Trung Tran ChunML

## data_import.ipynb

      
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                ChunML
                / data_import.ipynb
            
            
              Created
              December 12, 2018 01:12
            
          
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## tensorflow_1_1.py
import pandas as pd
import tensorflow as tf
import os
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt

## tensorflow_1_2.py
def read_labels_from_file(data_root, label_path):
    data = pd.read_csv(label_path)
    data['fn'] = data.id.map(lambda x: os.path.join(
        data_root, 'train', x + '.jpg'))
    int_to_breed = data.breed.unique()
    breed_to_int = dict((v, k) for k, v in enumerate(int_to_breed))
    data['int_breed'] = data.breed.map(lambda x: breed_to_int[x])

    return data.fn, data.int_breed, int_to_breed, breed_to_int

## tensorflow_1_3.py
data_root = './data/all'
label_path = './data/all/labels.csv'

filenames, labels, int_to_breed, breed_to_int = read_labels_from_file(data_root, label_path)

list(zip(*(filenames, labels)))[:5]

## tensorflow_1_4.py
def one_shot_input_fn(filenames, labels):
    dataset = tf.data.Dataset.from_tensor_slices(
        (filenames, labels))

## tensorflow_1_0.py
image = tf.placeholder(tf.float32, [None, 224, 224, 3])
label = tf.placeholder(tf.int64, [None])

loss, train_op = SomeNetwork(image, label)

with tf.Session() as sess:
    processed_image, processed_label = get_data_from_file()
	loss, _ = sess.run(
		[loss, train_op],
		feed_dict={image: processed_image,

## tensorflow_1_5.py
    dataset = dataset.map(_parse_data)

## tensorflow_1_6.py
    dataset = dataset.batch(1)

## tensorflow_1_7.py
    iterator = dataset.make_one_shot_iterator()

## tensorflow_1_8.py
    img, label = iterator.get_next()
    return img, label
	import pandas as pd
	import tensorflow as tf
	import os
	import numpy as np
	from PIL import Image
	import matplotlib.pyplot as plt
	def read_labels_from_file(data_root, label_path):
	data = pd.read_csv(label_path)
	data['fn'] = data.id.map(lambda x: os.path.join(
	data_root, 'train', x + '.jpg'))
	int_to_breed = data.breed.unique()
	breed_to_int = dict((v, k) for k, v in enumerate(int_to_breed))
	data['int_breed'] = data.breed.map(lambda x: breed_to_int[x])

	return data.fn, data.int_breed, int_to_breed, breed_to_int
	data_root = './data/all'
	label_path = './data/all/labels.csv'

	filenames, labels, int_to_breed, breed_to_int = read_labels_from_file(data_root, label_path)

	list(zip(*(filenames, labels)))[:5]
	def one_shot_input_fn(filenames, labels):
	dataset = tf.data.Dataset.from_tensor_slices(
	(filenames, labels))
	image = tf.placeholder(tf.float32, [None, 224, 224, 3])
	label = tf.placeholder(tf.int64, [None])

	loss, train_op = SomeNetwork(image, label)

	with tf.Session() as sess:
	processed_image, processed_label = get_data_from_file()
	loss, _ = sess.run(
	[loss, train_op],
	feed_dict={image: processed_image,