dolaameng

Use PETA Dataset for Gender Detection

Steps

1. Download dataset from here
2. Uncompress the dataset to PETA, it should contain 10 subfolders like this

drwxrwxr-x 3 dola dola 4096 Oct 20  2014 3DPeS
drwxrwxr-x 3 dola dola 4096 Oct 20  2014 CAVIAR4REID
drwxrwxr-x 3 dola dola 4096 Oct 20  2014 CUHK
drwxrwxr-x 3 dola dola 4096 Oct 20  2014 GRID

dolaameng

#!/usr/bin/env python
#
# I tested by Python 3.4.3 on Windows 8.1
# Python 3.4.3 (v3.4.3:9b73f1c3e601, Feb 24 2015, 22:43:06) [MSC v.1600 32 bit (Intel)] on win32

import urllib.request
import getpass

# If you access to url below via Proxy,
# set environment variable 'http_proxy' before execute this.

dolaameng

import tensorflow as tf
import numpy as np

# graph
tf.reset_default_graph()
x = tf.placeholder(tf.float32, [], name='x')
y = tf.multiply(x, 2, name='y')
z = tf.add(x, 1, name='z')

# signature

dolaameng

TensorFlow Serving in 10 minutes!

TensorFlow SERVING is Googles' recommended way to deploy TensorFlow models. Without proper computer engineering background, it can be quite intimidating, even for people who feel comfortable with TensorFlow itself. Few things that I've found particularly hard were:

• Tutorial examples have C++ code (which I don't know)
• Tutorials have Kubernetes, gRPG, Bezel (some of which I saw for the first time)
• It needs to be compiled. That process takes forever!

After all, it worked just fine. Here I present an easiest possible way to deploy your models with TensorFlow Serving. You will have your self-built model running inside TF-Serving by the end of this tutorial. It will be scalable, and you will be able to query it via REST.

dolaameng

"""Script to illustrate usage of tf.estimator.Estimator in TF v1.3"""
import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data as mnist_data
from tensorflow.contrib import slim
from tensorflow.contrib.learn import ModeKeys
from tensorflow.contrib.learn import learn_runner


# Show debugging output

dolaameng

// A simple quickref for Eigen. Add anything that's missing.
// Main author: Keir Mierle

#include <Eigen/Dense>

Matrix<double, 3, 3> A;               // Fixed rows and cols. Same as Matrix3d.
Matrix<double, 3, Dynamic> B;         // Fixed rows, dynamic cols.
Matrix<double, Dynamic, Dynamic> C;   // Full dynamic. Same as MatrixXd.
Matrix<double, 3, 3, RowMajor> E;     // Row major; default is column-major.
Matrix3f P, Q, R;                     // 3x3 float matrix.

dolaameng

import torch
import torch.nn as nn
from torch.autograd import Variable

batch_size = 3
max_length = 3
hidden_size = 2
n_layers =1

# container

dolaameng

def color_hist(img, nbins=32, bins_range=(0, 256)):
    # Compute the histogram of the color channels separately
    channel1_hist = np.histogram(img[:,:,0], bins=nbins, range=bins_range)
    channel2_hist = np.histogram(img[:,:,1], bins=nbins, range=bins_range)
    channel3_hist = np.histogram(img[:,:,2], bins=nbins, range=bins_range)
    # Concatenate the histograms into a single feature vector
    hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))
    # Return the individual histograms, bin_centers and feature vector
    return hist_features

dolaameng

######################################## train and save model in train.py

# input, output, hyperparameter as placeholders, e.g.
x = tf.placeholder(tf.float32, (None, 32, 32, 3), name="x")
y = tf.placeholder(tf.int32, (None), name="y")
keep_prob = tf.placeholder(tf.float32, name="keep_prob")

# build model
yhat, loss = build_whatevermodel(x)
train_op = whateveroptimizer.minimize(loss)

dolaameng

'''Neural doodle with Keras

Script usage:
```
python neural_doodle.py nb_colors path_to_style_image.png path_to_style_mask.png \
        [optional_path_to_content_image] \
        path_to_your_doodle.png prefix_for_results
```

Examples: