calibrate.py

import time

import_start = time.clock()
import tensorflow as tf
import_stop = time.clock()

import numpy as np
from keras.models import Model
from keras.layers import Dense, Input

def create_model():
    input_layer = Input(shape=(1,))
    dense_layer = Dense(1)(input_layer)
    output = Dense(1, activation=None)(dense_layer)

    model = Model(inputs=input_layer, outputs=output)
    model.compile(loss='mean_squared_error', optimizer='Adam')
    return model

def main():
    model = create_model()  # create a model with random weights
    dummy_input = np.asarray(0).reshape((1,1))

    first_prediction_start = time.clock()
    model.predict(dummy_input)  # dummy prediction
    first_prediction_stop = time.clock()

    second_prediction_start = time.clock()
    model.predict(dummy_input)  # dummy prediction
    second_prediction_stop = time.clock()

    third_prediction_start = time.clock()
    model.predict(dummy_input)  # dummy prediction
    third_prediction_stop = time.clock()

    print("%f s to load TensorFlow\n" % (import_stop - import_start))

    print("%f s for first prediction" % (first_prediction_stop - first_prediction_start))
    print("%f s for second prediction" % (second_prediction_stop - second_prediction_start))
    print("%f s for third prediction" % (third_prediction_stop - third_prediction_start))

if __name__ == "__main__":
    main()