multithreading.py

# Create first network with Keras
from keras.models import Sequential
from keras.layers import Dense
import numpy
import threading as t
import tensorflow as tf

graph = tf.get_default_graph()

def t_thread():
    with graph.as_default():
        # create model
        model = Sequential()
        model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))
        model.add(Dense(8, init='uniform', activation='relu'))
        model.add(Dense(1, init='uniform', activation='sigmoid'))

        model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

        # evaluate the model
        scores = model.evaluate(numpy.array([[0,0,0,0,0,0,0,0]]), numpy.array([[1]]))

    print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

# versus thread - does not work
th = t.Thread(target=t_thread)
th.start()
th.join()

th2 = t.Thread(target=t_thread)
th2.start()
th2.join()

th3 = t.Thread(target=t_thread)
th3.start()
th3.join()