notebook.community

Edit and run

Note to students

Please run through this notebook. The cell that writes out records will not work because you do not have access to the output bucket, however, all other cells will work and there is no need to write output data. This bucket already contains the necessary processed data:
gs://flowers-public/tfrecords-jpeg-192x192-2/

Imports





In [1]:



    


import os, sys, math
import numpy as np
from matplotlib import pyplot as plt
if 'google.colab' in sys.modules: # Colab-only Tensorflow version selector
  %tensorflow_version 2.x
import tensorflow as tf
print("Tensorflow version " + tf.__version__)
AUTO = tf.data.experimental.AUTOTUNE # used in tf.data.Dataset API



    


















    






TensorFlow 2.x selected.
Tensorflow version 2.1.0-rc1

Configuration





In [0]:



    


GCS_PATTERN = 'gs://flowers-public/*/*.jpg'
GCS_OUTPUT = 'gs://flowers-public/tfrecords-jpeg-192x192-2/flowers'  # prefix for output file names
SHARDS = 16
TARGET_SIZE = [192, 192]
CLASSES = [b'daisy', b'dandelion', b'roses', b'sunflowers', b'tulips'] # do not change, maps to the labels in the data (folder names)



    











In [0]:



    


#@title "display utilities [RUN ME]"
def display_9_images_from_dataset(dataset):
  plt.figure(figsize=(13,13))
  subplot=331
  for i, (image, label) in enumerate(dataset):
    plt.subplot(subplot)
    plt.axis('off')
    plt.imshow(image.numpy().astype(np.uint8))
    plt.title(label.numpy().decode("utf-8"), fontsize=16)
    subplot += 1
    if i==8:
      break
  plt.tight_layout()
  plt.subplots_adjust(wspace=0.1, hspace=0.1)
  plt.show()

Colab-only auth

You would need this if you were accessing private GCS buckets. Uncomment if you want to re-use this notebook to write data to you own private bucket.





In [0]:



    


# if 'google.colab' in sys.modules:
#   from google.colab import auth
#   auth.authenticate_user()

Read images and labels





In [5]:



    


nb_images = len(tf.io.gfile.glob(GCS_PATTERN))
shard_size = math.ceil(1.0 * nb_images / SHARDS)
print("Pattern matches {} images which will be rewritten as {} .tfrec files containing {} images each.".format(nb_images, SHARDS, shard_size))

def decode_jpeg_and_label(filename):
  bits = tf.io.read_file(filename)
  image = tf.image.decode_jpeg(bits)
  # parse flower name from containing directory
  label = tf.strings.split(tf.expand_dims(filename, axis=-1), sep='/')
  label = label.values[-2]
  return image, label

filenames = tf.data.Dataset.list_files(GCS_PATTERN, seed=35155) # This also shuffles the images
dataset1 = filenames.map(decode_jpeg_and_label, num_parallel_calls=AUTO)



    


















    






Pattern matches 3670 images which will be rewritten as 16 .tfrec files containing 230 images each.

















In [6]:



    


display_9_images_from_dataset(dataset1)

Resize and crop images to common size

No need to study the code in this cell. It's only image resizing.





In [0]:



    


def resize_and_crop_image(image, label):
  # Resize and crop using "fill" algorithm:
  # always make sure the resulting image
  # is cut out from the source image so that
  # it fills the TARGET_SIZE entirely with no
  # black bars and a preserved aspect ratio.
  w = tf.shape(image)[0]
  h = tf.shape(image)[1]
  tw = TARGET_SIZE[1]
  th = TARGET_SIZE[0]
  resize_crit = (w * th) / (h * tw)
  image = tf.cond(resize_crit < 1,
                  lambda: tf.image.resize(image, [w*tw/w, h*tw/w]), # if true
                  lambda: tf.image.resize(image, [w*th/h, h*th/h])  # if false
                 )
  nw = tf.shape(image)[0]
  nh = tf.shape(image)[1]
  image = tf.image.crop_to_bounding_box(image, (nw - tw) // 2, (nh - th) // 2, tw, th)
  return image, label
  
dataset2 = dataset1.map(resize_and_crop_image, num_parallel_calls=AUTO)



    











In [10]:



    


display_9_images_from_dataset(dataset2)

Speed test: too slow

Google Cloud Storage is capable of great throughput but has a per-file access penalty. Run the cell below and see that throughput is around 8 images per second. That is too slow. Training on thousands of individual files will not work. We have to use the TFRecord format to group files together.





In [11]:



    


display_dataset = dataset2.batch(8)
for image, label in display_dataset.take(30):
  print("Image batch shape {}, {})".format(image.numpy().shape, [lbl.decode('utf8') for lbl in label.numpy()]))



    


















    






Image batch shape (8, 192, 192, 3), ['sunflowers', 'tulips', 'tulips', 'tulips', 'sunflowers', 'tulips', 'tulips', 'daisy'])
Image batch shape (8, 192, 192, 3), ['dandelion', 'daisy', 'dandelion', 'roses', 'dandelion', 'roses', 'roses', 'roses'])
Image batch shape (8, 192, 192, 3), ['daisy', 'daisy', 'tulips', 'daisy', 'tulips', 'daisy', 'sunflowers', 'daisy'])
Image batch shape (8, 192, 192, 3), ['daisy', 'roses', 'sunflowers', 'tulips', 'tulips', 'roses', 'daisy', 'daisy'])
Image batch shape (8, 192, 192, 3), ['roses', 'dandelion', 'roses', 'tulips', 'daisy', 'tulips', 'roses', 'tulips'])
Image batch shape (8, 192, 192, 3), ['daisy', 'dandelion', 'tulips', 'tulips', 'dandelion', 'tulips', 'roses', 'daisy'])
Image batch shape (8, 192, 192, 3), ['dandelion', 'sunflowers', 'sunflowers', 'roses', 'daisy', 'dandelion', 'daisy', 'roses'])
Image batch shape (8, 192, 192, 3), ['roses', 'roses', 'sunflowers', 'daisy', 'sunflowers', 'daisy', 'roses', 'roses'])
Image batch shape (8, 192, 192, 3), ['dandelion', 'dandelion', 'roses', 'dandelion', 'dandelion', 'daisy', 'daisy', 'daisy'])
Image batch shape (8, 192, 192, 3), ['tulips', 'tulips', 'daisy', 'sunflowers', 'dandelion', 'sunflowers', 'sunflowers', 'tulips'])
Image batch shape (8, 192, 192, 3), ['roses', 'sunflowers', 'roses', 'tulips', 'daisy', 'sunflowers', 'sunflowers', 'sunflowers'])
Image batch shape (8, 192, 192, 3), ['daisy', 'roses', 'dandelion', 'daisy', 'dandelion', 'dandelion', 'tulips', 'tulips'])
Image batch shape (8, 192, 192, 3), ['daisy', 'sunflowers', 'sunflowers', 'dandelion', 'roses', 'dandelion', 'roses', 'dandelion'])
Image batch shape (8, 192, 192, 3), ['roses', 'sunflowers', 'tulips', 'daisy', 'roses', 'dandelion', 'sunflowers', 'dandelion'])
Image batch shape (8, 192, 192, 3), ['tulips', 'tulips', 'tulips', 'dandelion', 'sunflowers', 'tulips', 'sunflowers', 'tulips'])
Image batch shape (8, 192, 192, 3), ['sunflowers', 'tulips', 'tulips', 'sunflowers', 'daisy', 'sunflowers', 'tulips', 'sunflowers'])
Image batch shape (8, 192, 192, 3), ['roses', 'tulips', 'dandelion', 'roses', 'daisy', 'sunflowers', 'tulips', 'dandelion'])
Image batch shape (8, 192, 192, 3), ['tulips', 'tulips', 'tulips', 'tulips', 'tulips', 'roses', 'tulips', 'roses'])
Image batch shape (8, 192, 192, 3), ['tulips', 'tulips', 'tulips', 'dandelion', 'daisy', 'dandelion', 'dandelion', 'daisy'])
Image batch shape (8, 192, 192, 3), ['sunflowers', 'tulips', 'tulips', 'tulips', 'dandelion', 'dandelion', 'dandelion', 'dandelion'])
Image batch shape (8, 192, 192, 3), ['daisy', 'tulips', 'dandelion', 'sunflowers', 'tulips', 'tulips', 'dandelion', 'roses'])
Image batch shape (8, 192, 192, 3), ['dandelion', 'daisy', 'daisy', 'sunflowers', 'dandelion', 'roses', 'daisy', 'dandelion'])
Image batch shape (8, 192, 192, 3), ['tulips', 'dandelion', 'daisy', 'sunflowers', 'dandelion', 'sunflowers', 'daisy', 'tulips'])
Image batch shape (8, 192, 192, 3), ['dandelion', 'daisy', 'dandelion', 'dandelion', 'dandelion', 'tulips', 'sunflowers', 'daisy'])
Image batch shape (8, 192, 192, 3), ['roses', 'dandelion', 'roses', 'dandelion', 'roses', 'roses', 'sunflowers', 'sunflowers'])
Image batch shape (8, 192, 192, 3), ['daisy', 'dandelion', 'roses', 'tulips', 'sunflowers', 'sunflowers', 'tulips', 'dandelion'])
Image batch shape (8, 192, 192, 3), ['tulips', 'sunflowers', 'tulips', 'dandelion', 'daisy', 'dandelion', 'tulips', 'sunflowers'])
Image batch shape (8, 192, 192, 3), ['dandelion', 'tulips', 'roses', 'daisy', 'dandelion', 'daisy', 'tulips', 'tulips'])
Image batch shape (8, 192, 192, 3), ['tulips', 'tulips', 'tulips', 'sunflowers', 'tulips', 'sunflowers', 'dandelion', 'sunflowers'])
Image batch shape (8, 192, 192, 3), ['daisy', 'tulips', 'tulips', 'sunflowers', 'dandelion', 'tulips', 'sunflowers', 'dandelion'])

Recompress the images

The bandwidth savings outweight the decoding CPU cost





In [0]:



    


def recompress_image(image, label):
  height = tf.shape(image)[0]
  width = tf.shape(image)[1]
  image = tf.cast(image, tf.uint8)
  image = tf.image.encode_jpeg(image, optimize_size=True, chroma_downsampling=False)
  return image, label, height, width

dataset3 = dataset2.map(recompress_image, num_parallel_calls=AUTO)
dataset3 = dataset3.batch(shard_size) # sharding: there will be one "batch" of images per file

Write dataset to TFRecord files

Students, do not run this cell, just read through.

You do not have write access to the output bucket so you would get an error. Read through the code to amiliarize yourself with TFRecord encoding.





In [16]:



    


# Three types of data can be stored in TFRecords: bytestrings, integers and floats
# They are always stored as lists, a single data element will be a list of size 1

def _bytestring_feature(list_of_bytestrings):
  return tf.train.Feature(bytes_list=tf.train.BytesList(value=list_of_bytestrings))

def _int_feature(list_of_ints): # int64
  return tf.train.Feature(int64_list=tf.train.Int64List(value=list_of_ints))

def _float_feature(list_of_floats): # float32
  return tf.train.Feature(float_list=tf.train.FloatList(value=list_of_floats))
  

def to_tfrecord(tfrec_filewriter, img_bytes, label, height, width):  
  class_num = np.argmax(np.array(CLASSES)==label) # 'roses' => 2 (order defined in CLASSES)
  one_hot_class = np.eye(len(CLASSES))[class_num]     # [0, 0, 1, 0, 0] for class #2, roses

  feature = {
      "image": _bytestring_feature([img_bytes]), # one image in the list
      "class": _int_feature([class_num]),        # one class in the list
      
      # additional (not very useful) fields to demonstrate TFRecord writing/reading of different types of data
      "label":         _bytestring_feature([label]),          # fixed length (1) list of strings, the text label
      "size":          _int_feature([height, width]),         # fixed length (2) list of ints
      "one_hot_class": _float_feature(one_hot_class.tolist()) # variable length  list of floats, n=len(CLASSES)
  }
  return tf.train.Example(features=tf.train.Features(feature=feature))
  
print("Writing TFRecords")
for shard, (image, label, height, width) in enumerate(dataset3):
  # batch size used as shard size here
  shard_size = image.numpy().shape[0]
  # good practice to have the number of records in the filename
  filename = GCS_OUTPUT + "{:02d}-{}.tfrec".format(shard, shard_size)
  
  with tf.io.TFRecordWriter(filename) as out_file:
    for i in range(shard_size):
      example = to_tfrecord(out_file,
                            image.numpy()[i], # re-compressed image: already a byte string
                            label.numpy()[i],
                            height.numpy()[i],
                            width.numpy()[i])
      out_file.write(example.SerializeToString())
    print("Wrote file {} containing {} records".format(filename, shard_size))



    


















    






Writing TFRecords
Wrote file gs://flowers-public/tfrecords-jpeg-192x192-nnn/flowers00-230.tfrec containing 230 records
Wrote file gs://flowers-public/tfrecords-jpeg-192x192-nnn/flowers01-230.tfrec containing 230 records
Wrote file gs://flowers-public/tfrecords-jpeg-192x192-nnn/flowers02-230.tfrec containing 230 records
Wrote file gs://flowers-public/tfrecords-jpeg-192x192-nnn/flowers03-230.tfrec containing 230 records
Wrote file gs://flowers-public/tfrecords-jpeg-192x192-nnn/flowers04-230.tfrec containing 230 records
Wrote file gs://flowers-public/tfrecords-jpeg-192x192-nnn/flowers05-230.tfrec containing 230 records
Wrote file gs://flowers-public/tfrecords-jpeg-192x192-nnn/flowers06-230.tfrec containing 230 records
Wrote file gs://flowers-public/tfrecords-jpeg-192x192-nnn/flowers07-230.tfrec containing 230 records
Wrote file gs://flowers-public/tfrecords-jpeg-192x192-nnn/flowers08-230.tfrec containing 230 records
Wrote file gs://flowers-public/tfrecords-jpeg-192x192-nnn/flowers09-230.tfrec containing 230 records
Wrote file gs://flowers-public/tfrecords-jpeg-192x192-nnn/flowers10-230.tfrec containing 230 records
Wrote file gs://flowers-public/tfrecords-jpeg-192x192-nnn/flowers11-230.tfrec containing 230 records
Wrote file gs://flowers-public/tfrecords-jpeg-192x192-nnn/flowers12-230.tfrec containing 230 records
Wrote file gs://flowers-public/tfrecords-jpeg-192x192-nnn/flowers13-230.tfrec containing 230 records
Wrote file gs://flowers-public/tfrecords-jpeg-192x192-nnn/flowers14-230.tfrec containing 230 records
Wrote file gs://flowers-public/tfrecords-jpeg-192x192-nnn/flowers15-220.tfrec containing 220 records

Read from TFRecord Dataset

Resume running the cells





In [0]:



    


def read_tfrecord(example):
    features = {
        "image": tf.io.FixedLenFeature([], tf.string),  # tf.string = bytestring (not text string)
        "class": tf.io.FixedLenFeature([], tf.int64),   # shape [] means scalar
        
        # additional (not very useful) fields to demonstrate TFRecord writing/reading of different types of data
        "label":         tf.io.FixedLenFeature([], tf.string),  # one bytestring
        "size":          tf.io.FixedLenFeature([2], tf.int64),  # two integers
        "one_hot_class": tf.io.VarLenFeature(tf.float32)        # a certain number of floats
    }
    # decode the TFRecord
    example = tf.io.parse_single_example(example, features)
    
    # FixedLenFeature fields are now ready to use: exmple['size']
    # VarLenFeature fields require additional sparse_to_dense decoding
    
    image = tf.image.decode_jpeg(example['image'], channels=3)
    image = tf.reshape(image, [*TARGET_SIZE, 3])
    
    class_num = example['class']
    
    label  = example['label']
    height = example['size'][0]
    width  = example['size'][1]
    one_hot_class = tf.sparse.to_dense(example['one_hot_class'])
    return image, class_num, label, height, width, one_hot_class
    
# read from TFRecords. For optimal performance, read from multiple
# TFRecord files at once and set the option experimental_deterministic = False
# to allow order-altering optimizations.

option_no_order = tf.data.Options()
option_no_order.experimental_deterministic = False

filenames = tf.io.gfile.glob(GCS_OUTPUT + "*.tfrec")
dataset4 = tf.data.TFRecordDataset(filenames, num_parallel_reads=AUTO)
dataset4 = dataset4.with_options(option_no_order)
dataset4 = dataset4.map(read_tfrecord, num_parallel_calls=AUTO)
dataset4 = dataset4.shuffle(300)



    











In [12]:



    


display_dataset = dataset4.map(lambda image, class_num, label, height, width, one_hot_class: (image, label))
display_9_images_from_dataset(display_dataset)

Speed test: fast

Loading training data is not a bottleneck anymore





In [13]:



    


for image, class_num, label, height, width, one_hot_class in dataset4.take(300):
    print("Image shape {}, {}x{} px, class={} ({:>10}, {})".format(image.numpy().shape, width, height, class_num, label.numpy().decode('utf8'), one_hot_class))



    


















    






Image shape (192, 192, 3), 192x192 px, class=4 (    tulips, [0. 0. 0. 0. 1.])
Image shape (192, 192, 3), 192x192 px, class=4 (    tulips, [0. 0. 0. 0. 1.])
Image shape (192, 192, 3), 192x192 px, class=1 ( dandelion, [0. 1. 0. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=4 (    tulips, [0. 0. 0. 0. 1.])
Image shape (192, 192, 3), 192x192 px, class=1 ( dandelion, [0. 1. 0. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=4 (    tulips, [0. 0. 0. 0. 1.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=2 (     roses, [0. 0. 1. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=2 (     roses, [0. 0. 1. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=1 ( dandelion, [0. 1. 0. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=0 (     daisy, [1. 0. 0. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=2 (     roses, [0. 0. 1. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=4 (    tulips, [0. 0. 0. 0. 1.])
Image shape (192, 192, 3), 192x192 px, class=4 (    tulips, [0. 0. 0. 0. 1.])
Image shape (192, 192, 3), 192x192 px, class=0 (     daisy, [1. 0. 0. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=2 (     roses, [0. 0. 1. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=4 (    tulips, [0. 0. 0. 0. 1.])
Image shape (192, 192, 3), 192x192 px, class=2 (     roses, [0. 0. 1. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=1 ( dandelion, [0. 1. 0. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=4 (    tulips, [0. 0. 0. 0. 1.])
Image shape (192, 192, 3), 192x192 px, class=1 ( dandelion, [0. 1. 0. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=2 (     roses, [0. 0. 1. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=4 (    tulips, [0. 0. 0. 0. 1.])
Image shape (192, 192, 3), 192x192 px, class=2 (     roses, [0. 0. 1. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=1 ( dandelion, [0. 1. 0. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=0 (     daisy, [1. 0. 0. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=0 (     daisy, [1. 0. 0. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=2 (     roses, [0. 0. 1. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=2 (     roses, [0. 0. 1. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=4 (    tulips, [0. 0. 0. 0. 1.])
Image shape (192, 192, 3), 192x192 px, class=1 ( dandelion, [0. 1. 0. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=4 (    tulips, [0. 0. 0. 0. 1.])
Image shape (192, 192, 3), 192x192 px, class=1 ( dandelion, [0. 1. 0. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=2 (     roses, [0. 0. 1. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=2 (     roses, [0. 0. 1. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=4 (    tulips, [0. 0. 0. 0. 1.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=4 (    tulips, [0. 0. 0. 0. 1.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=4 (    tulips, [0. 0. 0. 0. 1.])
Image shape (192, 192, 3), 192x192 px, class=0 (     daisy, [1. 0. 0. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=2 (     roses, [0. 0. 1. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=1 ( dandelion, [0. 1. 0. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=2 (     roses, [0. 0. 1. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=2 (     roses, [0. 0. 1. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=0 (     daisy, [1. 0. 0. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=4 (    tulips, [0. 0. 0. 0. 1.])
Image shape (192, 192, 3), 192x192 px, class=2 (     roses, [0. 0. 1. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=4 (    tulips, [0. 0. 0. 0. 1.])
Image shape (192, 192, 3), 192x192 px, class=4 (    tulips, [0. 0. 0. 0. 1.])
Image shape (192, 192, 3), 192x192 px, class=0 (     daisy, [1. 0. 0. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=2 (     roses, [0. 0. 1. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=1 ( dandelion, [0. 1. 0. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=1 ( dandelion, [0. 1. 0. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=4 (    tulips, [0. 0. 0. 0. 1.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=2 (     roses, [0. 0. 1. 0. 0.])
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Image shape (192, 192, 3), 192x192 px, class=4 (    tulips, [0. 0. 0. 0. 1.])
Image shape (192, 192, 3), 192x192 px, class=4 (    tulips, [0. 0. 0. 0. 1.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=1 ( dandelion, [0. 1. 0. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=1 ( dandelion, [0. 1. 0. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=1 ( dandelion, [0. 1. 0. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])
Image shape (192, 192, 3), 192x192 px, class=1 ( dandelion, [0. 1. 0. 0. 0.])
Image shape (192, 192, 3), 192x192 px, class=3 (sunflowers, [0. 0. 0. 1. 0.])

License

author: Martin Gorner
twitter: @martin_gorner

Copyright 2020 Google LLC

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

This is not an official Google product but sample code provided for an educational purpose

Content source: turbomanage/training-data-analyst

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