To run NNVIZ on your trained network you need to create a config.py. Then run nnviz server in the same directory as config.py
Example config.py
"""
Config file for nnviz
"""
from nnviz.utils import * # Necessary for using nnviz utilities.
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import model_from_json
from os import path as pathlib
model_path = pathlib.abspath("./examples/notebooks/saved_model/mnist_dense_diff.json")
weights_path = pathlib.abspath("./examples/notebooks/saved_model/mnist_dense_diff")
"""
Data Prep
"""
(x,y),(_,_) = tf.keras.datasets.mnist.load_data() # Load dataset
x = x.astype(np.float32).reshape(-1,784) # Prepare for input
# This will be used for setting outputs.
input_config = {
"examples":[ # Look at input_examples for detailed explaination
{
"name":f"ex_{i}_class_{str(y[i])}",
"input":{
"value":x[i:i+1],
},
"output":str(y[i])
}
for i in np.random.randint(0,len(x),20)
],
"input_layers_config":{ # Look at input_layers_config for detailed explaination
"input":{
"type":"image",
"shape":(28,28),
"transformer":"prepare_input_image",
"resize":(128,128)
}
},
"input_nodes":['input'] # Look at input_nodes for detailed explaination
}
model = model_from_json(open(model_path,"r").read()) # Create model from
model.load_weights(weights_path)
* API is not fully functioning yet.
Add different input types
Create a service sidebar
Customizable Network UI
1. Image
Input : Image with shape ( h,w,c ) for RGB images, (h,w) for greyscale images.
Renders : 128x128 Image
2. Row
Input : A list of tuples containing column name and column value.
Renders : A table