I am using Keras 2.0.4 (TensorFlow backend) for an image classification task (based on pretrained models).
During training/tuning I track all used metrics (e.g. categorical_accuracy
, categorical crossentropy
) with CSVLogger
- including the corresponding metrics being associated with the validation set (i.e. val_categorical_accuracy
, val_categorical_crossentropy
).
With the callback ModelCheckpoint
I am tracking the best configuration of weights (save_best_only=True
). In order to evaluate the model on the validation set I use model.evaluate()
.
My expectation is: tracked metrics by CSVLogger
(of 'best' epoch) equal the metrics calculated by model.evaluate()
.
Unfortunately this is NOT the case. Metrics differ by +- 5%.
Is there a reason for this behavior?
E D I T:
After some testing I could gain some insights:
- If I don't use a generator for training and validation data (and therefore no
model.fit_generator()
), the problem doesn't occur. --> Using the ImageDataGenerator
for training and validation data is the source of the discrepancy. (Please note, for calculation of evaluate
I don't use a generator, but I do use the same validation data (at least if DataImageGenerator
would work as expected...).
I think, the ImageDataGenerator doesn't work as it should (please,
also have a look at this).
- If I use no generators at all, there won't be this problem. Id est tracked metrics by
CSVLogger
(of 'best' epoch) equal the metrics calculated by model.evaluate()
.
Interestingly, there is another problem: if you use the same data for training and validation, there will be a discrepancy between training metrics (e.g. loss
) and validation metrics (e.g. val_loss
) at the end of each epoch.
(A similar problem)
Used Code:
############################ import section ############################
from __future__ import print_function # perform like in python 3.x
from keras.datasets import mnist
from keras.utils import np_utils # numpy utils for to_categorical()
from keras.models import Model, load_model
from keras.layers import Dense, GlobalAveragePooling2D, Dropout, GaussianDropout, Conv2D, MaxPooling2D
from keras.optimizers import SGD, Adam
from keras import backend as K
from keras.preprocessing.image import ImageDataGenerator
from keras import metrics
import os
import sys
from scipy import misc
import numpy as np
from keras.applications.vgg16 import preprocess_input as vgg16_preprocess_input
from keras.applications import VGG16
from keras.callbacks import CSVLogger, ModelCheckpoint
############################ manual settings ###########################
# general settings
seed = 1337
loss_function = 'categorical_crossentropy'
learning_rate = 0.001
epochs = 10
batch_size = 20
nb_classes = 5
img_width, img_height = 400, 400 # >= 48 necessary, as VGG16 is used
chosen_optimizer = SGD(lr=learning_rate, momentum=0.0, decay=0.0, nesterov=False)
steps_per_epoch = 40 // batch_size # 40 train samples in 5 classes
validation_steps = 40 // batch_size # 40 train samples in 5 classes
data_dir = # TODO: set path where data is stored (folders: 'train', 'val', 'test'; within each folder are folders named by classes)
# callbacks: CSVLogger & ModelCheckpoint
filepath = # TODO: set path, where you want to store files generated by the callbacks
file_best_checkpoint= 'best_epoch.hdf5'
file_csvlogger = 'logged_metrics.txt'
modelcheckpoint_best_epoch= ModelCheckpoint(filepath=os.path.join(filepath, file_best_checkpoint),
monitor = 'val_loss' , verbose = 1,
save_best_only = True,
save_weights_only=False, mode='auto',
period=1) # every epoch executed
csvlogger = CSVLogger(os.path.join(filepath, file_csvlogger) , separator=',', append=False)
############################ prepare data ##############################
# get validation data (for evaluation)
X_val, Y_val = # TODO: load train data (4darray, samples, img_width, img_height, nb_channels) IMPORTANT: 5 classes with 8 images each.
# preprocess data
my_preprocessing_function = mf.my_vgg16_preprocess_input
# 'augmentation' configuration we will use for training
train_datagen = ImageDataGenerator(preprocessing_function = my_preprocessing_function) # only preprocessing; static data set
# 'augmentation' configuration we will use for validation
val_datagen = ImageDataGenerator(preprocessing_function = my_preprocessing_function) # only preprocessing; static data set
train_data_dir = os.path.join(data_dir, 'train')
validation_data_dir = os.path.join(data_dir, 'val')
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
shuffle = True,
seed = seed, # random seed for shuffling and transformations
class_mode='categorical') # label type (categorical = one-hot vector)
validation_generator = val_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
shuffle = True,
seed = seed, # random seed for shuffling and transformations
class_mode='categorical') # label type (categorical = one-hot vector)
############################## training ###############################
print("
---------------------------------------------------------------")
print("------------------------ training model -----------------------")
print("---------------------------------------------------------------")
# create the base pre-trained model
base_model = VGG16(include_top=False, weights = None, input_shape=(img_width, img_height, 3), pooling = 'max', classes = nb_classes)
model_name = "VGG_modified"
# do not freeze any layers --> all layers trainable
for layer in base_model.layers:
layer.trainable = True
# define topping of base_model
x = base_model.output # get the last layer of our base_model
x = Dense(1024, activation='relu', name='fc1')(x)
x = Dense(1024, activation='relu', name='fc2')(x)
predictions = Dense(nb_classes, activation='softmax', name='predictions')(x)
# finally, stack model together
model = Model(outputs=predictions, name= model_name, inputs=base_model.input) #Keras 1.x.x: model = Model(input=base_model.input, output=predictions)
print(model.summary())
# compile the model (should be done *after* setting layers to non-trainable)
model.compile(optimizer = chosen_optimizer, loss=loss_function,
metrics=['categorical_accuracy','kullback_leibler_divergence'])
# train the model on your data
model.fit_generator(
train_generator,
steps_per_epoch=steps_per_epoch,
epochs=epochs,
validation_data=validation_generator,
validation_steps=validation_steps,
callbacks = [csvlogger, modelcheckpoint_best_epoch])
############################## evaluation ##############################
print("
---------------------------------------------------------------")
print("------------------ Evaluation of Best Epoch -------------------")
print("---------------------------------------------------------------")
# load model (corresponding to best training epoch)
model = load_model(os.path.join(filepath, file_best_checkpoint))
# evaluate model on validation data (in test mode!)
list_of_metrics = model.evaluate(X_val, Y_val, batch_size=batch_size, verbose=1, sample_weight=None)
index = 0
print('
Metrics:')
for metric in model.metrics_names:
print(metric+ ':' , str(list_of_metrics[index]))
index += 1
E D I T 2
Referring to 1. of E D I T:
If I use the same generator for validation data during training and evaluation (by using evaluate_generator()
), the problem still occurs.
Hence, it is definitely a problem caused by the generators...
See Question&Answers more detail:
os