Split data into batches

Question

I want to split my training data, test data and validation data into batches. I am working on Fashion MNIST dataset and accessing it directly from keras.datasets. I found the code mentioned below:

trainbatches = ImageDataGenerator().flowfromdirectory(trainpath, targetsize=(224,224), classes= classname, batchsize=10 testbatches = ImageDataGenerator().flowfromdirectory(testpath, targetsize=(224,224), classes= classname, batchsize=10
valbatches = ImageDataGenerator().flowfromdirectory(valpath, targetsize=(224,224), classes= classname, batch_size=10

As I have not downloaded the data on hard drive and accessing it from keras.datasets, how can I perform this action? I tried with ImageDataGenerator().flow but it does not work? Is there a way to perform this?

Answer 1

The Format Basically you used is incorrect the return format of keras dataset into seperate datalabel with images

This code work for me

# TensorFlow and tf.keras
import tensorflow as tf
from tensorflow import keras

# Helper libraries
import numpy as np
import matplotlib.pyplot as plt

print(tf.__version__)

fashion_mnist = keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

print(train_images.shape)
print(test_images.shape)


from keras.preprocessing.image import ImageDataGenerator
from keras.utils import np_utils
y_train = np_utils.to_categorical(train_labels, 10)
y_test = np_utils.to_categorical(test_labels,10)

datagen = ImageDataGenerator(
    featurewise_center=True,
    featurewise_std_normalization=True,
    rotation_range=20,
    width_shift_range=0.2,
    height_shift_range=0.2,
    horizontal_flip=True)


train_images=train_images.reshape(60000,28,28,1)
test_images=test_images.reshape(10000,28,28,1)

datagen.fit(train_images)

Prepare You Model and compile

# fits the model on batches with real-time data augmentation:
model.fit_generator(datagen.flow(train_images,train_labels, batch_size=32),
                steps_per_epoch=len(x_train) / 32, epochs=epochs)