title: Keras - .flow_from_directory(directory)
body: <p>I am trying to run an example of Resnet with cifar10 dataset using <code>.flow_from_directory(directory)</code>. The below code is below: </p>
  
  <pre><code>from __future__ import print_function
  from keras.datasets import cifar10
  from keras.preprocessing.image import ImageDataGenerator
  from keras.utils import np_utils
  from keras.callbacks import ReduceLROnPlateau, CSVLogger, EarlyStopping
  
  import numpy as np
  import resnet
  import os
  import cv2
  import csv
  #import keras 
  
  os.environ["CUDA_VISIBLE_DEVICES"] = "1"
  
  
  # input image dimensions
  img_rows, img_cols = 32, 32
  # The CIFAR10 images are RGB.
  img_channels = 3
  nb_classes = 10
  
  
  train_datagen = ImageDataGenerator(
          rescale=1./255,
          shear_range=0,
          zoom_range=0,
          horizontal_flip=False,
          width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
          height_shift_range=0.1)  # randomly shift images vertically (fraction of total height))
  
  test_datagen = ImageDataGenerator(rescale=1./255)  
  
  train_generator = train_datagen.flow_from_directory(
          '/home/datasets/cifar10/train',
          target_size=(32, 32),
          batch_size=32,
          shuffle=False)
  
  validation_generator = test_datagen.flow_from_directory(
          '/home/datasets/cifar10/test',
          target_size=(32, 32),
          batch_size=32,
          shuffle=False)
  
  model = resnet.ResnetBuilder.build_resnet_18((img_channels, img_rows, img_cols), nb_classes)
  model.compile(loss='categorical_crossentropy',
                optimizer='adam',
                metrics=['accuracy'])
  
  model.fit_generator(
          train_generator,
          steps_per_epoch=500,
          epochs=50,
          validation_data=validation_generator,
          validation_steps=250)
  </code></pre>
  
  <p>However, I am obtaining the following accuracy value. </p>
  
  <pre><code>500/500 [==============================] - 22s - loss: 0.8139 - acc: 0.9254 - val_loss: 12.7198 - val_acc: 0.1250
  Epoch 2/50
  500/500 [==============================] - 19s - loss: 1.0645 - acc: 0.8856 - val_loss: 8.4179 - val_acc: 0.0560
  Epoch 3/50
  500/500 [==============================] - 19s - loss: 2.1014 - acc: 0.7492 - val_loss: 10.7770 - val_acc: 0.0956
  Epoch 4/50
  500/500 [==============================] - 19s - loss: 1.6806 - acc: 0.7772 - val_loss: 6.1023 - val_acc: 0.0741
  Epoch 5/50
  500/500 [==============================] - 19s - loss: 1.1798 - acc: 0.8669 - val_loss: 6.9016 - val_acc: 0.1253
  Epoch 6/50
  500/500 [==============================] - 19s - loss: 1.5448 - acc: 0.8369 - val_loss: 3.6371 - val_acc: 0.0370
  Epoch 7/50
  500/500 [==============================] - 19s - loss: 1.3763 - acc: 0.8599 - val_loss: 4.8012 - val_acc: 0.1204
  Epoch 8/50
  500/500 [==============================] - 19s - loss: 1.0186 - acc: 0.8891 - val_loss: 6.8395 - val_acc: 0.0912
  Epoch 9/50
  500/500 [==============================] - 19s - loss: 0.9477 - acc: 0.9081 - val_loss: 10.4287 - val_acc: 0.1253
  Epoch 10/50
  500/500 [==============================] - 19s - loss: 1.0689 - acc: 0.8686 - val_loss: 7.9931 - val_acc: 0.1253
  </code></pre>
  
  <p>I am using Resnet from this <a href="https://github.com/raghakot/keras-resnet/blob/master/resnet.py" rel="noreferrer">link</a>. I tried numerous examples to sort the problem including the one on the official documentation. However, I am unable to resolve the problem. Training accuracy is changing however val accuracy is somewhat constatn.
   Can some one point the problem</p>