Loss validation that don't decrease in Keras images classification - image-processing

Question

I'm trying to finetune my VGG19 model with a bunch of images for classification.
have 18 classes with 6000 images in each well currated.
Using Keras 2.2.4
Model:
INIT_LR = 0.00001
BATCH_SIZE = 128
IMG_SIZE = (256, 256)
epochs = 150
model_base = keras.applications.vgg19.VGG19(include_top=False, input_shape=(*IMG_SIZE, 3), weights='imagenet')
output = Flatten()(model_base.output)
output = BatchNormalization()(output)
output = Dropout(0.5)(output)
output = Dense(64, activation='relu')(output)
output = BatchNormalization()(output)
output = Dropout(0.5)(output)
output = Dense(len(all_character_names), activation='softmax')(output)
model = Model(model_base.input, output)
for layer in model_base.layers[:-10]:
layer.trainable = False
opt = optimizers.Adam(lr=INIT_LR, decay=INIT_LR / epochs)
model.compile(optimizer=opt,
loss='categorical_crossentropy',
metrics=['accuracy', 'top_k_categorical_accuracy'])
Data augmentation:
image_datagen = ImageDataGenerator(
rotation_range=15,
width_shift_range=.15,
height_shift_range=.15,
#rescale=1./255,
shear_range=0.15,
zoom_range=0.15,
channel_shift_range=1,
vertical_flip=True,
horizontal_flip=True)
Model train:
validation_steps = data_generator.validation_samples/BATCH_SIZE
steps_per_epoch = data_generator.train_samples/BATCH_SIZE
model.fit_generator(
generator,
steps_per_epoch=steps_per_epoch,
epochs=epochs,
validation_data=validation_data,
validation_steps=validation_steps
)
Model summary:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 256, 256, 3) 0
_________________________________________________________________
block1_conv1 (Conv2D) (None, 256, 256, 64) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, 256, 256, 64) 36928
_________________________________________________________________
block1_pool (MaxPooling2D) (None, 128, 128, 64) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, 128, 128, 128) 73856
_________________________________________________________________
block2_conv2 (Conv2D) (None, 128, 128, 128) 147584
_________________________________________________________________
block2_pool (MaxPooling2D) (None, 64, 64, 128) 0
_________________________________________________________________
block3_conv1 (Conv2D) (None, 64, 64, 256) 295168
_________________________________________________________________
block3_conv2 (Conv2D) (None, 64, 64, 256) 590080
_________________________________________________________________
block3_conv3 (Conv2D) (None, 64, 64, 256) 590080
_________________________________________________________________
block3_conv4 (Conv2D) (None, 64, 64, 256) 590080
_________________________________________________________________
block3_pool (MaxPooling2D) (None, 32, 32, 256) 0
_________________________________________________________________
block4_conv1 (Conv2D) (None, 32, 32, 512) 1180160
_________________________________________________________________
block4_conv2 (Conv2D) (None, 32, 32, 512) 2359808
_________________________________________________________________
block4_conv3 (Conv2D) (None, 32, 32, 512) 2359808
_________________________________________________________________
block4_conv4 (Conv2D) (None, 32, 32, 512) 2359808
_________________________________________________________________
block4_pool (MaxPooling2D) (None, 16, 16, 512) 0
_________________________________________________________________
block5_conv1 (Conv2D) (None, 16, 16, 512) 2359808
_________________________________________________________________
block5_conv2 (Conv2D) (None, 16, 16, 512) 2359808
_________________________________________________________________
block5_conv3 (Conv2D) (None, 16, 16, 512) 2359808
_________________________________________________________________
block5_conv4 (Conv2D) (None, 16, 16, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, 8, 8, 512) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 32768) 0
_________________________________________________________________
batch_normalization_1 (Batch (None, 32768) 131072
_________________________________________________________________
dropout_1 (Dropout) (None, 32768) 0
_________________________________________________________________
dense_1 (Dense) (None, 64) 2097216
_________________________________________________________________
batch_normalization_2 (Batch (None, 64) 256
_________________________________________________________________
dropout_2 (Dropout) (None, 64) 0
_________________________________________________________________
dense_2 (Dense) (None, 19) 1235
=================================================================
Total params: 22,254,163
Trainable params: 19,862,931
Non-trainable params: 2,391,232
_________________________________________________________________
<keras.engine.input_layer.InputLayer object at 0x00000224568D0D68> False
<keras.layers.convolutional.Conv2D object at 0x00000224568D0F60> False
<keras.layers.convolutional.Conv2D object at 0x00000224568F0438> False
<keras.layers.pooling.MaxPooling2D object at 0x00000224570A5860> False
<keras.layers.convolutional.Conv2D object at 0x00000224570A58D0> False
<keras.layers.convolutional.Conv2D object at 0x00000224574196D8> False
<keras.layers.pooling.MaxPooling2D object at 0x0000022457524048> False
<keras.layers.convolutional.Conv2D object at 0x0000022457524D30> False
<keras.layers.convolutional.Conv2D object at 0x0000022457053160> False
<keras.layers.convolutional.Conv2D object at 0x00000224572E15C0> False
<keras.layers.convolutional.Conv2D object at 0x000002245707B080> False
<keras.layers.pooling.MaxPooling2D object at 0x0000022457088400> False
<keras.layers.convolutional.Conv2D object at 0x0000022457088E10> True
<keras.layers.convolutional.Conv2D object at 0x00000224575DB240> True
<keras.layers.convolutional.Conv2D object at 0x000002245747A320> True
<keras.layers.convolutional.Conv2D object at 0x0000022457486160> True
<keras.layers.pooling.MaxPooling2D object at 0x00000224574924E0> True
<keras.layers.convolutional.Conv2D object at 0x0000022457492D68> True
<keras.layers.convolutional.Conv2D object at 0x00000224574AD320> True
<keras.layers.convolutional.Conv2D object at 0x00000224574C6400> True
<keras.layers.convolutional.Conv2D object at 0x00000224574D2240> True
<keras.layers.pooling.MaxPooling2D object at 0x00000224574DAF98> True
<keras.layers.core.Flatten object at 0x00000224574EA080> True
<keras.layers.normalization.BatchNormalization object at 0x00000224574F82B0> True
<keras.layers.core.Dropout object at 0x000002247134BA58> True
<keras.layers.core.Dense object at 0x000002247136A7B8> True
<keras.layers.normalization.BatchNormalization object at 0x0000022471324438> True
<keras.layers.core.Dropout object at 0x00000224713249B0> True
<keras.layers.core.Dense object at 0x00000224713BF7F0> True
batchsize:128
LR:1e-05
The doomed graph:
Tries:
Tryed several LR
Tryed without training last 10, 5 layers, it is worst, simply not converging
Tryed several batch size, 128 give the best results
Also tryed resnet50 but not converging at all (even with last 3 layers trainables)
Tryed VGG16 with not much luck.
I add about 2000 new images each days to try to reach around 20 000 images per classe as I think this is here my problem.

Answer 1

In lower layer, network has learned low level features like edges, contours etc. It is higher layer, where these features combined. So in your case, you need much finer features such as hair color, person size etc. One thing you can tried that fine tune from last few layer(from block 4-5). Also you can use different learning rate, very low for VGG blocks and little higher for completely new dense layer. For implemention, this-thread will be helpful.