Why are the indicators calculated by model.evaluate () different from the tracked indicators during training at Keras?

Question

I am using Keras 2.0.4 (TensorFlow backend) for the task of classifying images (based on pre-processed models). In the training / setting time, I keep track of all the used metrics (eg categorical_accuracy, categorical crossentropy) using CSVLogger- including the relevant indicators relating to the inspection set (ie val_categorical_accuracy, val_categorical_crossentropy).

With a callback, ModelCheckpointI track the best weight configuration ( save_best_only=True). To evaluate a model in a validation set, use model.evaluate().

My expectation: the tracked metrics for the CSVLogger("best" era) are equal to the metrics calculated for model.evaluate(). Unfortunately, this is not the case. Indicators differ by + - 5% . Is there a reason for this behavior?

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ED i T:

After some testing, I could get some ideas:

• If I do not use the generator for training and data verification (and therefore not model.fit_generator()), the problem does not arise. → Use ImageDataGeneratorfor training and validation data is a source of inconsistency. (Note: evaluateI don’t use the generator for the calculation , but I do use the same validation data (at least if DataImageGeneratorit works as expected ...).
  I think ImageDataGenerator is not working properly (please also see this ).
• , . CSVLogger ( "" ) , model.evaluate().
  , : , (, loss) (, val_loss).
  ( )

:

############################ 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("\n---------------------------------------------------------------")
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("\n\n---------------------------------------------------------------")
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('\nMetrics:')
for metric in model.metrics_names:
    print(metric+ ':' , str(list_of_metrics[index]))
    index += 1

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E D T 2
1. of E D T: ( evaluate_generator()), . , , ...

Answer 1

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