def make_model():
model = Sequential()
model.add(Input(shape=input_shape))
model.add(Dense(4096))
model.add(ReLU())
model.add(Dense(4096))
model.add(ReLU())
model.add(Dense(4096))
model.add(ReLU())
model.add(Dense(4096))
model.add(ReLU())
model.add(Dense(4096))
model.add(ReLU())
model.add(Dense(10))
model.add(Softmax())
model.summary()
model.compile(Adam(), 'categorical_crossentropy', 'accuracy')
return model
def make_model():
model = Sequential()
model.add(Input(shape=input_shape))
model.add(Conv2D(32, kernel_size=(3, 3), padding='same'))
model.add(ReLU())
model.add(Dropout(0.3))
model.add(Conv2D(32, kernel_size=(3, 3), padding='same'))
model.add(ReLU())
model.add(MaxPooling2D(2, 2))
model.add(Conv2D(64, kernel_size=(3, 3), padding='same'))
model.add(ReLU())
model.add(Dropout(0.3))
model.add(Conv2D(64, kernel_size=(3, 3), padding='same'))
model.add(ReLU())
model.add(MaxPooling2D(2, 2))
model.add(Conv2D(128, kernel_size=(3, 3), padding='same'))
model.add(ReLU())
model.add(Dropout(0.3))
model.add(Conv2D(128, kernel_size=(3, 3), padding='same'))
model.add(ReLU())
model.add(MaxPooling2D(2, 2))
model.add(Flatten())
model.add(Dropout(0.3))
model.add(Dense(2500, kernel_initializer='He'))
model.add(ReLU())
model.add(Dropout(0.3))
model.add(Dense(1500, kernel_initializer='He'))
model.add(ReLU())
model.add(Dropout(0.3))
model.add(Dense(10, kernel_initializer='He'))
model.add(Softmax())
model.summary()
model.compile(Adam(), 'categorical_crossentropy', 'accuracy')
return model
def make_my_yolo(input_shape, num_anchors, num_classes):
ori_c, ori_w, ori_h = input_shape
model = Sequential()
model.add(Input(shape=input_shape))
add_conv2d_bn_leaky(model, 32, 3)
model.add(MaxPooling2D(2, 2))
add_conv2d_bn_leaky(model, 32, 3)
model.add(MaxPooling2D(2, 2))
add_conv2d_bn_leaky(model, 64, 3)
add_conv2d_bn_leaky(model, 64, 3)
model.add(MaxPooling2D(2, 2))
add_conv2d_bn_leaky(model, 64, 3)
add_conv2d_bn_leaky(model, 64, 3)
model.add(MaxPooling2D(2, 2))
add_conv2d_bn_leaky(model, 128, 3)
add_conv2d_bn_leaky(model, 128, 3)
model.add(MaxPooling2D(2, 2))
add_conv2d_bn_leaky(model, 128, 3)
add_conv2d_bn_leaky(model, 128, 3)
add_conv2d_bn_leaky(model, 128, 3)
model.add(Conv2D(num_anchors * (4 + 1 + num_classes), 1, 1))
model.add(Reshape((num_anchors, (4 + 1 + num_classes), ori_w // 32, ori_h // 32)))
model.add(Transpose((0, 1, 3, 4, 2)))
mask = np.ones((num_anchors, 7, 7, 7), dtype=bool)
mask[..., 2] = False
mask[..., 3] = False
model.add(MaskedSigmoid(mask))
return model
def make_darknet19(input_shape):
model = Sequential()
model.add(Input(shape=input_shape))
add_conv2d_bn_leaky(model, 32, 3)
model.add(MaxPooling2D(2, 2))
add_conv2d_bn_leaky(model, 64, 3)
model.add(MaxPooling2D(2, 2))
add_conv2d_bn_leaky(model, 128, 3)
add_conv2d_bn_leaky(model, 64, 1)
add_conv2d_bn_leaky(model, 128, 3)
model.add(MaxPooling2D(2, 2))
add_conv2d_bn_leaky(model, 256, 3)
add_conv2d_bn_leaky(model, 128, 1)
add_conv2d_bn_leaky(model, 256, 3)
model.add(MaxPooling2D(2, 2))
add_conv2d_bn_leaky(model, 512, 3)
add_conv2d_bn_leaky(model, 256, 1)
add_conv2d_bn_leaky(model, 512, 3)
add_conv2d_bn_leaky(model, 256, 1)
add_conv2d_bn_leaky(model, 512, 3)
model.add(MaxPooling2D(2, 2))
add_conv2d_bn_leaky(model, 1024, 3)
add_conv2d_bn_leaky(model, 512, 1)
add_conv2d_bn_leaky(model, 1024, 3)
add_conv2d_bn_leaky(model, 512, 1)
add_conv2d_bn_leaky(model, 1024, 3)
add_conv2d_bn_leaky(model, 1000, 1)
model.add(Conv2D(5 * (4 + 1 + 1), 1, 1))
return model