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I am working on a text recognition problem, in which essentially I am trying to read images similar to captchas.

I implemented a ResNet in keras and I run it on colab with gpu.

Because I cannot upload a million pictures I have created a loop where I train the model in a subset (10000 pics) save them and then load the next subset of pics and continue training.

I did some hyper tuning by trial and error but mostly I am using the original ResNet. My batch size is 128 and epoch = 30.

The model reaches fast a validation accuracy of 14% (after training it to 50k images) but I see no further improvement after this. Theoretically I would expect that the more I train it the more the improvement.

Question 1: What should I do next in order to improve the model accuracy?

Secondly, when I train it. I have some sudden drops in the validation accuracy and I can't understand why this happens.

enter image description here

Finally, most of the times I reach a pick of validation accuracy at the early stage (low number of epoch) and later it goes a bit lower.

Question 2: Why the model behaves like that?

Any ideas around the topic would be very welcome :) Thank you

The code is the following:

Note: I know it is quite a lot, I add it just in case.

load_res_net =  False
if load_res_net:

  import keras
  from keras.layers import Dense, Conv2D, BatchNormalization, Activation
  from keras.layers import AveragePooling2D, Input, Flatten
  from keras.optimizers import Adam
  from keras.callbacks import ModelCheckpoint, LearningRateScheduler
  from keras.callbacks import ReduceLROnPlateau
  from keras.preprocessing.image import ImageDataGenerator
  from keras.regularizers import l2
  from keras import backend as K
  from keras.models import Model
  from keras.datasets import cifar10
  import numpy as np
  import os


  # Training parameters
  batch_size = 32  # orig paper trained all networks with batch_size=128
  epochs = 200
  num_classes = alphabet.__len__()*10
  input_shape = (pic_dim_for_model[1], pic_dim_for_model[0], 1)


  # Subtracting pixel mean improves accuracy
  subtract_pixel_mean = True

  n = 3
  # Model version
  # Orig paper: version = 1 (ResNet v1), Improved ResNet: version = 2 (ResNet v2)
  version = 1

  # Computed depth from supplied model parameter n
  if version == 1:
      depth = n * 6 + 2
  elif version == 2:
      depth = n * 9 + 2

  # Model name, depth and version
  model_type = 'ResNet%dv%d' % (depth, version)


  def lr_schedule(epoch):
      """Learning Rate Schedule

      Learning rate is scheduled to be reduced after 80, 120, 160, 180 epochs.
      Called automatically every epoch as part of callbacks during training.

      # Arguments
          epoch (int): The number of epochs

      # Returns
          lr (float32): learning rate
      """
      lr = 1e-3
      if epoch > 180:
          lr *= 0.5e-3
      elif epoch > 160:
          lr *= 1e-3
      elif epoch > 120:
          lr *= 1e-2
      elif epoch > 80:
          lr *= 1e-1
      print('Learning rate: ', lr)
      return lr


  def resnet_layer(inputs,
                   num_filters=16,
                   kernel_size=3,
                   strides=1,
                   activation='relu',
                   batch_normalization=True,
                   conv_first=True):
      """2D Convolution-Batch Normalization-Activation stack builder

      # Arguments
          inputs (tensor): input tensor from input image or previous layer
          num_filters (int): Conv2D number of filters
          kernel_size (int): Conv2D square kernel dimensions
          strides (int): Conv2D square stride dimensions
          activation (string): activation name
          batch_normalization (bool): whether to include batch normalization
          conv_first (bool): conv-bn-activation (True) or
              bn-activation-conv (False)

      # Returns
          x (tensor): tensor as input to the next layer
      """
      conv = Conv2D(num_filters,
                    kernel_size=kernel_size,
                    strides=strides,
                    padding='same',
                    kernel_initializer='he_normal',
                    kernel_regularizer=l2(1e-4))

      x = inputs
      if conv_first:
          x = conv(x)
          if batch_normalization:
              x = BatchNormalization()(x)
          if activation is not None:
              x = Activation(activation)(x)
      else:
          if batch_normalization:
              x = BatchNormalization()(x)
          if activation is not None:
              x = Activation(activation)(x)
          x = conv(x)
      return x


  def resnet_v1(input_shape, depth, num_classes):
      """ResNet Version 1 Model builder [a]

      Stacks of 2 x (3 x 3) Conv2D-BN-ReLU
      Last ReLU is after the shortcut connection.
      At the beginning of each stage, the feature map size is halved (downsampled)
      by a convolutional layer with strides=2, while the number of filters is
      doubled. Within each stage, the layers have the same number filters and the
      same number of filters.
      Features maps sizes:
      stage 0: 32x32, 16
      stage 1: 16x16, 32
      stage 2:  8x8,  64
      The Number of parameters is approx the same as Table 6 of [a]:
      ResNet20 0.27M
      ResNet32 0.46M
      ResNet44 0.66M
      ResNet56 0.85M
      ResNet110 1.7M

      # Arguments
          input_shape (tensor): shape of input image tensor
          depth (int): number of core convolutional layers
          num_classes (int): number of classes (CIFAR10 has 10)

      # Returns
          model (Model): Keras model instance
      """
      if (depth - 2) % 6 != 0:
          raise ValueError('depth should be 6n+2 (eg 20, 32, 44 in [a])')
      # Start model definition.
      num_filters = 16
      num_res_blocks = int((depth - 2) / 6)

      inputs = Input(shape=input_shape)
      x = resnet_layer(inputs=inputs)
      # Instantiate the stack of residual units
      for stack in range(3):
          for res_block in range(num_res_blocks):
              strides = 1
              if stack > 0 and res_block == 0:  # first layer but not first stack
                  strides = 2  # downsample
              y = resnet_layer(inputs=x,
                               num_filters=num_filters,
                               strides=strides)
              y = resnet_layer(inputs=y,
                               num_filters=num_filters,
                               activation=None)
              if stack > 0 and res_block == 0:  # first layer but not first stack
                  # linear projection residual shortcut connection to match
                  # changed dims
                  x = resnet_layer(inputs=x,
                                   num_filters=num_filters,
                                   kernel_size=1,
                                   strides=strides,
                                   activation=None,
                                   batch_normalization=False)
              x = keras.layers.add([x, y])
              x = Activation('relu')(x)
          num_filters *= 2

      # Add classifier on top.
      # v1 does not use BN after last shortcut connection-ReLU
      x = AveragePooling2D(pool_size=8)(x)
      y = Flatten()(x)
      outputs = Dense(num_classes,
                      activation='softmax',
                      kernel_initializer='he_normal')(y)

      # Instantiate model.
      model = Model(inputs=inputs, outputs=outputs)
      return model
subtract_pixel_mean = True
for tries in range(0,10):
  data, labels = generate_captchas(number_of_generated_images_to_create = 8000, fonts_path = data_path + '/fonts', lines = 1)
  data2, labels2 = generate_captchas(number_of_generated_images_to_create = 8000, fonts_path = data_path + '/fonts', lines = 2)
  data = data + data2
  labels = labels + labels2
  del data2, labels2

  data = np.array(data, dtype="float") / 255.0
  labels = np.array(labels)
  print('try : ' + str(tries))
  print('images loaded: ' + str(len(labels)))

  # Split the training data into separate train and test sets
  (X_train, X_test, Y_train, Y_test) = train_test_split(data, labels, test_size=0.25, random_state=0)
  Y_test = np.array([one_hot_encoding_for_word(x) for x in Y_test])
  Y_train = np.array([one_hot_encoding_for_word(x) for x in Y_train])
  del data

  # If subtract pixel mean is enabled
  if subtract_pixel_mean:
      x_train_mean = np.mean(X_train, axis=0)
      X_train -= x_train_mean
      X_test -= x_train_mean


  # load next part of data, and the model and continue training

  model.fit(X_train, Y_train, validation_data = (X_test, Y_test), batch_size= 2**7, epochs=40, verbose=2)
  # Save the trained model to disk
  model.save(data_path + '/' + name_of_model +'.h5')
  del  X_train, Y_train
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  • $\begingroup$ Please try to be more be specific or provide additional details. Asking for ideas is generally too broad for this type of forum. $\endgroup$ – oW_ May 20 '19 at 15:21
  • $\begingroup$ There may be some overfitting because of the large difference in loss and validation loss. Would need to see the model. Did you work on that? Another question: Isn't it possible to extract the text from the pictures (e.g. using Tesseract OCR) and train on text instead on images? $\endgroup$ – Peter May 20 '19 at 16:40
  • $\begingroup$ @oW_ I added the code and made it more specific. I avoided to add the code because it is quite a lot and maybe it is overwhelming $\endgroup$ – George Sotiropoulos May 20 '19 at 18:00
  • $\begingroup$ @Peter. In most of the epochs the train and test accuracy have less than 4% difference, so I guess it is not enough to indicate over-fitting. I want to extract the text itself and an ocr would not be able to do that, thats the reason that i am using NN $\endgroup$ – George Sotiropoulos May 20 '19 at 18:03

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