r_u_a_bird.py

  # -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import

import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.estimator import regression
from tflearn.data_preprocessing import ImagePreprocessing
from tflearn.data_augmentation import ImageAugmentation
import scipy
import numpy as np
import argparse

parser = argparse.ArgumentParser(description='Decide if an image is a picture of a bird')
parser.add_argument('image', type=str, help='The image image file to check')
args = parser.parse_args()


# Same network definition as before
img_prep = ImagePreprocessing()
img_prep.add_featurewise_zero_center()
img_prep.add_featurewise_stdnorm()
img_aug = ImageAugmentation()
img_aug.add_random_flip_leftright()
img_aug.add_random_rotation(max_angle=25.)
img_aug.add_random_blur(sigma_max=3.)

network = input_data(shape=[None, 32, 32, 3],
                     data_preprocessing=img_prep,
                     data_augmentation=img_aug)
network = conv_2d(network, 32, 3, activation='relu')
network = max_pool_2d(network, 2)
network = conv_2d(network, 64, 3, activation='relu')
network = conv_2d(network, 64, 3, activation='relu')
network = max_pool_2d(network, 2)
network = fully_connected(network, 512, activation='relu')
network = dropout(network, 0.5)
network = fully_connected(network, 2, activation='softmax')
network = regression(network, optimizer='adam',
                     loss='categorical_crossentropy',
                     learning_rate=0.001)

model = tflearn.DNN(network, tensorboard_verbose=0, checkpoint_path='bird-classifier.tfl.ckpt')
model.load("bird-classifier.tfl.ckpt-50912")

# Load the image file
img = scipy.ndimage.imread(args.image, mode="RGB")

# Scale it to 32x32
img = scipy.misc.imresize(img, (32, 32), interp="bicubic").astype(np.float32, casting='unsafe')

# Predict
prediction = model.predict([img])

# Check the result.
is_bird = np.argmax(prediction[0]) == 1

if is_bird:
    print("That's a bird!")
else:
    print("That's not a bird!")

Does this script need "bird-classifier.tfl.ckpt" to work?

ayo fam how are we supposed to identify birds without that sweet sweet data set? gimme gimme please

You should also report the confidence percentage of the prediction at around line 56, report np.max(prediction[0])

If somebody is still wondering, this script is a part of Adam's nice tutorial on machine learning, over on Medium. https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.7pmvr7722.
You'll find a reference for this bird dataset in part 3.

Hi, can you tell me what your Y dataset looks like? The way I did it was [[1., 0.], ... , [0., 1.]]. I'm always getting [0., 1.] as my prediction.

how to run can u explain

in parser.add_argument('image', type=str, help='The image image file to check') does 'image' refers to the path to the image file?

types of image file is not clear. Should it be jpeg, bmp, GIF, TIF for testing data ?