def convert_drawer_model(model):
_input_shape = model.input_shape
figure = Model(input_shape=_input_shape[1:])
for config in model.get_config()["layers"]:
class_name = config.get("class_name", False)
class_config = config.get("config", False)
if class_name and class_config:
class_obj = is_class_object(class_name)
if class_name == "Conv2D":
conv_2d = get_conv2d_obj(class_obj, class_config)
figure.add(conv_2d)
elif class_name == "MaxPooling2D":
max_pooling_2d = get_maxpooling2d_obj(class_obj, class_config)
figure.add(max_pooling_2d)
elif class_name == "Dense":
dense = get_dense_obj(class_obj, class_config)
figure.add(dense)
else:
figure.add(class_obj())
else:
raise ValueError
return figure
def main():
model = Model(input_shape=(224, 224, 3))
model.add(Conv2D(64, (3, 3), padding="same"))
model.add(Conv2D(64, (3, 3), padding="same"))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(128, (3, 3), padding="same"))
model.add(Conv2D(128, (3, 3), padding="same"))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(256, (3, 3), padding="same"))
model.add(Conv2D(256, (3, 3), padding="same"))
model.add(Conv2D(256, (3, 3), padding="same"))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(512, (3, 3), padding="same"))
model.add(Conv2D(512, (3, 3), padding="same"))
model.add(Conv2D(512, (3, 3), padding="same"))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(512, (3, 3), padding="same"))
model.add(Conv2D(512, (3, 3), padding="same"))
model.add(Conv2D(512, (3, 3), padding="same"))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(4096))
model.add(Dense(4096))
model.add(Dense(1000))
model.save_fig(os.path.splitext(os.path.basename(__file__))[0] + ".svg")
save_model_to_pptx(
model,
os.path.splitext(os.path.basename(__file__))[0] + ".pptx")
def main():
config.text_size = 16
model = Model(input_shape=(227, 227, 3))
model.add(Conv2D(96, (11, 11), (4, 4)))
model.add(MaxPooling2D((3, 3), strides=(2, 2)))
model.add(Conv2D(256, (5, 5), padding="same"))
model.add(MaxPooling2D((3, 3), strides=(2, 2)))
model.add(Conv2D(384, (3, 3), padding="same"))
model.add(Conv2D(384, (3, 3), padding="same"))
model.add(Conv2D(256, (3, 3), padding="same"))
model.add(MaxPooling2D((3, 3), strides=(2, 2)))
model.add(Flatten())
model.add(Dense(4096))
model.add(Dense(4096))
model.add(Dense(1000))
save_model_to_file(
model,
os.path.splitext(os.path.basename(__file__))[0] + ".pdf")
def create_model():
model = Model(input_shape=(256, 256, 3))
#16
model.add(Conv2D(16, (3, 3), (1, 1), padding='same'))
model.add(Conv2D(16, (3, 3), (1, 1), padding='same'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
#32
model.add(Conv2D(32, (3, 3), (1, 1), padding='same'))
model.add(Conv2D(32, (3, 3), (1, 1), padding='same'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
#64
model.add(Conv2D(64, (3, 3), (1, 1), padding='same'))
model.add(Conv2D(64, (3, 3), (1, 1), padding='same'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
#96
model.add(Conv2D(96, (3, 3), (1, 1), padding='same'))
model.add(Conv2D(96, (3, 3), (1, 1), padding='same'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
#128
model.add(Conv2D(128, (3, 3), (1, 1), padding='same'))
model.add(Conv2D(128, (3, 3), (1, 1), padding='same'))
model.add(MaxPooling2D((2, 2), strides=(2, 2)))
model.add(Flatten())
model.add(Dense(96))
model.add(Dense(5))
# save as svg file
model.save_fig("example5.svg")
def convert_drawer_model(model):
_input_shape = model.input_shape
# figure = Model(input_shape=_input_shape[1:])
# CNN Model
if len(_input_shape) == 4:
figure = Model(input_shape=_input_shape[1:])
# MLP Model
else:
dims = int(np.sqrt(_input_shape[1]))
_input_shape = (1,dims, dims,1)
figure = Model(input_shape=_input_shape[1:])
figure.add(Flatten())
for config in model.get_config()["layers"]:
class_name = config.get("class_name", False)
class_config = config.get("config", False)
print(class_name,class_config)
if class_name and class_config:
# class_obj = is_class_object(class_name)
print(class_name)
if class_name == "Conv2D":
class_obj = is_class_object(class_name)
conv_2d = get_conv2d_obj(class_obj, class_config)
figure.add(conv_2d)
elif class_name == "MaxPooling2D":
class_obj = is_class_object(class_name)
max_pooling_2d = get_maxpooling2d_obj(class_obj, class_config)
figure.add(max_pooling_2d)
elif class_name == "AveragePooling2D":
class_obj = is_class_object(class_name)
avg_pooling_2d = get_avgpooling2d_obj(class_obj, class_config)
figure.add(avg_pooling_2d)
elif class_name == "Dense":
class_obj = is_class_object(class_name)
dense = get_dense_obj(class_obj, class_config)
figure.add(dense)
elif class_name == "Activation":
pass
elif class_name == "Dropout":
pass
elif class_name == "BatchNormalizationV1":
pass
elif class_name == "BatchNormalization":
pass
elif class_name == "InputLayer":
pass
elif class_name == "Sequential":
pass
elif class_name == "Add":
pass
else:
class_obj = is_class_object(class_name)
figure.add(class_obj())
else:
raise ValueError
return figure
def main():
config.channel_scale = 2 / 3
model = Model(input_shape=(256, 256, 3))
model.add(Conv2D(16, (3, 3), (2, 2), padding="same"))
model.add(Conv2D(32, (3, 3), (2, 2), padding="same"))
model.add(Conv2D(64, (3, 3), (2, 2), padding="same"))
model.add(Conv2D(128, (3, 3), (2, 2), padding="same"))
model.add(Conv2D(256, (3, 3), (2, 2), padding="same"))
model.add(Deconv2D(128, (3, 3), (2, 2), padding="same"))
model.add(Deconv2D(64, (3, 3), (2, 2), padding="same"))
model.add(Deconv2D(32, (3, 3), (2, 2), padding="same"))
model.add(Deconv2D(16, (3, 3), (2, 2), padding="same"))
model.add(Deconv2D(3, (3, 3), (2, 2), padding="same"))
model.save_fig(os.path.splitext(os.path.basename(__file__))[0] + ".svg")
save_model_to_pptx(
model,
os.path.splitext(os.path.basename(__file__))[0] + ".pptx")
def main():
configure(inter_layer_margin=65, channel_scale=4 / 5)
model = Model(input_shape=(32, 32, 1))
model.add(Conv2D(6, (5, 5), (1, 1)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(16, (5, 5)))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(120))
model.add(Dense(84))
model.add(Dense(10))
model.save_fig(os.path.splitext(os.path.basename(__file__))[0] + ".svg")
save_model_to_pptx(
model,
os.path.splitext(os.path.basename(__file__))[0] + ".pptx")
def convert_drawer_model(model):
_input_shape = model.input_shape
figure = Model(input_shape=_input_shape[1:])
prev_class_obj = None
prev_class_config = None
for i, config in enumerate(model.get_config()["layers"]):
class_name = config.get("class_name", False)
class_config = config.get("config", False)
if class_name and class_config:
class_obj = is_class_object(class_name)
if class_name == "Conv2D":
conv_2d = get_conv2d_obj(class_obj, class_config)
if prev_class_obj:
dropout = get_dropout_obj(prev_class_obj,
prev_class_config, class_config)
figure.add(dropout)
figure.add(conv_2d)
prev_class_obj = None # reset
prev_class_config = None
elif class_name == "MaxPooling2D":
max_pooling_2d = get_maxpooling2d_obj(class_obj, class_config)
figure.add(max_pooling_2d)
elif class_name == "Dense":
next_activation_layer_config = None
if verbose:
next_activation_layer = model.get_config()["layers"][i + 1]
if next_activation_layer["class_name"] == "Activation":
next_activation_layer_config = next_activation_layer[
"config"]
dense = get_dense_obj(class_obj,
class_config,
next_activation_layer_config=
next_activation_layer_config)
else:
dense = get_dense_obj(class_obj, class_config)
figure.add(dense)
elif class_name == "Dropout":
prev_class_obj = class_obj
prev_class_config = class_config
elif class_name == "Activation":
pass
else:
figure.add(class_obj())
else:
raise ValueError
return figure
from convnet_drawer import Model, Conv2D, MaxPooling2D, Flatten, Dense
model = Model(input_shape=(50, 50, 3))
model.add(Conv2D(32, (3, 3), padding="same"))
model.add(Conv2D(32, (3, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Conv2D(64, (3, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512))
model.add(Dense(4))
model.save_fig("example.svg")
def main():
model = Model(input_shape=(224, 224, 3))
model.add(Conv2D(96, (7, 7), strides=(2, 2)))
model.add(MaxPooling2D((3, 3), strides=(2, 2)))
model.add(Conv2D(256, (5, 5), strides=(2, 2), padding="same"))
model.add(MaxPooling2D((3, 3), strides=(2, 2)))
model.add(Conv2D(384, (3, 3), padding="same"))
model.add(Conv2D(384, (3, 3), padding="same"))
model.add(Conv2D(256, (3, 3), padding="same"))
model.add(MaxPooling2D((3, 3), strides=(2, 2)))
model.add(Flatten())
model.add(Dense(4096))
model.add(Dense(4096))
model.add(Dense(1000))
model.save_fig(os.path.splitext(os.path.basename(__file__))[0] + ".svg")