I am using tensorflow to do a multi-class classification
I load the training dataset and validation dataset in the following way
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="training",
shuffle=True,
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="validation",
shuffle=True,
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
Then when I train the model using model.fit()
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=epochs,
shuffle=True
)
I get validation accuracy around 95%.
But when I load the same validation set and use model.evaluate()
model.evaluate(val_ds)
I get very low accuracy (around 10%).
Why am I getting such different results? Am I using the model.evaluate function incorrectly?
Note : In the model.compile() I am specifying the following, Optimizer - Adam, Loss - SparseCategoricalCrossentropy, Metric - Accuracy
Model.evaluate() output
41/41 [==============================] - 5s 118ms/step - loss: 0.3037 - accuracy: 0.1032
Test Loss - 0.3036555051803589
Test Acc - 0.10315627604722977
Model.fit() output for last three epochs
Epoch 8/10
41/41 [==============================] - 3s 80ms/step - loss: 0.6094 - accuracy: 0.8861 - val_loss: 0.4489 - val_accuracy: 0.9483
Epoch 9/10
41/41 [==============================] - 3s 80ms/step - loss: 0.5377 - accuracy: 0.8953 - val_loss: 0.3868 - val_accuracy: 0.9554
Epoch 10/10
41/41 [==============================] - 3s 80ms/step - loss: 0.4663 - accuracy: 0.9092 - val_loss: 0.3404 - val_accuracy: 0.9590
