Zero validation loss and validation accuracy at classification problem - keras

Question

I'm running a multiclass classification problem using the below resnet model:
resnet = tf.keras.applications.ResNet50(
include_top=False ,
weights='imagenet' ,
input_shape=(96, 96, 3) ,
pooling="avg"
)
for layer in resnet.layers:
layer.trainable = True
model_resnet = tf.keras.Sequential()
model_resnet.add(resnet)
model_resnet.add(tf.keras.layers.Flatten())
model_resnet.add(tf.keras.layers.Dense(8, activation='softmax',name='output') )
model_resnet.compile( loss="sparse_categorical_crossentropy" , optimizer=tf.keras.optimizers.Adam(learning_rate=0.001) ,metrics=['accuracy'])
I also used a train and a test generator as below:
train_generator=img_gen.flow_from_dataframe(dataframe=train_dataset,x_col="file_loc",y_col='expr',target_size=(96, 96),batch_size=91,class_mode="raw")
test_generator=img_gen.flow_from_dataframe(dataframe=test_dataset,x_col="file_loc",target_size=(96, 96),batch_size=93,y_col=None,shuffle=False,class_mode=None)
when I am running the code below I get the wanted results and everything works fine
model_resnet.fit_generator(train_generator,
steps_per_epoch=STEP_SIZE_TRAIN_resnet,
epochs=20
)
I wanted to compute the validation accuracy of every epoch so I wrote something like this
model_path = f"/content/weights" + "{val_accuracy:.4f}.hdf5"
checkpoint = tf.keras.callbacks.ModelCheckpoint(
model_path,
monitor='val_accuracy',
save_best_only=True,
mode='max',
verbose=1
)
history = model_resnet.fit_generator(
train_generator,
epochs=5,
steps_per_epoch=STEP_SIZE_TRAIN_resnet,
validation_data=test_generator,
validation_steps=STEP_SIZE_TEST_resnet,
max_queue_size=1,
shuffle=True,
callbacks=[checkpoint],
verbose=1
)
The problem is that for every epoch the validation loss and validation accuracy remain zero even though the training loss and accuracy change. I ran this code for over 20 epochs and it doesn't change at all. I can't find what am I doing wrong since without this it works perfectly,does anyone have any idea?
Epoch 1: val_accuracy improved from -inf to 0.00000, saving model to /content/weights0.0000.hdf5
500/500 [==============================] - 30s 60ms/step - loss: 1.0213 - accuracy: 0.6546 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00
Epoch 2/5
500/500 [==============================] - ETA: 0s - loss: 0.9644 - accuracy: 0.6672
Epoch 2: val_accuracy did not improve from 0.00000
500/500 [==============================] - 29s 58ms/step - loss: 0.9644 - accuracy: 0.6672 - val_loss: 0.0000e+00 - val_accuracy: 0.0000e+00
Edit: I didn't specify the test labels of the test dataset because I used to compute the accuracy score as below:
y_pred = model_resnet.predict(test_generator)
y_pred_max = np.argmax(y_pred, axis=1)
y_true = test_dataset["expr"].to_numpy()
print("accuracy",accuracy_score(y_true, y_pred_max))
I changed the test_generator as below:
test_generator=img_gen.flow_from_dataframe(dataframe=test_dataset,x_col="file_loc",target_size=(96, 96),batch_size=93,y_col='expr',shuffle=False,class_mode=None)
but nothing has changed, it still results in zero

Answer 1

As #Dr.Snoopy said, the problems were that I didn't specify the test labels in these generator (which are required to compute accuracy) and I had different class modes in the generator,the correct was "raw" in both.