(4096, name='fc6'),
Activation(activation=relu, name='relu6'),
Dropout(dropout, name='drop6'),
Dense(4096, name='fc7'),
Activation(activation=relu, name='relu7'),
Dropout(dropout, name='drop7'),
Dense(num_classes, name='fc8')
])(feature_var)
return z
def create_vgg19():
# Input variables denoting the features and label data
feature_var = input((num_channels, image_height, image_width))
label_var = input((num_classes))
# apply model to input
# remove mean value
input = minus(feature_var,
constant([[[104]], [[117]], [[124]]]),
name='mean_removed_input')
with default_options(activation=None, pad=True, bias=True):
z = Sequential([
# we separate Convolution and ReLU to name the output for feature extraction (usually before ReLU)
For(
range(2), lambda i: [
Convolution2D((3, 3), 64, name='conv1_{}'.format(i)),
Activation(activation=relu, name='relu1_{}'.format(i)),
]),
MaxPooling((2, 2), (2, 2), name='pool1'),
For(
range(2), lambda i: [
Convolution2D((3, 3), 128, name='conv2_{}'.format(i)),
Activation(activation=relu, name='relu2_{}'.format(i)),
]),
MaxPooling((2, 2), (2, 2), name='pool2'),
For(
range(4), lambda i: [
Convolution2D((3, 3), 256, name='conv3_{}'.format(i)),
Activation(activation=relu, name='relu3_{}'.format(i)),
]),
MaxPooling((2, 2), (2, 2), name='pool3'),
For(
range(4), lambda i: [
Convolution2D((3, 3), 512, name='conv4_{}'.format(i)),
Activation(activation=relu, name='relu4_{}'.format(i)),
]),
MaxPooling((2, 2), (2, 2), name='pool4'),
For(
range(4), lambda i: [
Convolution2D((3, 3), 512, name='conv5_{}'.format(i)),
Activation(activation=relu, name='relu5_{}'.format(i)),
]),
MaxPooling((2, 2), (2, 2), name='pool5'),
Dense(4096, name='fc6'),
Activation(activation=relu, name='relu6'),
Dropout(0.5, name='drop6'),
Dense(4096, name='fc7'),
Activation(activation=relu, name='relu7'),
Dropout(0.5, name='drop7'),
Dense(num_classes, name='fc8')
])(input)
# loss and metric
ce = cross_entropy_with_softmax(z, label_var)
pe = classification_error(z, label_var)
pe5 = classification_error(z, label_var, topN=5)
log_number_of_parameters(z)
print()
return {
'feature': feature_var,
'label': label_var,
'ce': ce,
'pe': pe,
'pe5': pe5,
'output': z
}
from autcar import Trainer
from cntk.layers import Dense, Sequential, Activation, Convolution2D, MaxPooling, Dropout, BatchNormalization
from cntk import softmax, relu
input_folder_path = "src/ml/data/autcar_training"
output_folder_path = "src/ml/data/autcar_training_balanced"
image_width = 224
image_height = 168
trainer = Trainer(deeplearning_framework="cntk", image_height=image_height, image_width=image_width)
trainer.create_balanced_dataset(input_folder_path, output_folder_path=output_folder_path)
model = Sequential([
Convolution2D(filter_shape=(5,5), num_filters=32, strides=(1,1), pad=True, name="first_conv"),
BatchNormalization(map_rank=1),
Activation(relu),
MaxPooling(filter_shape=(3,3), strides=(2,2), name="first_max"),
Convolution2D(filter_shape=(3,3), num_filters=48, strides=(1,1), pad=True, name="second_conv"),
BatchNormalization(map_rank=1),
Activation(relu),
MaxPooling(filter_shape=(3,3), strides=(2,2), name="second_max"),
Convolution2D(filter_shape=(3,3), num_filters=64, strides=(1,1), pad=True, name="third_conv"),
BatchNormalization(map_rank=1),
Activation(relu),
MaxPooling(filter_shape=(3,3), strides=(2,2), name="third_max"),
Convolution2D(filter_shape=(5,5), num_filters=32, strides=(1,1), pad=True, name="fourth_conv"),
BatchNormalization(map_rank=1),
Activation(relu),
Dense(100, activation=relu),
Dropout(0.1),
Dense(12, activation=softmax)
