def make_simplenet(model_path):
cnn = Model(model_path)
cnn.build_graph(
image_shape=(32, 32, 1),
n_classes=10,
layers=[
Conv2D(64, (3, 3), activation='relu', batch_normal=0.95), #1
Conv2D(32, (3, 3), activation='relu', batch_normal=0.95), #2
Conv2D(32, (3, 3), activation='relu', batch_normal=0.95), #3
Conv2D(32, (3, 3), activation='relu', batch_normal=0.95), #4
MaxPooling2D((2, 2)),
Conv2D(32, (3, 3), activation='relu', batch_normal=0.95), #5
Conv2D(32, (3, 3), activation='relu', batch_normal=0.95), #6
Conv2D(64, (3, 3), activation='relu', batch_normal=0.95), #7
MaxPooling2D((2, 2)),
Conv2D(64, (3, 3), activation='relu', batch_normal=0.95), #8
Conv2D(64, (3, 3), activation='relu', batch_normal=0.95), #9
MaxPooling2D((2, 2)),
Conv2D(128, (3, 3), activation='relu', batch_normal=0.95), #10
Conv2D(256, (1, 1), activation='relu', batch_normal=0.95), #11
Conv2D(64, (1, 1), activation='relu', batch_normal=0.95), #12
MaxPooling2D((2, 2)),
Conv2D(64, (3, 3), activation='relu', batch_normal=0.95), #13
MaxPooling2D((2, 2)),
Flatten(),
Dense(10),
],
alpha=1e-3,
)
return cnn
def make_lr(model_path):
lr = Model(model_path)
lr.build_graph(image_shape=(32, 32, 1),
n_classes=10,
layers=[
Flatten(),
Dense(10),
],
alpha=1e-3)
return lr
def make_nn(model_path):
nn = Model(model_path)
nn.build_graph(
image_shape=(32, 32, 1),
n_classes=10,
layers=[
Flatten(),
Dense(32 * 32, activation='relu'),
Dense(10),
],
alpha=1e-4,
)
return nn
def make_simplenet_dropout(model_path):
cnn = Model(model_path)
cnn.build_graph(
image_shape=(32, 32, 1),
n_classes=10,
layers=[
Conv2D(64, (3, 3), activation='relu', batch_normal=0.95), #1
Dropout(0.8),
Conv2D(32, (3, 3), activation='relu', batch_normal=0.95), #2
Dropout(0.8),
Conv2D(32, (3, 3), activation='relu', batch_normal=0.95), #3
Dropout(0.8),
Conv2D(32, (3, 3), activation='relu', batch_normal=0.95), #4
Dropout(0.8),
MaxPooling2D((2, 2)),
Conv2D(32, (3, 3), activation='relu', batch_normal=0.95), #5
Dropout(0.8),
Conv2D(32, (3, 3), activation='relu', batch_normal=0.95), #6
Dropout(0.8),
Conv2D(64, (3, 3), activation='relu', batch_normal=0.95), #7
Dropout(0.8),
MaxPooling2D((2, 2)),
Conv2D(64, (3, 3), activation='relu', batch_normal=0.95), #8
Dropout(0.8),
Conv2D(64, (3, 3), activation='relu', batch_normal=0.95), #9
Dropout(0.8),
MaxPooling2D((2, 2)),
Conv2D(128, (3, 3), activation='relu', batch_normal=0.95), #10
Dropout(0.8),
Conv2D(256, (1, 1), activation='relu', batch_normal=0.95), #11
Dropout(0.8),
Conv2D(64, (1, 1), activation='relu', batch_normal=0.95), #12
Dropout(0.8),
MaxPooling2D((2, 2)),
Conv2D(64, (3, 3), activation='relu', batch_normal=0.95), #13
Dropout(0.8),
MaxPooling2D((2, 2)),
Flatten(),
Dense(10),
],
alpha=1e-3,
)
return cnn
def make_cnn(model_path):
cnn = Model(model_path)
cnn.build_graph(
image_shape=(32, 32, 1),
n_classes=10,
layers=[
Conv2D(32, (3, 3), activation='relu'),
BatchNorm(),
MaxPooling2D((2, 2)),
Flatten(),
Dense(128, activation='relu'),
Dropout(0.5),
Dense(10),
],
alpha=1e-3,
)
return cnn
