KERAS low fit loss and high loss evaluation

Question

I'm new to keras. This code is working on classifying between MRI images of brain with or without tumor. When I run model.evaluate() to see the accuracy I get very high loss value even it is low when I'm training the model(normal less than 1) and I get the following error:

WARNING:tensorflow:6 out of the last 11 calls to <function Model.make_test_function.<locals>.test_function at 0x00000221AC143AF0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.

The most of the code is copied from this link .

Here is the full code:

import numpy as np
import matplotlib.pyplot as plt
import os
import cv2

import tensorflow as tf
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D

def load_data( DATADIR, IMG_SIZE, CATEGORIES ):
    data = []
    for category in CATEGORIES:  # do dogs and cats
        
        path = os.path.join(DATADIR,category)  # create path to dogs and cats
        class_num = CATEGORIES.index(category)  # get the classification  (0 or a 1). 0=dog 1=cat

        for img in os.listdir(path):  # iterate over each image per dogs and cats
            try:
                img_array = cv2.imread(os.path.join(path,img) ,cv2.IMREAD_GRAYSCALE)  # convert to array
                
                img_array = cv2.medianBlur(img_array,5)
                
                img_array = cv2.adaptiveThreshold(img_array,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
                
                new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))  # resize to normalize data size
                
                data.append([new_array, class_num])  # add this to our training_data
            except Exception as e:  # in the interest in keeping the output clean...
                pass
            #except OSError as e:
            #    print("OSErrroBad img most likely", e, os.path.join(path,img))
            #except Exception as e:
            #    print("general exception", e, os.path.join(path,img))
    return data

TRAIN_DATADIR = "F:\Train"
TEST_DATADIR = "F:\Test"

CATEGORIES = ["no", "yes"]
IMG_SIZE = 128
training_data = load_data(TRAIN_DATADIR, IMG_SIZE, CATEGORIES)
testing_data = load_data(TEST_DATADIR, IMG_SIZE, CATEGORIES)

print(len(training_data))

import random
random.shuffle(training_data)
random.shuffle(testing_data)

X_train = []
y_train = []

for features,label in training_data:
    X_train.append(features)
    y_train.append(label)

#print(X[0].reshape(-1, IMG_SIZE, IMG_SIZE, 1))

X_train = np.asarray(X_train)
y_train = np.asarray(y_train)

X_train = np.array(X_train).reshape(-1, IMG_SIZE, IMG_SIZE, 1)


X_test = []
y_test = []

for features,label in testing_data:
    X_test.append(features)
    y_test.append(label)

    
X_test = np.asarray(X_test)
y_test = np.asarray(y_test)
#print(X[0].reshape(-1, IMG_SIZE, IMG_SIZE, 1))

X_test = np.array(X_test).reshape(-1, IMG_SIZE, IMG_SIZE, 1)

X_train = X_train/255.0


model = Sequential()

model.add(Conv2D(32, (3, 3), input_shape = X_train.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

model.fit(X_train, y_train, batch_size=10, epochs=15)

score = model.evaluate(X_test, y_test,verbose=1)

Answer 1

Ignore the warning.

Your low training loss and high evaluation loss means that your model is overfitted. Stop training when your validation accuracy starts to increase.