Book a Demo!
CoCalc Logo Icon
StoreFeaturesDocsShareSupportNewsAboutPoliciesSign UpSign In
Download

📚 The CoCalc Library - books, templates and other resources

132923 views
License: OTHER
Tweet
Share
Share
1


2


3
def plot_logistic_regression_graph():

4
    import graphviz

5
    lr_graph = graphviz.Digraph(node_attr={'shape': 'circle', 'fixedsize': 'True'},

6
                                graph_attr={'rankdir': 'LR', 'splines': 'line'})

7
    inputs = graphviz.Digraph(node_attr={'shape': 'circle'}, name="cluster_0")

8
    output = graphviz.Digraph(node_attr={'shape': 'circle'}, name="cluster_2")

9


10
    for i in range(4):

11
        inputs.node("x[%d]" % i, labelloc="c")

12
    inputs.body.append('label = "inputs"')

13
    inputs.body.append('color = "white"')

14


15
    lr_graph.subgraph(inputs)

16


17
    output.body.append('label = "output"')

18
    output.body.append('color = "white"')

19
    output.node("y")

20


21
    lr_graph.subgraph(output)

22


23
    for i in range(4):

24
        lr_graph.edge("x[%d]" % i, "y", label="w[%d]" % i)

25
    return lr_graph

26


27


28
def plot_single_hidden_layer_graph():

29
    import graphviz

30
    nn_graph = graphviz.Digraph(node_attr={'shape': 'circle', 'fixedsize': 'True'},

31
                                graph_attr={'rankdir': 'LR', 'splines': 'line'})

32


33
    inputs = graphviz.Digraph(node_attr={'shape': 'circle'}, name="cluster_0")

34
    hidden = graphviz.Digraph(node_attr={'shape': 'circle'}, name="cluster_1")

35
    output = graphviz.Digraph(node_attr={'shape': 'circle'}, name="cluster_2")

36


37
    for i in range(4):

38
        inputs.node("x[%d]" % i)

39


40
    inputs.body.append('label = "inputs"')

41
    inputs.body.append('color = "white"')

42


43
    hidden.body.append('label = "hidden layer"')

44
    hidden.body.append('color = "white"')

45


46
    for i in range(3):

47
        hidden.node("h%d" % i, label="h[%d]" % i)

48


49
    output.node("y")

50
    output.body.append('label = "output"')

51
    output.body.append('color = "white"')

52


53
    nn_graph.subgraph(inputs)

54
    nn_graph.subgraph(hidden)

55
    nn_graph.subgraph(output)

56


57
    for i in range(4):

58
        for j in range(3):

59
            nn_graph.edge("x[%d]" % i, "h%d" % j)

60


61
    for i in range(3):

62
        nn_graph.edge("h%d" % i, "y")

63
    return nn_graph

64


65


66
def plot_two_hidden_layer_graph():

67
    import graphviz

68
    nn_graph = graphviz.Digraph(node_attr={'shape': 'circle', 'fixedsize': 'True'},

69
                                graph_attr={'rankdir': 'LR', 'splines': 'line'})

70


71
    inputs = graphviz.Digraph(node_attr={'shape': 'circle'}, name="cluster_0")

72
    hidden = graphviz.Digraph(node_attr={'shape': 'circle'}, name="cluster_1")

73
    hidden2 = graphviz.Digraph(node_attr={'shape': 'circle'}, name="cluster_2")

74


75
    output = graphviz.Digraph(node_attr={'shape': 'circle'}, name="cluster_3")

76


77
    for i in range(4):

78
        inputs.node("x[%d]" % i)

79


80
    inputs.body.append('label = "inputs"')

81
    inputs.body.append('color = "white"')

82


83
    for i in range(3):

84
        hidden.node("h1[%d]" % i)

85


86
    for i in range(3):

87
        hidden2.node("h2[%d]" % i)

88


89
    hidden.body.append('label = "hidden layer 1"')

90
    hidden.body.append('color = "white"')

91


92
    hidden2.body.append('label = "hidden layer 2"')

93
    hidden2.body.append('color = "white"')

94


95
    output.node("y")

96
    output.body.append('label = "output"')

97
    output.body.append('color = "white"')

98


99
    nn_graph.subgraph(inputs)

100
    nn_graph.subgraph(hidden)

101
    nn_graph.subgraph(hidden2)

102


103
    nn_graph.subgraph(output)

104


105
    for i in range(4):

106
        for j in range(3):

107
            nn_graph.edge("x[%d]" % i, "h1[%d]" % j, label="")

108


109
    for i in range(3):

110
        for j in range(3):

111
            nn_graph.edge("h1[%d]" % i, "h2[%d]" % j, label="")

112


113
    for i in range(3):

114
        nn_graph.edge("h2[%d]" % i, "y", label="")

115


116
    return nn_graph

117


118