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This change uses tensorflow directly #31

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300 changes: 153 additions & 147 deletions ann_visualizer/visualize.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,180 +27,186 @@ def ann_viz(model, view=True, filename="network.gv", title="My Neural Network"):

title: A title for the graph
"""
from graphviz import Digraph;
import keras;
from keras.models import Sequential;
from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten;
import json;
input_layer = 0;
hidden_layers_nr = 0;
layer_types = [];
hidden_layers = [];
output_layer = 0;
from graphviz import Digraph
from tensorflow.python.keras.layers import Conv2D, MaxPooling2D, Activation, Dense
from tensorflow.python.keras.layers.core import Dropout, Flatten
# import keras
input_layer = 0
hidden_layers_nr = 0
layer_types = []
hidden_layers = []
output_layer = 0
for layer in model.layers:
if(layer == model.layers[0]):
input_layer = int(str(layer.input_shape).split(",")[1][1:-1]);
hidden_layers_nr += 1;
if (type(layer) == keras.layers.core.Dense):
hidden_layers.append(int(str(layer.output_shape).split(",")[1][1:-1]));
layer_types.append("Dense");
if layer == model.layers[0]:
input_layer = int(str(layer.input_shape).split(",")[1][1:-1])
hidden_layers_nr += 1
if type(layer) == Dense:
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Perhaps, rewrite this as:

Suggested change
if type(layer) == Dense:
if isinstance(Dense, layer):

hidden_layers.append(int(str(layer.output_shape).split(",")[1][1:-1]))
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This is a bit complex for noobs to read, would be nice to simplify it.

layer_types.append("Dense")
else:
hidden_layers.append(1);
if (type(layer) == keras.layers.convolutional.Conv2D):
layer_types.append("Conv2D");
elif (type(layer) == keras.layers.pooling.MaxPooling2D):
layer_types.append("MaxPooling2D");
elif (type(layer) == keras.layers.core.Dropout):
layer_types.append("Dropout");
elif (type(layer) == keras.layers.core.Flatten):
layer_types.append("Flatten");
elif (type(layer) == keras.layers.core.Activation):
layer_types.append("Activation");
hidden_layers.append(1)
if type(layer) == Conv2D:
layer_types.append("Conv2D")
elif type(layer) == MaxPooling2D:
layer_types.append("MaxPooling2D")
elif type(layer) == Dropout:
layer_types.append("Dropout")
elif type(layer) == Flatten:
layer_types.append("Flatten")
elif type(layer) == Activation:
Comment on lines +48 to +56
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Ditto!

layer_types.append("Activation")
else:
if(layer == model.layers[-1]):
output_layer = int(str(layer.output_shape).split(",")[1][1:-1]);
if (layer == model.layers[-1]):
output_layer = int(str(layer.output_shape).split(",")[1][1:-1])
else:
hidden_layers_nr += 1;
if (type(layer) == keras.layers.core.Dense):
hidden_layers.append(int(str(layer.output_shape).split(",")[1][1:-1]));
layer_types.append("Dense");
hidden_layers_nr += 1
if (type(layer) == Dense):
hidden_layers.append(int(str(layer.output_shape).split(",")[1][1:-1]))
layer_types.append("Dense")
else:
hidden_layers.append(1);
if (type(layer) == keras.layers.convolutional.Conv2D):
layer_types.append("Conv2D");
elif (type(layer) == keras.layers.pooling.MaxPooling2D):
layer_types.append("MaxPooling2D");
elif (type(layer) == keras.layers.core.Dropout):
layer_types.append("Dropout");
elif (type(layer) == keras.layers.core.Flatten):
layer_types.append("Flatten");
elif (type(layer) == keras.layers.core.Activation):
layer_types.append("Activation");
last_layer_nodes = input_layer;
nodes_up = input_layer;
if(type(model.layers[0]) != keras.layers.core.Dense):
last_layer_nodes = 1;
nodes_up = 1;
input_layer = 1;
hidden_layers.append(1)
if (type(layer) == Conv2D):
layer_types.append("Conv2D")
elif (type(layer) == MaxPooling2D):
layer_types.append("MaxPooling2D")
elif (type(layer) == Dropout):
layer_types.append("Dropout")
elif (type(layer) == Flatten):
layer_types.append("Flatten")
elif (type(layer) == Activation):
layer_types.append("Activation")
Comment on lines +68 to +77
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See: https://stackoverflow.com/questions/1549801/what-are-the-differences-between-type-and-isinstance?noredirect=1&lq=1

last_layer_nodes = input_layer
nodes_up = input_layer
if type(model.layers[0]) != Dense:
last_layer_nodes = 1
nodes_up = 1
input_layer = 1

g = Digraph('g', filename=filename);
n = 0;
g.graph_attr.update(splines="false", nodesep='1', ranksep='2');
#Input Layer
g = Digraph('g', filename=filename)
n = 0
g.graph_attr.update(splines="false", nodesep='1', ranksep='2')
# Input Layer
with g.subgraph(name='cluster_input') as c:
if(type(model.layers[0]) == keras.layers.core.Dense):
the_label = title+'\n\n\n\nInput Layer';
if (int(str(model.layers[0].input_shape).split(",")[1][1:-1]) > 10):
the_label += " (+"+str(int(str(model.layers[0].input_shape).split(",")[1][1:-1]) - 10)+")";
input_layer = 10;
if type(model.layers[0]) == Dense:
the_label = title + '\n\n\n\nInput Layer'
if int(str(model.layers[0].input_shape).split(",")[1][1:-1]) > 10:
the_label += " (+" + str(int(str(model.layers[0].input_shape).split(",")[1][1:-1]) - 10) + ")"
input_layer = 10
c.attr(color='white')
for i in range(0, input_layer):
n += 1;
c.node(str(n));
n += 1
c.node(str(n))
c.attr(label=the_label)
c.attr(rank='same');
c.node_attr.update(color="#2ecc71", style="filled", fontcolor="#2ecc71", shape="circle");
c.attr(rank='same')
c.node_attr.update(color="#2ecc71", style="filled", fontcolor="#2ecc71", shape="circle")

elif(type(model.layers[0]) == keras.layers.convolutional.Conv2D):
#Conv2D Input visualizing
the_label = title+'\n\n\n\nInput Layer';
c.attr(color="white", label=the_label);
c.node_attr.update(shape="square");
pxls = str(model.layers[0].input_shape).split(',');
clr = int(pxls[3][1:-1]);
elif (type(model.layers[0]) == Conv2D):
# Conv2D Input visualizing
the_label = title + '\n\n\n\nInput Layer'
c.attr(color="white", label=the_label)
c.node_attr.update(shape="square")
pxls = str(model.layers[0].input_shape).split(',')
clr = int(pxls[3][1:-1])
if (clr == 1):
clrmap = "Grayscale";
the_color = "black:white";
clrmap = "Grayscale"
the_color = "black:white"
elif (clr == 3):
clrmap = "RGB";
the_color = "#e74c3c:#3498db";
clrmap = "RGB"
the_color = "#e74c3c:#3498db"
else:
clrmap = "";
c.node_attr.update(fontcolor="white", fillcolor=the_color, style="filled");
n += 1;
c.node(str(n), label="Image\n"+pxls[1]+" x"+pxls[2]+" pixels\n"+clrmap, fontcolor="white");
clrmap = ""
c.node_attr.update(fontcolor="white", fillcolor=the_color, style="filled")
n += 1
c.node(str(n), label="Image\n" + pxls[1] + " x" + pxls[2] + " pixels\n" + clrmap, fontcolor="white")
else:
raise ValueError("ANN Visualizer: Layer not supported for visualizing");
raise ValueError("ANN Visualizer: Layer not supported for visualizing")
for i in range(0, hidden_layers_nr):
with g.subgraph(name="cluster_"+str(i+1)) as c:
with g.subgraph(name="cluster_" + str(i + 1)) as c:
if (layer_types[i] == "Dense"):
c.attr(color='white');
c.attr(rank='same');
#If hidden_layers[i] > 10, dont include all
the_label = "";
c.attr(color='white')
c.attr(rank='same')
# If hidden_layers[i] > 10, dont include all
the_label = ""
if (int(str(model.layers[i].output_shape).split(",")[1][1:-1]) > 10):
the_label += " (+"+str(int(str(model.layers[i].output_shape).split(",")[1][1:-1]) - 10)+")";
hidden_layers[i] = 10;
c.attr(labeljust="right", labelloc="b", label=the_label);
the_label += " (+" + str(int(str(model.layers[i].output_shape).split(",")[1][1:-1]) - 10) + ")"
hidden_layers[i] = 10
c.attr(labeljust="right", labelloc="b", label=the_label)
for j in range(0, hidden_layers[i]):
n += 1;
c.node(str(n), shape="circle", style="filled", color="#3498db", fontcolor="#3498db");
for h in range(nodes_up - last_layer_nodes + 1 , nodes_up + 1):
g.edge(str(h), str(n));
last_layer_nodes = hidden_layers[i];
nodes_up += hidden_layers[i];
n += 1
c.node(str(n), shape="circle", style="filled", color="#3498db", fontcolor="#3498db")
for h in range(nodes_up - last_layer_nodes + 1, nodes_up + 1):
g.edge(str(h), str(n))
last_layer_nodes = hidden_layers[i]
nodes_up += hidden_layers[i]
elif (layer_types[i] == "Conv2D"):
c.attr(style='filled', color='#5faad0');
n += 1;
kernel_size = str(model.layers[i].get_config()['kernel_size']).split(',')[0][1] + "x" + str(model.layers[i].get_config()['kernel_size']).split(',')[1][1 : -1];
filters = str(model.layers[i].get_config()['filters']);
c.node("conv_"+str(n), label="Convolutional Layer\nKernel Size: "+kernel_size+"\nFilters: "+filters, shape="square");
c.node(str(n), label=filters+"\nFeature Maps", shape="square");
g.edge("conv_"+str(n), str(n));
for h in range(nodes_up - last_layer_nodes + 1 , nodes_up + 1):
g.edge(str(h), "conv_"+str(n));
last_layer_nodes = 1;
nodes_up += 1;
c.attr(style='filled', color='#5faad0')
n += 1
kernel_size = str(model.layers[i].get_config()['kernel_size']).split(',')[0][1] + "x" + \
str(model.layers[i].get_config()['kernel_size']).split(',')[1][1: -1]
filters = str(model.layers[i].get_config()['filters'])
c.node("conv_" + str(n),
label="Convolutional Layer\nKernel Size: " + kernel_size + "\nFilters: " + filters,
shape="square")
c.node(str(n), label=filters + "\nFeature Maps", shape="square")
g.edge("conv_" + str(n), str(n))
for h in range(nodes_up - last_layer_nodes + 1, nodes_up + 1):
g.edge(str(h), "conv_" + str(n))
last_layer_nodes = 1
nodes_up += 1
elif (layer_types[i] == "MaxPooling2D"):
c.attr(color="white");
n += 1;
pool_size = str(model.layers[i].get_config()['pool_size']).split(',')[0][1] + "x" + str(model.layers[i].get_config()['pool_size']).split(',')[1][1 : -1];
c.node(str(n), label="Max Pooling\nPool Size: "+pool_size, style="filled", fillcolor="#8e44ad", fontcolor="white");
for h in range(nodes_up - last_layer_nodes + 1 , nodes_up + 1):
g.edge(str(h), str(n));
last_layer_nodes = 1;
nodes_up += 1;
c.attr(color="white")
n += 1
pool_size = str(model.layers[i].get_config()['pool_size']).split(',')[0][1] + "x" + \
str(model.layers[i].get_config()['pool_size']).split(',')[1][1: -1]
c.node(str(n), label="Max Pooling\nPool Size: " + pool_size, style="filled", fillcolor="#8e44ad",
fontcolor="white")
for h in range(nodes_up - last_layer_nodes + 1, nodes_up + 1):
g.edge(str(h), str(n))
last_layer_nodes = 1
nodes_up += 1
elif (layer_types[i] == "Flatten"):
n += 1;
c.attr(color="white");
c.node(str(n), label="Flattening", shape="invtriangle", style="filled", fillcolor="#2c3e50", fontcolor="white");
for h in range(nodes_up - last_layer_nodes + 1 , nodes_up + 1):
g.edge(str(h), str(n));
last_layer_nodes = 1;
nodes_up += 1;
n += 1
c.attr(color="white")
c.node(str(n), label="Flattening", shape="invtriangle", style="filled", fillcolor="#2c3e50",
fontcolor="white")
for h in range(nodes_up - last_layer_nodes + 1, nodes_up + 1):
g.edge(str(h), str(n))
last_layer_nodes = 1
nodes_up += 1
elif (layer_types[i] == "Dropout"):
n += 1;
c.attr(color="white");
c.node(str(n), label="Dropout Layer", style="filled", fontcolor="white", fillcolor="#f39c12");
for h in range(nodes_up - last_layer_nodes + 1 , nodes_up + 1):
g.edge(str(h), str(n));
last_layer_nodes = 1;
nodes_up += 1;
n += 1
c.attr(color="white")
c.node(str(n), label="Dropout Layer", style="filled", fontcolor="white", fillcolor="#f39c12")
for h in range(nodes_up - last_layer_nodes + 1, nodes_up + 1):
g.edge(str(h), str(n))
last_layer_nodes = 1
nodes_up += 1
elif (layer_types[i] == "Activation"):
n += 1;
c.attr(color="white");
fnc = model.layers[i].get_config()['activation'];
c.node(str(n), shape="octagon", label="Activation Layer\nFunction: "+fnc, style="filled", fontcolor="white", fillcolor="#00b894");
for h in range(nodes_up - last_layer_nodes + 1 , nodes_up + 1):
g.edge(str(h), str(n));
last_layer_nodes = 1;
nodes_up += 1;

n += 1
c.attr(color="white")
fnc = model.layers[i].get_config()['activation']
c.node(str(n), shape="octagon", label="Activation Layer\nFunction: " + fnc, style="filled",
fontcolor="white", fillcolor="#00b894")
for h in range(nodes_up - last_layer_nodes + 1, nodes_up + 1):
g.edge(str(h), str(n))
last_layer_nodes = 1
nodes_up += 1

with g.subgraph(name='cluster_output') as c:
if (type(model.layers[-1]) == keras.layers.core.Dense):
if (type(model.layers[-1]) == Dense):
c.attr(color='white')
c.attr(rank='same');
c.attr(labeljust="1");
for i in range(1, output_layer+1):
n += 1;
c.node(str(n), shape="circle", style="filled", color="#e74c3c", fontcolor="#e74c3c");
for h in range(nodes_up - last_layer_nodes + 1 , nodes_up + 1):
g.edge(str(h), str(n));
c.attr(rank='same')
c.attr(labeljust="1")
for i in range(1, output_layer + 1):
n += 1
c.node(str(n), shape="circle", style="filled", color="#e74c3c", fontcolor="#e74c3c")
for h in range(nodes_up - last_layer_nodes + 1, nodes_up + 1):
g.edge(str(h), str(n))
c.attr(label='Output Layer', labelloc="bottom")
c.node_attr.update(color="#2ecc71", style="filled", fontcolor="#2ecc71", shape="circle");
c.node_attr.update(color="#2ecc71", style="filled", fontcolor="#2ecc71", shape="circle")

g.attr(arrowShape="none");
g.edge_attr.update(arrowhead="none", color="#707070");
g.attr(arrowShape="none")
g.edge_attr.update(arrowhead="none", color="#707070")
if view == True:
g.view();
g.view()