def debug_create_network(network_stack):
'''
Main function to visualize network when designing custom networks
Args:
network_stack (list): List of lists containing information on layers in the network
initializer (keras iniatialzer): Random wieght initializer for layers in the sub-network
Returns:
neural network: The required complete network
'''
count = []
for i in range(len(names)):
count.append(1)
G = nx.DiGraph()
G.add_node("Net", pos=(1, 1))
sequential_first = "data"
#sequential_second, count = get_layer_uid(network_stack[0], count);
count = []
for i in range(len(names)):
count.append(1)
position = 1
G.add_node(sequential_first, pos=(2, 1))
position += 1
max_width = 1
width = 0
for i in range(len(network_stack)):
if (type(network_stack[i]) == list):
is_block = True
if (type(network_stack[i][-1]) != list):
if (network_stack[i][-1]["name"] in ["add", "concatenate"]):
is_block = False
if (is_block):
G, count, sequential_second, position, _ = debug_create_block(
network_stack[i], count, G, sequential_first, position, 0)
sequential_first = sequential_second
else:
branch_end_points = []
branch_max_length = 0
branches = []
branch_net = []
if (max_width < len(network_stack[i]) - 2):
max_width = len(network_stack[i]) - 2
width = 0
for j in range(len(network_stack[i]) - 1):
branch_first = sequential_first
branch_position = position
if (width > 0):
if (column == width):
column += 2
else:
column = width
else:
column = (j + 1) * 2
for k in range(len(network_stack[i][j])):
if type(network_stack[i][j][k]) == list:
is_block2 = True
if (type(network_stack[i][j][k][-1]) != list):
if (network_stack[i][j][k][-1]["name"]
in ["add", "concatenate"]):
is_block2 = False
if (is_block2):
G, count, branch_second, branch_position, width = debug_create_block(
network_stack[i][j][k], count, G,
branch_first, branch_position,
column - 2) #j*2+width
else:
G, count, branch_second, branch_position, width = debug_create_block(
[network_stack[i][j][k]], count, G,
branch_first, branch_position,
column - 2) #j+k+width
branch_first = branch_second
else:
branch_second, count = get_layer_uid(
network_stack[i][j][k], count)
G.add_node(branch_second,
pos=(column, branch_position))
branch_position += 1
G.add_edge(branch_first, branch_second)
branch_first = branch_second
branch_max_length = max(branch_position,
branch_max_length)
if (k == len(network_stack[i][j]) - 1):
branch_end_points.append(branch_second)
position = branch_max_length
position += 1
max_width += width
sequential_second, count = get_layer_uid(
network_stack[i][-1], count)
G.add_node(sequential_second, pos=(2, position))
position += 1
for i in range(len(branch_end_points)):
G.add_edge(branch_end_points[i], sequential_second)
sequential_first = sequential_second
else:
sequential_second, count = get_layer_uid(network_stack[i], count)
G.add_node(sequential_second, pos=(2, position))
position += 1
G.add_edge(sequential_first, sequential_second)
sequential_first = sequential_second
max_width = max(max_width, width)
if (max_width == 1):
G.add_node("monk", pos=(3, position))
else:
G.add_node("monk", pos=(max_width + 3, position))
pos = nx.get_node_attributes(G, 'pos')
plt.figure(3, figsize=(16, 20 + position // 6))
nx.draw_networkx(G,
pos,
with_label=True,
font_size=16,
node_color="yellow",
node_size=100)
plt.savefig("graph.png")
def create_network(network_stack, current_in_shape, initializer):
'''
Main function to create network when designing custom networks
Args:
network_stack (list): List of lists containing information on layers in the network
initializer (keras iniatialzer): Random wieght initializer for layers in the sub-network
Returns:
neural network: The required complete network
'''
count = []
for i in range(len(names)):
count.append(1)
G = nx.DiGraph()
G.add_node("Net", pos=(1, 1))
sequential_first = "data"
if (len(current_in_shape) == 2):
c, w = current_in_shape
input1 = krl.Input(shape=(w, c))
elif (len(current_in_shape) == 3):
c, h, w = current_in_shape
input1 = krl.Input(shape=(h, w, c))
elif (len(current_in_shape) == 4):
c, d, h, w = current_in_shape
input1 = krl.Input(shape=(d, h, w, c))
out = input1
count = []
for i in range(len(names)):
count.append(1)
position = 1
G.add_node(sequential_first, pos=(2, 1))
position += 1
max_width = 1
width = 0
for i in range(len(network_stack)):
if (type(network_stack[i]) == list):
is_block = True
if (type(network_stack[i][-1]) != list):
if (network_stack[i][-1]["name"] in ["add", "concatenate"]):
is_block = False
if (is_block):
G, count, sequential_second, position, _, out, initializer = create_block(
network_stack[i], count, G, sequential_first, position, 0,
out, initializer)
#print(sequential_second, current_in_shape)
sequential_first = sequential_second
else:
branch_end_points = []
branch_max_length = 0
branches = []
branch_net = []
out_base = out
if (max_width < len(network_stack[i]) - 2):
max_width = len(network_stack[i]) - 2
width = 0
for j in range(len(network_stack[i]) - 1):
small_net = []
branch_first = sequential_first
branch_net.append(out_base)
branch_position = position
if (width > 0):
if (column == width):
column += 2
else:
column = width
else:
column = (j + 1) * 2
for k in range(len(network_stack[i][j])):
if type(network_stack[i][j][k]) == list:
is_block2 = True
if (type(network_stack[i][j][k][-1]) != list):
if (network_stack[i][j][k][-1]["name"]
in ["add", "concatenate"]):
is_block2 = False
if (is_block2):
G, count, branch_second, branch_position, width, branch_net[
j], initializer = create_block(
network_stack[i][j][k],
count,
G,
branch_first,
branch_position,
column - 2, #j+k+width
branch_net[j],
initializer)
else:
G, count, branch_second, branch_position, width, branch_net[
j], initializer = create_block(
[network_stack[i][j][k]], count, G,
branch_first, branch_position,
column - 2, branch_net[j], initializer)
#print(branch_second, current_in_shape_base)
branch_first = branch_second
small_net.append(branch_net[j])
else:
branch_second, count = get_layer_uid(
network_stack[i][j][k], count)
branch_net[j] = custom_model_get_layer(
network_stack[i][j][k],
initializer)(branch_net[j])
small_net.append(
custom_model_get_layer(network_stack[i][j][k],
initializer))
G.add_node(branch_second,
pos=(column, branch_position))
branch_position += 1
G.add_edge(branch_first, branch_second)
branch_first = branch_second
branch_max_length = max(branch_position,
branch_max_length)
if (k == len(network_stack[i][j]) - 1):
branch_end_points.append(branch_second)
branches.append(small_net)
position = branch_max_length
position += 1
max_width += width
sequential_second, count = get_layer_uid(
network_stack[i][-1], count)
if (network_stack[i][-1]["name"] == "concatenate"):
out = keras.layers.concatenate(branch_net)
else:
out = keras.layers.add(branch_net)
sequential_second, count = get_layer_uid(
network_stack[i][-1], count)
G.add_node(sequential_second, pos=(2, position))
position += 1
for i in range(len(branch_end_points)):
G.add_edge(branch_end_points[i], sequential_second)
sequential_first = sequential_second
else:
sequential_second, count = get_layer_uid(network_stack[i], count)
G.add_node(sequential_second, pos=(2, position))
out = custom_model_get_layer(network_stack[i], initializer)(out)
position += 1
G.add_edge(sequential_first, sequential_second)
sequential_first = sequential_second
net = keras.models.Model(inputs=input1, outputs=out)
max_width = max(max_width, width)
if (max_width == 1):
G.add_node("monk", pos=(3, position))
else:
G.add_node("monk", pos=(max_width + 3, position))
pos = nx.get_node_attributes(G, 'pos')
plt.figure(3, figsize=(12, 12 + position // 6))
nx.draw_networkx(G,
pos,
with_label=True,
font_size=16,
node_color="yellow",
node_size=100)
plt.savefig("graph.png")
return net
def debug_create_block(network_stack, count, G, sequential_first, position,
current_width):
'''
Recursively visualize sub-blocks when designing custom networks
Args:
network_stack (list): List of lists containing information on layers for the sub-branch in the network
count (dict): A dictionary mapping to a count of every type of layer in the network
G (directed graph): NetworkX object
sequential_first (str): NAme of the current input layer
position (int): Vertical position on the directed graph
current_width (int): Horizontal position on the directed graph
out (placeholder): Keras placeholder temporary input for this sub-network
initializer (keras iniatialzer): Random wieght initializer for layers in the sub-network
Returns:
neural network: The required sub-branch
directed graph: Updated directed graph
str: Name of the outermost layer in the sub-network
int: Vertical position of the outer most layer in the sub-network
int: Horizontal position of the outer most layer in the sub-network
'''
position += 1
max_width = current_width
for i in range(len(network_stack)):
if (type(network_stack[i]) == list):
is_block = True
if (type(network_stack[i][-1]) != list):
if (network_stack[i][-1]["name"] in ["add", "concatenate"]):
is_block = False
if (is_block):
G, count, sequential_second, position, _ = debug_create_block(
network_stack[i], count, G, sequential_first, position,
current_width) #0
sequential_first = sequential_second
else:
branch_end_points = []
branch_max_length = 0
branches = []
branch_net = []
#if(max_width < len(network_stack[i])-2):
# max_width = len(network_stack[i])-2;
max_width = current_width
width = current_width
for j in range(len(network_stack[i]) - 1):
branch_first = sequential_first
branch_position = position
column = max((j + 1) * 2 + current_width, width)
max_width = column
for k in range(len(network_stack[i][j])):
if type(network_stack[i][j][k]) == list:
is_block2 = True
if (type(network_stack[i][j][k][-1]) != list):
if (network_stack[i][j][k][-1]["name"]
in ["add", "concatenate"]):
is_block2 = False
if (is_block2):
G, count, branch_second, branch_position, width = debug_create_block(
network_stack[i][j][k], count, G,
branch_first, branch_position,
column - 2) #j+k+width, j*2+current_width
else:
G, count, branch_second, branch_position, width = debug_create_block(
[network_stack[i][j][k]], count, G,
branch_first, branch_position,
column - 2) #j+k+width, j+k+current_width
branch_first = branch_second
else:
branch_second, count = get_layer_uid(
network_stack[i][j][k], count)
G.add_node(branch_second,
pos=(column, branch_position))
branch_position += 1
G.add_edge(branch_first, branch_second)
branch_first = branch_second
branch_max_length = max(branch_position,
branch_max_length)
if (k == len(network_stack[i][j]) - 1):
branch_end_points.append(branch_second)
position = branch_max_length
position += 1
max_width += 2
sequential_second, count = get_layer_uid(
network_stack[i][-1], count)
G.add_node(sequential_second,
pos=(2 + current_width, position))
position += 1
for i in range(len(branch_end_points)):
G.add_edge(branch_end_points[i], sequential_second)
sequential_first = sequential_second
else:
sequential_second, count = get_layer_uid(network_stack[i], count)
G.add_node(sequential_second, pos=(2 + current_width, position))
position += 1
G.add_edge(sequential_first, sequential_second)
sequential_first = sequential_second
return G, count, sequential_second, position, max_width
def create_block(network_stack, count, G, sequential_first, position, current_width, out, initializer):
'''
Recursively create sub-blocks when designing custom networks
Args:
network_stack (list): List of lists containing information on layers for the sub-branch in the network
count (dict): A dictionary mapping to a count of every type of layer in the network
G (directed graph): NetworkX object
sequential_first (str): NAme of the current input layer
position (int): Vertical position on the directed graph
current_width (int): Horizontal position on the directed graph
out (placeholder): Keras placeholder temporary input for this sub-network
initializer (keras iniatialzer): Random wieght initializer for layers in the sub-network
Returns:
neural network: The required sub-branch
directed graph: Updated directed graph
str: Name of the outermost layer in the sub-network
int: Vertical position of the outer most layer in the sub-network
int: Horizontal position of the outer most layer in the sub-network
'''
position += 1;
max_width = current_width;
for i in range(len(network_stack)):
if(type(network_stack[i]) == list):
is_block = True;
if(type(network_stack[i][-1]) != list):
if(network_stack[i][-1]["name"] in ["add", "concatenate"]):
is_block=False;
if(is_block):
G, count, sequential_second, position, _, out, initializer = create_block(network_stack[i], count,
G, sequential_first, position, 0,
out, initializer)
#print(sequential_second, current_in_shape)
sequential_first = sequential_second
else:
branch_end_points = [];
branch_max_length = 0;
branches = [];
branch_net = [];
out_base = out;
#if(max_width < len(network_stack[i])-2):
# max_width = len(network_stack[i])-2;
max_width = current_width;
width = current_width;
for j in range(len(network_stack[i])-1):
small_net = [];
branch_net.append(out_base)
branch_first = sequential_first
branch_position = position
column = max((j+1)*2+current_width, width);
max_width = column;
for k in range(len(network_stack[i][j])):
if type(network_stack[i][j][k]) == list:
is_block2 = True;
if(type(network_stack[i][j][k][-1]) != list):
if(network_stack[i][j][k][-1]["name"] in ["add", "concatenate"]):
is_block2=False;
if(is_block2):
G, count, branch_second, branch_position, width, branch_net[j], initializer = create_block(network_stack[i][j][k],
count,
G,
branch_first,
branch_position,
column-2, #j+k+width
branch_net[j],
initializer)
else:
G, count, branch_second, branch_position, width, branch_net[j], initializer = create_block([network_stack[i][j][k]],
count,
G,
branch_first,
branch_position,
column-2,
branch_net[j],
initializer)
#print(branch_second, current_in_shape_base)
branch_first = branch_second
small_net.append(branch_net[j]);
else:
branch_second, count = get_layer_uid(network_stack[i][j][k], count);
branch_net[j] = custom_model_get_layer(network_stack[i][j][k], initializer)(branch_net[j]);
small_net.append(custom_model_get_layer(network_stack[i][j][k], initializer));
G.add_node(branch_second, pos=(column, branch_position));
branch_position += 1;
G.add_edge(branch_first, branch_second);
branch_first = branch_second;
branch_max_length = max(branch_position, branch_max_length)
if(k == len(network_stack[i][j])-1):
branch_end_points.append(branch_second);
branches.append(small_net);
position = branch_max_length;
position += 1;
max_width += 2;
sequential_second, count = get_layer_uid(network_stack[i][-1], count);
if(network_stack[i][-1]["name"] == "concatenate"):
out = keras.layers.concatenate(branch_net)
else:
out = keras.layers.add(branch_net)
G.add_node(sequential_second, pos=(2 + current_width, position));
position += 1;
for i in range(len(branch_end_points)):
G.add_edge(branch_end_points[i], sequential_second);
sequential_first = sequential_second;
else:
sequential_second, count = get_layer_uid(network_stack[i], count);
out = custom_model_get_layer(network_stack[i], initializer)(out);
G.add_node(sequential_second, pos=(2 + current_width, position))
position += 1;
G.add_edge(sequential_first, sequential_second);
sequential_first = sequential_second;
return G, count, sequential_second, position, max_width, out, initializer
def create_block(network_stack, count, G, sequential_first, position,
current_width, out, initializer):
position += 1
max_width = current_width
for i in range(len(network_stack)):
if (type(network_stack[i]) == list):
is_block = True
if (type(network_stack[i][-1]) != list):
if (network_stack[i][-1]["name"] in ["add", "concatenate"]):
is_block = False
if (is_block):
G, count, sequential_second, position, _, out, initializer = create_block(
network_stack[i], count, G, sequential_first, position, 0,
out, initializer)
#print(sequential_second, current_in_shape)
sequential_first = sequential_second
else:
branch_end_points = []
branch_max_length = 0
branches = []
branch_net = []
out_base = out
#if(max_width < len(network_stack[i])-2):
# max_width = len(network_stack[i])-2;
max_width = current_width
width = current_width
for j in range(len(network_stack[i]) - 1):
small_net = []
branch_net.append(out_base)
branch_first = sequential_first
branch_position = position
column = max((j + 1) * 2 + current_width, width)
max_width = column
for k in range(len(network_stack[i][j])):
if type(network_stack[i][j][k]) == list:
is_block2 = True
if (type(network_stack[i][j][k][-1]) != list):
if (network_stack[i][j][k][-1]["name"]
in ["add", "concatenate"]):
is_block2 = False
if (is_block2):
G, count, branch_second, branch_position, width, branch_net[
j], initializer = create_block(
network_stack[i][j][k],
count,
G,
branch_first,
branch_position,
column - 2, #j+k+width
branch_net[j],
initializer)
else:
G, count, branch_second, branch_position, width, branch_net[
j], initializer = create_block(
[network_stack[i][j][k]], count, G,
branch_first, branch_position,
column - 2, branch_net[j], initializer)
#print(branch_second, current_in_shape_base)
branch_first = branch_second
small_net.append(branch_net[j])
else:
branch_second, count = get_layer_uid(
network_stack[i][j][k], count)
branch_net[j] = custom_model_get_layer(
network_stack[i][j][k],
initializer)(branch_net[j])
small_net.append(
custom_model_get_layer(network_stack[i][j][k],
initializer))
G.add_node(branch_second,
pos=(column, branch_position))
branch_position += 1
G.add_edge(branch_first, branch_second)
branch_first = branch_second
branch_max_length = max(branch_position,
branch_max_length)
if (k == len(network_stack[i][j]) - 1):
branch_end_points.append(branch_second)
branches.append(small_net)
position = branch_max_length
position += 1
max_width += 2
sequential_second, count = get_layer_uid(
network_stack[i][-1], count)
if (network_stack[i][-1]["name"] == "concatenate"):
out = keras.layers.concatenate(branch_net)
else:
out = keras.layers.add(branch_net)
G.add_node(sequential_second,
pos=(2 + current_width, position))
position += 1
for i in range(len(branch_end_points)):
G.add_edge(branch_end_points[i], sequential_second)
sequential_first = sequential_second
else:
sequential_second, count = get_layer_uid(network_stack[i], count)
out = custom_model_get_layer(network_stack[i], initializer)(out)
G.add_node(sequential_second, pos=(2 + current_width, position))
position += 1
G.add_edge(sequential_first, sequential_second)
sequential_first = sequential_second
return G, count, sequential_second, position, max_width, out, initializer
def debug_create_block(network_stack, count, G, sequential_first, position,
current_width):
position += 1
max_width = current_width
for i in range(len(network_stack)):
if (type(network_stack[i]) == list):
is_block = True
if (type(network_stack[i][-1]) != list):
if (network_stack[i][-1]["name"] in ["add", "concatenate"]):
is_block = False
if (is_block):
G, count, sequential_second, position, _ = debug_create_block(
network_stack[i], count, G, sequential_first, position,
current_width) #0
sequential_first = sequential_second
else:
branch_end_points = []
branch_max_length = 0
branches = []
branch_net = []
#if(max_width < len(network_stack[i])-2):
# max_width = len(network_stack[i])-2;
max_width = current_width
width = current_width
for j in range(len(network_stack[i]) - 1):
branch_first = sequential_first
branch_position = position
column = max((j + 1) * 2 + current_width, width)
max_width = column
for k in range(len(network_stack[i][j])):
if type(network_stack[i][j][k]) == list:
is_block2 = True
if (type(network_stack[i][j][k][-1]) != list):
if (network_stack[i][j][k][-1]["name"]
in ["add", "concatenate"]):
is_block2 = False
if (is_block2):
G, count, branch_second, branch_position, width = debug_create_block(
network_stack[i][j][k], count, G,
branch_first, branch_position,
column - 2) #j+k+width, j*2+current_width
else:
G, count, branch_second, branch_position, width = debug_create_block(
[network_stack[i][j][k]], count, G,
branch_first, branch_position,
column - 2) #j+k+width, j+k+current_width
branch_first = branch_second
else:
branch_second, count = get_layer_uid(
network_stack[i][j][k], count)
G.add_node(branch_second,
pos=(column, branch_position))
branch_position += 1
G.add_edge(branch_first, branch_second)
branch_first = branch_second
branch_max_length = max(branch_position,
branch_max_length)
if (k == len(network_stack[i][j]) - 1):
branch_end_points.append(branch_second)
position = branch_max_length
position += 1
max_width += 2
sequential_second, count = get_layer_uid(
network_stack[i][-1], count)
G.add_node(sequential_second,
pos=(2 + current_width, position))
position += 1
for i in range(len(branch_end_points)):
G.add_edge(branch_end_points[i], sequential_second)
sequential_first = sequential_second
else:
sequential_second, count = get_layer_uid(network_stack[i], count)
G.add_node(sequential_second, pos=(2 + current_width, position))
position += 1
G.add_edge(sequential_first, sequential_second)
sequential_first = sequential_second
return G, count, sequential_second, position, max_width
def setup_model(system_dict):
if (system_dict["model"]["type"] == "pretrained"):
model_name = system_dict["model"]["params"]["model_name"]
use_pretrained = system_dict["model"]["params"]["use_pretrained"]
freeze_base_network = system_dict["model"]["params"][
"freeze_base_network"]
custom_network = system_dict["model"]["custom_network"]
final_layer = system_dict["model"]["final_layer"]
num_classes = system_dict["dataset"]["params"]["num_classes"]
input_size = system_dict["dataset"]["params"]["input_size"]
finetune_net, model_name = get_base_model(model_name, use_pretrained,
num_classes,
freeze_base_network,
input_size)
if (len(custom_network)):
if (final_layer):
finetune_net = create_final_layer(finetune_net, custom_network,
num_classes)
else:
msg = "Final layer not assigned"
raise ConstraintError(msg)
else:
x = finetune_net.output
x = krl.GlobalAveragePooling2D()(x)
x = krl.Dense(512)(x)
x = krl.ReLU()(x)
x = krl.Dropout(0.5)(x)
x = krl.Dense(num_classes)(x)
preds = krl.Softmax()(x)
finetune_net = keras.models.Model(inputs=finetune_net.input,
outputs=preds)
system_dict["local"]["model"] = finetune_net
return system_dict
else:
count = []
for i in range(len(names)):
count.append(1)
if (len(system_dict["dataset"]["params"]["data_shape"]) == 2):
c, w = system_dict["dataset"]["params"]["data_shape"]
input1 = krl.Input(shape=(w, c))
elif (len(system_dict["dataset"]["params"]["data_shape"]) == 3):
c, h, w = system_dict["dataset"]["params"]["data_shape"]
input1 = krl.Input(shape=(h, w, c))
elif (len(system_dict["dataset"]["params"]["data_shape"]) == 4):
c, d, h, w = system_dict["dataset"]["params"]["data_shape"]
input1 = krl.Input(shape=(d, h, w, c))
network_stack = system_dict["custom_model"]["network_stack"]
G = nx.DiGraph()
G.add_node("Net", pos=(1, 1))
sequential_first = "data"
sequential_second, count = get_layer_uid(network_stack[0], count)
count = []
for i in range(len(names)):
count.append(1)
position = 1
G.add_node(sequential_first, pos=(2, 1))
position += 1
max_width = 1
for i in range(len(network_stack)):
if (type(network_stack[i]) == list):
branch_end_points = []
branch_lengths = []
branches = []
branch_net = []
out_base = out
if (max_width < len(network_stack[i]) - 2):
max_width = len(network_stack[i]) - 2
for j in range(len(network_stack[i]) - 1):
small_net = []
branch_net.append(out_base)
branch_first = sequential_first
branch_position = position
column = j + 2
for k in range(len(network_stack[i][j])):
branch_second, count = get_layer_uid(
network_stack[i][j][k], count)
small_net.append(
custom_model_get_layer(
network_stack[i][j][k],
system_dict["custom_model"]
["network_initializer"]))
branch_net[j] = custom_model_get_layer(
network_stack[i][j][k], system_dict["custom_model"]
["network_initializer"])(branch_net[j])
G.add_node(branch_second,
pos=(column, branch_position))
branch_position += 1
G.add_edge(branch_first, branch_second)
branch_first = branch_second
if (k == len(network_stack[i][j]) - 1):
branch_end_points.append(branch_second)
branch_lengths.append(len(network_stack[i][j]))
branches.append(small_net)
position += max(branch_lengths)
position += 1
sequential_second, count = get_layer_uid(
network_stack[i][-1], count)
if (network_stack[i][-1]["name"] == "concatenate"):
out = keras.layers.concatenate(branch_net)
else:
out = keras.layers.add(branch_net)
G.add_node(sequential_second, pos=(2, position))
position += 1
for i in range(len(branch_end_points)):
G.add_edge(branch_end_points[i], sequential_second)
sequential_first = sequential_second
else:
sequential_second, count = get_layer_uid(
network_stack[i], count)
# Get layer here
if (i == 0):
out = custom_model_get_layer(
network_stack[i], system_dict["custom_model"]
["network_initializer"])(input1)
else:
out = custom_model_get_layer(
network_stack[i], system_dict["custom_model"]
["network_initializer"])(out)
G.add_node(sequential_second, pos=(2, position))
position += 1
G.add_edge(sequential_first, sequential_second)
sequential_first = sequential_second
net = keras.models.Model(inputs=input1, outputs=out)
if (max_width == 1):
G.add_node("monk", pos=(3, position))
else:
G.add_node("monk", pos=(max_width + 3, position))
pos = nx.get_node_attributes(G, 'pos')
plt.figure(3, figsize=(8, 12 + position // 6))
nx.draw_networkx(G,
pos,
with_label=True,
font_size=16,
node_color="yellow",
node_size=100)
plt.savefig("graph.png")
system_dict["local"]["model"] = net
return system_dict
def debug_custom_model(network_stack):
count = []
for i in range(len(names)):
count.append(1)
G = nx.DiGraph()
G.add_node("Net", pos=(1, 1))
sequential_first = "data"
sequential_second, count = get_layer_uid(network_stack[0], count)
count = []
for i in range(len(names)):
count.append(1)
position = 1
G.add_node(sequential_first, pos=(2, 1))
position += 1
max_width = 1
for i in range(len(network_stack)):
if (type(network_stack[i]) == list):
branch_end_points = []
branch_lengths = []
branches = []
branch_net = []
if (max_width < len(network_stack[i]) - 2):
max_width = len(network_stack[i]) - 2
for j in range(len(network_stack[i]) - 1):
branch_first = sequential_first
branch_position = position
column = j + 2
for k in range(len(network_stack[i][j])):
branch_second, count = get_layer_uid(
network_stack[i][j][k], count)
G.add_node(branch_second, pos=(column, branch_position))
branch_position += 1
G.add_edge(branch_first, branch_second)
branch_first = branch_second
if (k == len(network_stack[i][j]) - 1):
branch_end_points.append(branch_second)
branch_lengths.append(len(network_stack[i][j]))
position += max(branch_lengths)
position += 1
sequential_second, count = get_layer_uid(network_stack[i][-1],
count)
G.add_node(sequential_second, pos=(2, position))
position += 1
for i in range(len(branch_end_points)):
G.add_edge(branch_end_points[i], sequential_second)
sequential_first = sequential_second
else:
sequential_second, count = get_layer_uid(network_stack[i], count)
G.add_node(sequential_second, pos=(2, position))
position += 1
G.add_edge(sequential_first, sequential_second)
sequential_first = sequential_second
if (max_width == 1):
G.add_node("monk", pos=(3, position))
else:
G.add_node("monk", pos=(max_width + 3, position))
pos = nx.get_node_attributes(G, 'pos')
plt.figure(3, figsize=(8, 12 + position // 6))
nx.draw_networkx(G,
pos,
with_label=True,
font_size=16,
node_color="yellow",
node_size=100)
plt.savefig("graph.png")
