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I tried to train a model for binary classification, but after 3 epoch the validation accuracy still stayed at 0.5000.

The dataset consist of 1512 images for both class, so it is a total of 3024 images. I used keras to do transfer learning using VGG16 model. 

from keras import models
from keras import layers
from keras import optimizers
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import TensorBoard, EarlyStopping, ModelCheckpoint

# Stop when we stop learning.
early_stopper = EarlyStopping(patience=10)
# tensorboard
tensorboard = TensorBoard(log_dir='./logs')
model_check_point = ModelCheckpoint('vgg16.h5', save_best_only=True)
train_dir = 'dataset\\training_set'
validation_dir = 'dataset\\validation_set'
image_size_x = 360
image_size_y = 180
#Load the VGG model
vgg_conv = VGG16(weights='imagenet', include_top=False, input_shape=(image_size_x, image_size_y, 3))
# Freeze the layers except the last 4 layers
for layer in vgg_conv.layers[:-4]:
    layer.trainable = False

# Create the model
model = models.Sequential()

# Add the vgg convolutional base model
model.add(vgg_conv)

# Add new layers
model.add(layers.Flatten())
model.add(layers.Dense(1024, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1, activation='sigmoid'))

train_datagen = ImageDataGenerator(
      rescale=1./255,
      rotation_range=20,
      width_shift_range=0.2,
      height_shift_range=0.2,
      horizontal_flip=True,
      fill_mode='nearest')

validation_datagen = ImageDataGenerator(rescale=1./255)

# Change the batchsize according to your system RAM
train_batchsize = 4
val_batchsize = 4

train_generator = train_datagen.flow_from_directory(
        train_dir,
        target_size=(image_size_x, image_size_y),
        batch_size=train_batchsize,
        class_mode='binary')

validation_generator = validation_datagen.flow_from_directory(
        validation_dir,
        target_size=(image_size_x, image_size_y),
        batch_size=val_batchsize,
        class_mode='binary')
# Compile the model
model.compile(loss='binary_crossentropy',
              optimizer=optimizers.Adam(lr=0.1),
              metrics=['acc'])
# Train the model
history = model.fit_generator(
      train_generator,
      steps_per_epoch=train_generator.samples/train_generator.batch_size ,
      epochs=7,
      validation_data=validation_generator,
      validation_steps=validation_generator.samples/validation_generator.batch_size,
      verbose=1,
      callbacks=[tensorboard, early_stopper, model_check_point])

The result was

Using TensorFlow backend.
2019-02-03 14:46:06.520723: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
Found 2416 images belonging to 2 classes.
Found 608 images belonging to 2 classes.
Epoch 1/7
604/604 [==============================] - 2046s 3s/step - loss: 8.0208 - acc: 0.5008 - val_loss: 8.0590 - val_acc: 0.5000
Epoch 2/7
604/604 [==============================] - 1798s 3s/step - loss: 8.0055 - acc: 0.5033 - val_loss: 8.0590 - val_acc: 0.5000
Epoch 3/7
604/604 [==============================] - 2500s 4s/step - loss: 8.0054 - acc: 0.5033 - val_loss: 8.0590 - val_acc: 0.5000

I tried to increase the learning rate from 0.0001 to 0.01, tried using another optimizer (RMSprop), but the validation accuracy still stays at 0.5000. Here is the model summary

_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
inception_v3 (Model)         (None, 9, 4, 2048)        21802784
=================================================================
Total params: 21,802,784
Trainable params: 0
Non-trainable params: 21,802,784
_________________________________________________________________
None
Penrose stairs
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1 Answers1

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VGG expects a different preprocessing than rescale = 1/255.. You can import the proper preprocessing input function from the same module and give it to the ImageDataGenerator: 

from keras.applications.vgg16 import preprocess_input
train_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)

Note that you should omit the rescale argument as this is handled by the preprocessing function. See also this answer.

If that doesn't help, I suggest you try freezing the entire convolutional base and only training the top classifier. The convolutional base contains pretrained weights, whereas the weights in the top classifier are randomly initialized. This might cause large gradient updates to the pretrained weights which renders them useless. In addition, you don't have that many data so it might a good idea to freeze the convolutional stack regardless of whether it causes your issue here. See this answer for an example of someone who ran into this problem with VGG.

Your model definition looks correct apart from that.

sdcbr
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