TensorFlow: training on my own image

Answer #1:

If you are interested in how to input your own data in TensorFlow, you can look at this tutorial.
I’ve also written a guide with best practices for CS230 at Stanford here.

New answer (with tf.data) and with labels

With the introduction of tf.data in r1.4, we can create a batch of images without placeholders and without queues. The steps are the following:

1. Create a list containing the filenames of the images and a corresponding list of labels
2. Create a tf.data.Dataset reading these filenames and labels
3. Preprocess the data
4. Create an iterator from the tf.data.Dataset which will yield the next batch

The code is:

# step 1
filenames = tf.constant(['im_01.jpg', 'im_02.jpg', 'im_03.jpg', 'im_04.jpg'])
labels = tf.constant([0, 1, 0, 1])

# step 2: create a dataset returning slices of `filenames`
dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))

# step 3: parse every image in the dataset using `map`
def _parse_function(filename, label):
    image_string = tf.read_file(filename)
    image_decoded = tf.image.decode_jpeg(image_string, channels=3)
    image = tf.cast(image_decoded, tf.float32)
    return image, label

dataset = dataset.map(_parse_function)
dataset = dataset.batch(2)

# step 4: create iterator and final input tensor
iterator = dataset.make_one_shot_iterator()
images, labels = iterator.get_next()

Now we can run directly sess.run([images, labels]) without feeding any data through placeholders.

Old answer (with TensorFlow queues)

To sum it up you have multiple steps:

1. Create a list of filenames (ex: the paths to your images)
2. Create a TensorFlow filename queue
3. Read and decode each image, resize them to a fixed size (necessary for batching)
4. Output a batch of these images

The simplest code would be:

# step 1
filenames = ['im_01.jpg', 'im_02.jpg', 'im_03.jpg', 'im_04.jpg']

# step 2
filename_queue = tf.train.string_input_producer(filenames)

# step 3: read, decode and resize images
reader = tf.WholeFileReader()
filename, content = reader.read(filename_queue)
image = tf.image.decode_jpeg(content, channels=3)
image = tf.cast(image, tf.float32)
resized_image = tf.image.resize_images(image, [224, 224])

# step 4: Batching
image_batch = tf.train.batch([resized_image], batch_size=8)

Answer #2:

Based on @olivier-moindrot’s answer, but for Tensorflow 2.0+:

# step 1
filenames = tf.constant(['im_01.jpg', 'im_02.jpg', 'im_03.jpg', 'im_04.jpg'])
labels = tf.constant([0, 1, 0, 1])

# step 2: create a dataset returning slices of `filenames`
dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))

def im_file_to_tensor(file, label):
    def _im_file_to_tensor(file, label):
        path = f"../foo/bar/{file.numpy().decode()}"
        im = tf.image.decode_jpeg(tf.io.read_file(path), channels=3)
        im = tf.cast(image_decoded, tf.float32) / 255.0
        return im, label
    return tf.py_function(_im_file_to_tensor, 
                          inp=(file, label), 
                          Tout=(tf.float32, tf.uint8))

dataset = dataset.map(im_file_to_tensor)

If you are hitting an issue similar to:

ValueError: Cannot take the length of Shape with unknown rank

when passing tf.data.Dataset tensors to model.fit, then take a look at https://github.com/tensorflow/tensorflow/issues/24520. A fix for the code snippet above would be:

def im_file_to_tensor(file, label):
    def _im_file_to_tensor(file, label):
        path = f"../foo/bar/{file.numpy().decode()}"
        im = tf.image.decode_jpeg(tf.io.read_file(path), channels=3)
        im = tf.cast(image_decoded, tf.float32) / 255.0
        return im, label

    file, label = tf.py_function(_im_file_to_tensor, 
                                 inp=(file, label), 
                                 Tout=(tf.float32, tf.uint8))
    file.set_shape([192, 192, 3])
    label.set_shape([])
    return (file, label)

Answer #3:

2.0 Compatible Answer using Tensorflow Hub: Tensorflow Hub is a Provision/Product Offered by Tensorflow, which comprises the Models developed by Google, for Text and Image Datasets.

It saves Thousands of Hours of Training Time and Computational Effort, as it reuses the Existing Pre-Trained Model.

If we have an Image Dataset, we can take the Existing Pre-Trained Models from TF Hub and can adopt it to our Dataset.

Code for Re-Training our Image Dataset using the Pre-Trained Model, MobileNet, is shown below:

import itertools

import os

import matplotlib.pylab as plt

import numpy as np
import tensorflow as tf

import tensorflow_hub as hub
module_selection = ("mobilenet_v2_100_224", 224) #@param ["("mobilenet_v2_100_224"", 224)"", ""(""inception_v3"", 299)""] {type:""raw"", allow-input: true}
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