Keras model.evaluate accuracy stuck at 50 percent while using ImageDataGenerator

Question

I am trying to find the accuracy of my saved Keras model using model.evaluate .

I have loaded in my model using this:

model = keras.models.load_model("../input/modelpred/2_convPerSection_4_sections")

I have a CSV file with two columns, one for the filename of an image and one for the label. Here is a sample:

id,label
95d04f434d05c1565abdd1cbf250499920ae8ecf.tif,0
169d0a4a1dbd477f9c1a00cd090eff28ac9ef2c1.tif,0
51cb2710ab9a05569bbdedd838293c37748772db.tif,1
4bbb675f8fde60e7f23b3354ee8df223d952c83c.tif,1
667a242a7a02095f25e0833d83062e8d14a897cd.tif,0

I have loaded this CSV into a pandas dataframe and fed it into an ImageDataGenerator :

df = pd.read_csv("../input/cancercsv/df_test.csv", dtype=object)

test_path = "../input/histopathologic-cancer-detection/train"

test_data_generator = ImageDataGenerator(rescale=1./255).flow_from_dataframe(dataframe = df,
                                                                                  directory=test_path,
                                                                                  x_col = "id",
                                                                                  y_col = "label",
                                                                                  target_size=(96,96),
                                                                                  batch_size=16,
                                                                                  shuffle=False)

Now I try to evaluate my model using:

val = model.evaluate(test_data_generator, verbose = 1)
print(val)

However, the accuracy doesn't change from 50 percent, but, my model had a 90 percent validation accuracy when trained.

Here is what is returned:

163/625 [======>.......................] - ETA: 21s - loss: 1.1644 - accuracy: 0.5000

I was able to ensure that my model worked and the generator was properly feeding data, by creating an ROC curve using matplotlib and scikit-learn, which produced a 90 percent AUC, so I'm not sure where the problem is:

predictions = model.predict_generator(test_data_generator, steps=len(test_data_generator), verbose = 1)
false_positive_rate, true_positive_rate, threshold = roc_curve(test_data_generator.classes, np.round(predictions))
area_under_curve = auc(false_positive_rate, true_positive_rate)

plt.plot([0, 1], [0, 1], 'k--')
plt.plot(false_positive_rate, true_positive_rate, label='AUC = {:.3f}'.format(area_under_curve))
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.title('ROC curve')
plt.legend(loc='best')
plt.show()

Similar questions say that the problem came from setting shuffle parameter in the ImageDataGenerator to True , but mine has always been set to False . Another similar problem was fixed by retraining with a sigmoid activation rather than softmax, but I used sigmoid in my final layer, so that can't be the problem

This is my first time using Keras. What did I do wrong?

Answer 1

The problem was because of class_mode parameter in flow function. Default is categorical .

Setting it as binary solved the problem. Corrected code:

test_data_generator = ImageDataGenerator(rescale=1./255).flow_from_dataframe(dataframe = df,
                                                                                  directory=test_path,
                                                                                  x_col = "id",
                                                                                  y_col = "label",
                                                                                 class_mode = 'binary',
                                                                                  target_size=(96,96),
                                                                                  batch_size=16,
                                                                                  shuffle=False)