我有一个目录，其中包含图像的子文件夹（根据标签）。我想在Keras中使用ImageDataGenerator时将这些数据拆分为train和test set。尽管keras中的model.fit（）具有用于指定拆分的参数validation_split，但我找不到与model.fit_generator（）相同的参数。怎么做？
train_datagen = ImageDataGenerator(rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=32,
class_mode='binary')
model.fit_generator(
train_generator,
samples_per_epoch=nb_train_samples,
nb_epoch=nb_epoch,
validation_data=??,
nb_val_samples=nb_validation_samples)
我没有单独的验证数据目录，需要将其从训练数据中分离出来
答案 0 :(得分：30)
Keras现在使用ImageDataGenerator从单个目录中添加了Train /验证拆分：
train_datagen = ImageDataGenerator(rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
validation_split=0.2) # set validation split
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='binary',
subset='training') # set as training data
validation_generator = train_datagen.flow_from_directory(
train_data_dir, # same directory as training data
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='binary'
subset='validation') # set as validation data
model.fit_generator(
train_generator,
steps_per_epoch = train_generator.samples // batch_size,
validation_data = validation_generator,
validation_steps = validation_generator.samples // batch_size,
epochs = nb_epochs)
答案 1 :(得分：13)
例如，您拥有这样的文件夹
full_dataset
|--horse (40 images)
|--donkey (30 images)
|--cow ((50 images)
|--zebra (70 images)
第一路
image_generator = ImageDataGenerator(rescale=1/255, validation_split=0.2)
train_dataset = image_generator.flow_from_directory(batch_size=32,
directory='full_dataset',
shuffle=True,
target_size=(280, 280),
subset="training",
class_mode='categorical')
validation_dataset = image_generator.flow_from_directory(batch_size=32,
directory='full_dataset',
shuffle=True,
target_size=(280, 280),
subset="validation",
class_mode='categorical')
第二种方式
import glob
horse = glob.glob('full_dataset/horse/*.*')
donkey = glob.glob('full_dataset/donkey/*.*')
cow = glob.glob('full_dataset/cow/*.*')
zebra = glob.glob('full_dataset/zebra/*.*')
data = []
labels = []
for i in horse:
image=tf.keras.preprocessing.image.load_img(i, color_mode='RGB',
target_size= (280,280))
image=np.array(image)
data.append(image)
labels.append(0)
for i in donkey:
image=tf.keras.preprocessing.image.load_img(i, color_mode='RGB',
target_size= (280,280))
image=np.array(image)
data.append(image)
labels.append(1)
for i in cow:
image=tf.keras.preprocessing.image.load_img(i, color_mode='RGB',
target_size= (280,280))
image=np.array(image)
data.append(image)
labels.append(2)
for i in zebra:
image=tf.keras.preprocessing.image.load_img(i, color_mode='RGB',
target_size= (280,280))
image=np.array(image)
data.append(image)
labels.append(3)
data = np.array(data)
labels = np.array(labels)
from sklearn.model_selection import train_test_split
X_train, X_test, ytrain, ytest = train_test_split(data, labels, test_size=0.2,
random_state=42)
第一种方法的主要缺点，不能用于显示图片。如果您写validation_dataset[1]，它将出错。但是，如果我使用第一种方法，它会起作用：X_test[1]
答案 2 :(得分：5)
我有一个PR。一种方法是对文件名进行散列并进行变体赋值。
示例：
# -*- coding: utf-8 -*-
"""Train model using transfer learning."""
import os
import re
import glob
import hashlib
import argparse
import warnings
import six
import numpy as np
import tensorflow as tf
from tensorflow.python.platform import gfile
from keras.models import Model
from keras import backend as K
from keras.optimizers import SGD
from keras.layers import Dense, GlobalAveragePooling2D, Input
from keras.applications.inception_v3 import InceptionV3
from keras.preprocessing.image import (ImageDataGenerator, Iterator,
array_to_img, img_to_array, load_img)
from keras.callbacks import ModelCheckpoint, TensorBoard, EarlyStopping
RANDOM_SEED = 0
MAX_NUM_IMAGES_PER_CLASS = 2 ** 27 - 1 # ~134M
VALID_IMAGE_FORMATS = frozenset(['jpg', 'jpeg', 'JPG', 'JPEG'])
# we chose to train the top 2 inception blocks
BATCH_SIZE = 100
TRAINABLE_LAYERS = 172
INCEPTIONV3_BASE_LAYERS = len(InceptionV3(weights=None, include_top=False).layers)
STEPS_PER_EPOCH = 625
VALIDATION_STEPS = 100
MODEL_INPUT_WIDTH = 299
MODEL_INPUT_HEIGHT = 299
MODEL_INPUT_DEPTH = 3
FC_LAYER_SIZE = 1024
# Helper: Save the model.
checkpointer = ModelCheckpoint(
filepath='./output/checkpoints/inception.{epoch:03d}-{val_loss:.2f}.hdf5',
verbose=1,
save_best_only=True)
# Helper: Stop when we stop learning.
early_stopper = EarlyStopping(patience=10)
# Helper: TensorBoard
tensorboard = TensorBoard(log_dir='./output/')
def as_bytes(bytes_or_text, encoding='utf-8'):
"""Converts bytes or unicode to `bytes`, using utf-8 encoding for text.
# Arguments
bytes_or_text: A `bytes`, `str`, or `unicode` object.
encoding: A string indicating the charset for encoding unicode.
# Returns
A `bytes` object.
# Raises
TypeError: If `bytes_or_text` is not a binary or unicode string.
"""
if isinstance(bytes_or_text, six.text_type):
return bytes_or_text.encode(encoding)
elif isinstance(bytes_or_text, bytes):
return bytes_or_text
else:
raise TypeError('Expected binary or unicode string, got %r' %
(bytes_or_text,))
class CustomImageDataGenerator(ImageDataGenerator):
def flow_from_image_lists(self, image_lists,
category, image_dir,
target_size=(256, 256), color_mode='rgb',
class_mode='categorical',
batch_size=32, shuffle=True, seed=None,
save_to_dir=None,
save_prefix='',
save_format='jpeg'):
return ImageListIterator(
image_lists, self,
category, image_dir,
target_size=target_size, color_mode=color_mode,
class_mode=class_mode,
data_format=self.data_format,
batch_size=batch_size, shuffle=shuffle, seed=seed,
save_to_dir=save_to_dir,
save_prefix=save_prefix,
save_format=save_format)
class ImageListIterator(Iterator):
"""Iterator capable of reading images from a directory on disk.
# Arguments
image_lists: Dictionary of training images for each label.
image_data_generator: Instance of `ImageDataGenerator`
to use for random transformations and normalization.
target_size: tuple of integers, dimensions to resize input images to.
color_mode: One of `"rgb"`, `"grayscale"`. Color mode to read images.
classes: Optional list of strings, names of sudirectories
containing images from each class (e.g. `["dogs", "cats"]`).
It will be computed automatically if not set.
class_mode: Mode for yielding the targets:
`"binary"`: binary targets (if there are only two classes),
`"categorical"`: categorical targets,
`"sparse"`: integer targets,
`None`: no targets get yielded (only input images are yielded).
batch_size: Integer, size of a batch.
shuffle: Boolean, whether to shuffle the data between epochs.
seed: Random seed for data shuffling.
data_format: String, one of `channels_first`, `channels_last`.
save_to_dir: Optional directory where to save the pictures
being yielded, in a viewable format. This is useful
for visualizing the random transformations being
applied, for debugging purposes.
save_prefix: String prefix to use for saving sample
images (if `save_to_dir` is set).
save_format: Format to use for saving sample images
(if `save_to_dir` is set).
"""
def __init__(self, image_lists, image_data_generator,
category, image_dir,
target_size=(256, 256), color_mode='rgb',
class_mode='categorical',
batch_size=32, shuffle=True, seed=None,
data_format=None,
save_to_dir=None, save_prefix='', save_format='jpeg'):
if data_format is None:
data_format = K.image_data_format()
classes = list(image_lists.keys())
self.category = category
self.num_class = len(classes)
self.image_lists = image_lists
self.image_dir = image_dir
how_many_files = 0
for label_name in classes:
for _ in self.image_lists[label_name][category]:
how_many_files += 1
self.samples = how_many_files
self.class2id = dict(zip(classes, range(len(classes))))
self.id2class = dict((v, k) for k, v in self.class2id.items())
self.classes = np.zeros((self.samples,), dtype='int32')
self.image_data_generator = image_data_generator
self.target_size = tuple(target_size)
if color_mode not in {'rgb', 'grayscale'}:
raise ValueError('Invalid color mode:', color_mode,
'; expected "rgb" or "grayscale".')
self.color_mode = color_mode
self.data_format = data_format
if self.color_mode == 'rgb':
if self.data_format == 'channels_last':
self.image_shape = self.target_size + (3,)
else:
self.image_shape = (3,) + self.target_size
else:
if self.data_format == 'channels_last':
self.image_shape = self.target_size + (1,)
else:
self.image_shape = (1,) + self.target_size
if class_mode not in {'categorical', 'binary', 'sparse', None}:
raise ValueError('Invalid class_mode:', class_mode,
'; expected one of "categorical", '
'"binary", "sparse", or None.')
self.class_mode = class_mode
self.save_to_dir = save_to_dir
self.save_prefix = save_prefix
self.save_format = save_format
i = 0
self.filenames = []
for label_name in classes:
for j, _ in enumerate(self.image_lists[label_name][category]):
self.classes[i] = self.class2id[label_name]
img_path = get_image_path(self.image_lists,
label_name,
j,
self.image_dir,
self.category)
self.filenames.append(img_path)
i += 1
print("Found {} {} files".format(len(self.filenames), category))
super(ImageListIterator, self).__init__(self.samples, batch_size, shuffle,
seed)
def next(self):
"""For python 2.x.
# Returns
The next batch.
"""
with self.lock:
index_array, current_index, current_batch_size = next(
self.index_generator)
# The transformation of images is not under thread lock
# so it can be done in parallel
batch_x = np.zeros((current_batch_size,) + self.image_shape,
dtype=K.floatx())
grayscale = self.color_mode == 'grayscale'
# build batch of image data
for i, j in enumerate(index_array):
img = load_img(self.filenames[j],
grayscale=grayscale,
target_size=self.target_size)
x = img_to_array(img, data_format=self.data_format)
x = self.image_data_generator.random_transform(x)
x = self.image_data_generator.standardize(x)
batch_x[i] = x
# optionally save augmented images to disk for debugging purposes
if self.save_to_dir:
for i in range(current_batch_size):
img = array_to_img(batch_x[i], self.data_format, scale=True)
fname = '{prefix}_{index}_{hash}.{format}'.format(
prefix=self.save_prefix,
index=current_index + i,
hash=np.random.randint(10000),
format=self.save_format)
img.save(os.path.join(self.save_to_dir, fname))
# build batch of labels
if self.class_mode == 'sparse':
batch_y = self.classes[index_array]
elif self.class_mode == 'binary':
batch_y = self.classes[index_array].astype(K.floatx())
elif self.class_mode == 'categorical':
batch_y = np.zeros((len(batch_x), self.num_class),
dtype=K.floatx())
for i, label in enumerate(self.classes[index_array]):
batch_y[i, label] = 1.
else:
return batch_x
return batch_x, batch_y
# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py
def create_image_lists(image_dir, validation_pct=10):
"""Builds a list of training images from the file system.
Analyzes the sub folders in the image directory, splits them into stable
training, testing, and validation sets, and returns a data structure
describing the lists of images for each label and their paths.
# Arguments
image_dir: string path to a folder containing subfolders of images.
validation_pct: integer percentage of images reserved for validation.
# Returns
dictionary of label subfolder, with images split into training
and validation sets within each label.
"""
if not os.path.isdir(image_dir):
raise ValueError("Image directory {} not found.".format(image_dir))
image_lists = {}
sub_dirs = [x[0] for x in os.walk(image_dir)]
sub_dirs_without_root = sub_dirs[1:] # first element is root directory
for sub_dir in sub_dirs_without_root:
file_list = []
dir_name = os.path.basename(sub_dir)
if dir_name == image_dir:
continue
print("Looking for images in '{}'".format(dir_name))
for extension in VALID_IMAGE_FORMATS:
file_glob = os.path.join(image_dir, dir_name, '*.' + extension)
file_list.extend(glob.glob(file_glob))
if not file_list:
warnings.warn('No files found')
continue
if len(file_list) < 20:
warnings.warn('Folder has less than 20 images, which may cause '
'issues.')
elif len(file_list) > MAX_NUM_IMAGES_PER_CLASS:
warnings.warn('WARNING: Folder {} has more than {} images. Some '
'images will never be selected.'
.format(dir_name, MAX_NUM_IMAGES_PER_CLASS))
label_name = re.sub(r'[^a-z0-9]+', ' ', dir_name.lower())
training_images = []
validation_images = []
for file_name in file_list:
base_name = os.path.basename(file_name)
# Get the hash of the file name and perform variant assignment.
hash_name = hashlib.sha1(as_bytes(base_name)).hexdigest()
hash_pct = ((int(hash_name, 16) % (MAX_NUM_IMAGES_PER_CLASS + 1)) *
(100.0 / MAX_NUM_IMAGES_PER_CLASS))
if hash_pct < validation_pct:
validation_images.append(base_name)
else:
training_images.append(base_name)
image_lists[label_name] = {
'dir': dir_name,
'training': training_images,
'validation': validation_images,
}
return image_lists
# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py
def get_image_path(image_lists, label_name, index, image_dir, category):
""""Returns a path to an image for a label at the given index.
# Arguments
image_lists: Dictionary of training images for each label.
label_name: Label string we want to get an image for.
index: Int offset of the image we want. This will be moduloed by the
available number of images for the label, so it can be arbitrarily large.
image_dir: Root folder string of the subfolders containing the training
images.
category: Name string of set to pull images from - training, testing, or
validation.
# Returns
File system path string to an image that meets the requested parameters.
"""
if label_name not in image_lists:
raise ValueError('Label does not exist ', label_name)
label_lists = image_lists[label_name]
if category not in label_lists:
raise ValueError('Category does not exist ', category)
category_list = label_lists[category]
if not category_list:
raise ValueError('Label %s has no images in the category %s.',
label_name, category)
mod_index = index % len(category_list)
base_name = category_list[mod_index]
sub_dir = label_lists['dir']
full_path = os.path.join(image_dir, sub_dir, base_name)
return full_path
def get_generators(image_lists, image_dir):
train_datagen = CustomImageDataGenerator(rescale=1. / 255,
horizontal_flip=True)
test_datagen = CustomImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_image_lists(
image_lists=image_lists,
category='training',
image_dir=image_dir,
target_size=(MODEL_INPUT_HEIGHT, MODEL_INPUT_WIDTH),
batch_size=BATCH_SIZE,
class_mode='categorical',
seed=RANDOM_SEED)
validation_generator = test_datagen.flow_from_image_lists(
image_lists=image_lists,
category='validation',
image_dir=image_dir,
target_size=(MODEL_INPUT_HEIGHT, MODEL_INPUT_WIDTH),
batch_size=BATCH_SIZE,
class_mode='categorical',
seed=RANDOM_SEED)
return train_generator, validation_generator
def get_model(num_classes, weights='imagenet'):
# create the base pre-trained model
# , input_tensor=input_tensor
base_model = InceptionV3(weights=weights, include_top=False)
# add a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)
# let's add a fully-connected layer
x = Dense(FC_LAYER_SIZE, activation='relu')(x)
# and a logistic layer -- let's say we have 2 classes
predictions = Dense(num_classes, activation='softmax')(x)
# this is the model we will train
model = Model(inputs=[base_model.input], outputs=[predictions])
return model
def get_top_layer_model(model):
"""Used to train just the top layers of the model."""
# first: train only the top layers (which were randomly initialized)
# i.e. freeze all convolutional InceptionV3 layers
for layer in model.layers[:INCEPTIONV3_BASE_LAYERS]:
layer.trainable = False
for layer in model.layers[INCEPTIONV3_BASE_LAYERS:]:
layer.trainable = True
# compile the model (should be done after setting layers to non-trainable)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy',
metrics=['accuracy'])
return model
def get_mid_layer_model(model):
"""After we fine-tune the dense layers, train deeper."""
# freeze the first TRAINABLE_LAYER_INDEX layers and unfreeze the rest
for layer in model.layers[:TRAINABLE_LAYERS]:
layer.trainable = False
for layer in model.layers[TRAINABLE_LAYERS:]:
layer.trainable = True
# we need to recompile the model for these modifications to take effect
# we use SGD with a low learning rate
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9),
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
def train_model(model, epochs, generators, callbacks=None):
train_generator, validation_generator = generators
model.fit_generator(
train_generator,
steps_per_epoch=STEPS_PER_EPOCH,
validation_data=validation_generator,
validation_steps=VALIDATION_STEPS,
epochs=epochs,
callbacks=callbacks)
return model
def main(image_dir, validation_pct):
sub_dirs = [x[0] for x in gfile.Walk(image_dir)]
num_classes = len(sub_dirs) - 1
print("Number of classes found: {}".format(num_classes))
model = get_model(num_classes)
print("Using validation percent of %{}".format(validation_pct))
image_lists = create_image_lists(image_dir, validation_pct)
generators = get_generators(image_lists, image_dir)
# Get and train the top layers.
model = get_top_layer_model(model)
model = train_model(model, epochs=10, generators=generators)
# Get and train the mid layers.
model = get_mid_layer_model(model)
_ = train_model(model, epochs=100, generators=generators,
callbacks=[checkpointer, early_stopper, tensorboard])
# save model
model.save('./output/model.hdf5', overwrite=True)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--image-dir', required=True, help='data directory')
parser.add_argument('--validation-pct', default=10, help='validation percentage')
args = parser.parse_args()
os.makedirs('./output/checkpoints/', exist_ok=True)
main(**vars(args))
答案 3 :(得分：4)
参考这个问题https://github.com/keras-team/keras/issues/597，可以用下面的代码把整个集合拆分成train和val：
train_datagen = ImageDataGenerator(rescale=1./255,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True
validation_split=0.2) # val 20%
val_datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)
train_data = train_datagen.flow_from_directory(train_path,
target_size=(224, 224),
color_mode='rgb',
batch_size=BS,
class_mode='categorical',
shuffle=True,
subset = 'training')
val_data = val_datagen.flow_from_directory(train_path,
target_size=(224, 224),
color_mode='rgb',
batch_size=BS,
class_mode='categorical',
shuffle=False,
subset = 'validation')
答案 4 :(得分：3)
如果我们在ImageDataGenerator中使用subset，则相同的扩充将同时应用于训练和验证。如果只想对训练集应用增强，则可以使用split-folders软件包拆分文件夹，该软件包可以直接使用pip安装。
https://pypi.org/project/split-folders/
这会将数据集分为train，val和test目录，然后您可以为它们中的每一个创建单独的生成器。
答案 5 :(得分：1)
这是一个简单的tensorflow 2.0代码
from tensorflow.keras.preprocessing.image import ImageDataGenerator
def image_data_generator(data_dir,
data_augment=False,
batch_size=BATCH_SIZE,
target_size=(100, 100),
color_mode='rgb',
class_mode='binary',
shuffle=True):
if data_augment:
datagen = ImageDataGenerator(rescale=1./255,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
validation_split=0.2,#this is the trick
horizontal_flip=True)
else:
datagen = ImageDataGenerator(rescale=1./255)
generator = datagen.flow_from_directory(data_dir,
target_size=target_size,
color_mode=color_mode,
batch_size=batch_size,
shuffle=shuffle,
class_mode=class_mode)
return generator
train_generator = image_data_generator('Your_DataBase_Path',data_augment=True)
答案 6 :(得分：0)
如果您使用的是 TensorFlow 2.x，您可以使用相同的 fit() 函数并为 Image ImageDataGenerator 使用参数validation_split
答案 7 :(得分：0)
如果您正在为分割任务寻找生成器中的训练/验证拆分，您可以使用以下代码段：
from tensorflow.keras.preprocessing.image import ImageDataGenerator
BATCH_SIZE = 16
val_fraction = 0.1
image_generator = ImageDataGenerator(rescale=1/255,
brightness_range=[0.75, 1.75],
validation_split=val_fraction)
mask_generator = ImageDataGenerator(validation_split=val_fraction)
train_image_generator = image_generator.flow_from_dataframe(df_img,
directory=image_dir,
x_col='image',
class_mode=None,
color_mode='rgb',
target_size=(INPUT_SIZE, INPUT_SIZE),
batch_size=BATCH_SIZE,
shuffle=True,
subset='training',
seed=1)
train_mask_generator = mask_generator.flow_from_dataframe(df_gt,
directory=gt_dir,
x_col='mask',
color_mode='grayscale',
class_mode=None,
target_size=(INPUT_SIZE, INPUT_SIZE),
batch_size=BATCH_SIZE,
shuffle=True,
subset='training',
seed=1)
validation_image_generator = image_generator.flow_from_dataframe(df_img,
directory=image_dir,
x_col='image',
class_mode=None,
color_mode='rgb',
target_size=(INPUT_SIZE, INPUT_SIZE),
batch_size=BATCH_SIZE,
subset='validation',
seed=1)
validation_mask_generator = mask_generator.flow_from_dataframe(df_gt,
directory=gt_dir,
x_col='mask',
color_mode='grayscale',
class_mode=None,
target_size=(INPUT_SIZE, INPUT_SIZE),
batch_size=BATCH_SIZE,
subset='validation',
seed=1)
train_generator = zip(train_image_generator, train_mask_generator)
validation_generator = zip(validation_image_generator, validation_mask_generator)
答案 8 :(得分：0)
如果您只是想将数据集划分为训练和验证（没有任何增强等）
new Node(1, 6)答案 9 :(得分：-1)
我不知道您是否仍然感兴趣，但我找到了以下解决方法。最重要的函数是GetTrainValidTestGeneratorFromDir，其他函数只是被它使用。基本思想是首先使用validation_split 将ImageDataGenerator 一分为二。通过这种方式，您将获得两个迭代器。您可以使用第二个作为测试迭代器。您将按以下方式进一步划分第一个： 首先使用 flow_from_directory 使用训练子集（因此您可以确保排除测试数据）。现在您可以使用相同的生成器来获取两个分割的数据帧，然后您可以使用 flow_from_dataframe 函数。您将在不更改文件夹的情况下获得三个 ImageDataIterator
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 15 10:15:18 2021
@author: Alessandro
"""
import pandas as pd
from keras.preprocessing.image import ImageDataGenerator
def ShuffleDataframe(thedataframe):
thedataframe = thedataframe.sample(n=len(thedataframe), random_state=42)
thedataframe = thedataframe.reset_index()
thedataframe.drop('index', axis='columns', inplace=True)
return(thedataframe)
def TransformGeneratorClassNumberToLabels(theGenerator, theLabelsNumbers):
labelnames = theGenerator.class_indices
labelnames = list(labelnames.keys())
theLabelsString = [labelnames[i] for i in theLabelsNumbers]
return(theLabelsString)
def GetGeneratorDataframe(theGenerator):
training_filenames = theGenerator.filenames
theLabelsNumbers = theGenerator.classes
thelabelsString = TransformGeneratorClassNumberToLabels(theGenerator,
theLabelsNumbers)
thedataframe = pd.DataFrame({'File': training_filenames,
'Label': thelabelsString})
thedataframe = ShuffleDataframe(thedataframe)
return(thedataframe)
def GetTrainValidTestGeneratorFromDir(thedirectory,
input_shape= (256, 256, 3),
validation_split=0.1,
rescaling = 1./255):
train_datagen = ImageDataGenerator(rescale=1./255,
validation_split=0.2)
train_and_valid_generator = train_datagen.flow_from_directory(thedirectory,
target_size=input_shape[0:2],
batch_size=20,
class_mode="categorical",
subset = 'training',
save_to_dir ='checkdir')
test_generator = train_datagen.flow_from_directory(thedirectory,
target_size=input_shape[0:2],
batch_size=20,
class_mode="categorical",
subset = 'validation')
thedataframe = GetGeneratorDataframe(train_and_valid_generator)
class_mode = "categorical"
training_generator = train_datagen.flow_from_dataframe(dataframe = thedataframe,
directory = thedirectory,
target_size=input_shape[0:2],
x_col="File",
y_col = "Label",
subset= "training",
class_mode=class_mode)
validation_generator = train_datagen.flow_from_dataframe(dataframe = thedataframe,
directory = thedirectory,
target_size=input_shape[0:2],
x_col="File",
y_col = "Label",
subset= "validation",
class_mode=class_mode)
return training_generator, validation_generator, test_generator
input_shape = (450, 450, 3)
myDir = "MyFolder"
(training_generator,
validation_generator,
test_generator) = GetTrainValidTestGeneratorFromDir(myDir)
# next part is just to verify whhat you got
training_dataframe = GetGeneratorDataframe(training_generator)
valid_dataframe = GetGeneratorDataframe(validation_generator)
test_dataframe = GetGeneratorDataframe(test_generator)