[Django]-How to predict in multiple and simultanous Keras classifier sessions in Django?

My understanding of tensorflow graphs and sessions is:

A tensorflow graph hosts operations, placeholders and Variables. A tensorflow graph lives inside a tensorflow session (that’s why to save a trained model using tensorflow.train.Saver, you need something like Saver.save(sess, the graph)).

Here is a simple example to help you make sense of the relation between a keras model and a tensorflow graph:

import tensorflow as tf 
from keras.layers import Input, Dense
from keras.models import Model 

tf.reset_default_graph()
graph_1, graph_2 = tf.Graph(), tf.Graph()
with graph_1.as_default():
    x_in = Input(shape=(1, ), name='in_graph1')
    pred = Dense(5, name='dense1_graph1')(x_in)
    pred = Dense(1, name='dense2_graph1')(pred)
    model = Model(input=x_in, output=pred)
with graph_2.as_default():
    x_in = Input(shape=(1, ), name='in_graph2')
    pred = Dense(10, name='dense1_graph2')(x_in)
    pred = Dense(1, name='dense2_graph2')(pred)
    model = Model(input=x_in, output=pred)


with tf.Session() as sess:
    default_ops = sess.graph.get_operations()

with graph_1.as_default():
    with tf.Session() as sess:
        one_ops = sess.graph.get_operations()

with graph_2.as_default():
    with tf.Session() as sess:      
        two_ops = sess.graph.get_operations()

As you can see by running the code, default_ops is an empty list which means there is no operation in the default graph. one_ops is a list of operations of the first keras model and two_ops is a list of operations of the second keras model.

So, by using with graph.as_default(), a keras model can be exclusively embedded in a tensorflow graph.

With this in mind, it becomes easy to load several keras models in a single script. I think the example script below will address your confusions:

import numpy as np
import tensorflow as tf 
from keras.layers import Input, Dense
from keras.models import Model
from Keras.models import model_from_json

tf.reset_default_graph()
x = np.linspace(1, 4, 4)
y = np.random.rand(4)

models = {}
graph_1, graph_2 = tf.Graph(), tf.Graph()

# graph_1 
with graph_1.as_default():
    x_in = Input(shape=(1, ), name='in_graph1')
    pred = Dense(5, name='dense1_graph1')(x_in)
    pred = Dense(1, name='dense2_graph1')(pred)
    model = Model(input=x_in, output=pred)
    models['graph_1'] = model
# graph_2
with graph_2.as_default():
    x_in = Input(shape=(1, ), name='in_graph2')
    pred = Dense(10, name='dense1_graph2')(x_in)
    pred = Dense(1, name='dense2_graph2')(pred)
    model = Model(input=x_in, output=pred)
    models['graph_2'] = model

# save the two models 
with tf.Session(graph=graph_1) as sess:
    with open("model_1.json", "w") as source:
        source.write(models['graph_1'].to_json())
    models['graph_1'].save_weights("weights_1.h5")
with tf.Session(graph=graph_2) as sess:
    with open("model_2.json", "w") as source:
        source.write(models['graph_2'].to_json())
    models['graph_2'].save_weights("weights_2.h5")

####################################################
# play with the model 
pred_one, pred_one_reloaded = [], []
pred_two, pred_two_reloaded = [], []
for _ in range(10):
    print(_)
    if _ % 2 == 0:

        with graph_1.as_default():
            with tf.Session() as sess:
                sess.run(tf.global_variables_initializer())
                pred_one.append(models['graph_1'].predict(x).ravel())
        with tf.Session() as sess:
            with open("model_1.json", "r") as f:
                model = model_from_json(f.read())
            model.load_weights("weights_1.h5")
            pred_one_reloaded.append(model.predict(x).ravel())

    else:
        with graph_2.as_default():
            with tf.Session() as sess:
                sess.run(tf.global_variables_initializer())
                pred_two.append(models['graph_2'].predict(x).ravel())
        with tf.Session() as sess:
            with open("model_2.json", "r") as f:
                model = model_from_json(f.read())
            model.load_weights("weights_2.h5")
            pred_two_reloaded.append(model.predict(x).ravel())

pred_one = np.array(pred_one)
pred_one_reloaded = np.array(pred_one_reloaded)
pred_two = np.array(pred_two)
pred_two_reloaded = np.array(pred_two_reloaded)

print(pred_one)
print(pred_one_reloaded)
print(pred_two)
print(pred_two_reloaded)