jamartinh

def mc_dropout_inference(model, input, num_samples):
    # Repeat interleave the input
    repeated_input = input.repeat_interleave(num_samples, dim=0)

    # Enable dropout during inference
    model.train()

    # Forward pass with the repeated input
    repeated_output = model(repeated_input)

jamartinh

Data Science Challenge

The purpose of this challenge is to assist us in evaluating candidates for a role in our Data Science team. We only pass this challenge to candidates that we feel have a solid background and could be a good fit for our team. We appreciate you taking this time to help ensure we are a good match for each other.

Tips

Include code, graphics and text in a combined output. Tell a story, and let us understand as clearly as possible your thoughts and analytical process.

Part 1: Experiment design

jamartinh

jamartinh

import os

import keras
from keras import Input
from keras.layers import BatchNormalization, Dense, LSTM, concatenate, Dropout, TimeDistributed
from keras.optimizers import RMSprop
from keras.utils import plot_model

from metrics import in_top5, top5_accuracy

jamartinh

import os

import keras
from keras import Input
from keras.layers import BatchNormalization, Dense, LSTM, concatenate, Dropout, TimeDistributed
from keras.optimizers import RMSprop
from keras.utils import plot_model

from metrics import in_top5, top5_accuracy

jamartinh

# -------------------------------------------------------------------------------
# Copyright (c) 2012 Jose Antonio Martin H..
# All rights reserved. This program and the accompanying materials
# are made available under the terms of the GNU Public License v3.0
# which accompanies this distribution, and is available at
# http://www.gnu.org/licenses/gpl.html
#
# Contributors:
#     Jose Antonio Martin H. - Translation to Python from Java
# -------------------------------------------------------------------------------