Keras.utils.visualize_util installation _keras of neural network visualization module in Keras

In Keras, a neural network visualization function plot is provided, and the visualization results can be saved locally. Plot use is as follows:

From Keras.utils.visualize_util import plot  
plot (model, to_file= ' model.png ')  

Note: The author uses the Keras version is 1.0.6, if is python3.5

From
keras.utils
import
plot_model
plot_model (model,to_file= ' model.png ')

However, this feature relies on the Graphviz module and the Pydot module, so you need to install the two modules first and install the Graphviz software itself (the version I installed is 2.38).

Second, the installation step command line input pip install Graphviz install Graphviz software. Official website address for http://www.graphviz.org/decompression version: Configure environment variables. Add Graphviz-2.38\release\bin in the installation directory to the PATH environment variable installation version: Install the MSI command line to enter PIP install pydot==1.1.0 Note: The Pydot for the installation of version 1.1.0 is required here because the Find_graphviz function is deprecated in the latest version (up to 2016.8 the latest version number is 1.2.x) and is used with an error

Third, test methods use the following script

# encoding:utf-8 "" "" "" "  

import numpy as NP from  
keras.models import sequential  
from Keras.layers.core import dense, activation from  
keras.optimizers import SGD from  
keras.utils import np_utils From      
keras.utils.visualize_util import plot  


def run ():  
    # Build Neural network  
    model = sequential ()  
    Model.add (Dense (4, input_dim=2, init= ' uniform '))  
    Model.add (Activation (' Relu '))  
    model.add (Dense (2, init= ' Uniform '))  
    Model.add (Activation (' sigmoid '))  
    SGD = SGD (lr=0.05, decay=1e-6, momentum=0.9, nesterov=true)  
    model.compile (loss= ' binary_crossentropy ', OPTIMIZER=SGD, metrics=[' accuracy ')  

    # Neural Network visualization  
    plot (model, to_file= ' model.png ')  

if __name__ = = ' __ Main__ ':  
    run ()  

Run the example, the error will be displayed: No module named ' Keras.utils.visualize_util '

Pip Install pydot==1.1.0 This method is for Python2,
but Python3 is not, because the Python3 installed 1.2. There is a change in the version, where the visualization needs to be visualize_ Util such an API, but in 1.2, this API was canceled, so python3 users should install the Pydot_ng command line
Pip install pydot_ng
Pip install Pydot

Import NumPy as NP    
from keras.models import sequential    
to Keras.layers.core import dense, activation from    
K Eras.optimizers Import SGD    
keras.utils import np_utils from        
keras.utils.vis_utils import Plot_model    
def run ():    
    # Build Neural network    
    model = sequential ()    
    model.add (Dense (4, input_dim=2, kernel_initializer= ' uniform '))    
    Model.add (Activation (' Relu '))    
    model.add (Dense (2, kernel_initializer= ' Uniform '))    
    Model.add ( Activation (' sigmoid ')    
    sgd = SGD (lr=0.05, decay=1e-6, momentum=0.9, nesterov=true)    
    model.compile (loss= ' Binary_crossentropy ', OPTIMIZER=SGD, metrics=[' accuracy ']    
    # Neural network visualization    
    Plot_model (model, to_file= ' Model.png ')    
if __name__ = = ' __main__ ':    
    run ()  

Note: Now the visual module is not called: keras.utils.visualize_util, changed to Keras.utils.vis_utils, so the introduction of the time to pay special attention.

The following code demonstrates creating a diagram:

 Import Pydot g = Pydot. Dot (graph_type= ' graph ') G.add_node (Pydot. Node (str (0), fontcolor= ' Transparent ')) for I in range (5): G.add_node (Pydot. Node (STR (i + 1)) G.add_edge (Pydot). Edge (str (0), str (i + 1))) for J in Range (5): G.add_node (Pydot. Node (Str (j + 1) + ' 0 ' + str (i + 1)) G.add_edge (Pydot. Edge (Str (j + 1) + ' 0 ' + str (i + 1), str (j + 1)) g.write_png (' C:/ch02_fig2-9_graph.png ', prog= ' Neato ')