Neural networks - why is loss always 0.0 and accuracy 1.0-CodePudding

I'm trying to train my neural network with 10 epochs. But my attempts are unsuccessful. I don't get it why am I always getting something like this:

 35/300 [==>...........................] - ETA: 1:09 - loss: 0.0000e 00 - accuracy: 1.0000
 36/300 [==>...........................] - ETA: 1:09 - loss: 0.0000e 00 - accuracy: 1.0000
 37/300 [==>...........................] - ETA: 1:08 - loss: 0.0000e 00 - accuracy: 1.0000

Here are my batch size and image width/height and whole feeding proccess:

batch_size = 32
img_height = 150
img_width = 150

    dataset_url = "http://cnrpark.it/dataset/CNR-EXT-Patches-150x150.zip"
    print(dataset_url)
    data_dir = tf.keras.utils.get_file(origin=dataset_url,
                                       fname='CNR-EXT-Patches-150x150',
                                       untar=True)
                
    train_ds = tf.keras.utils.image_dataset_from_directory(
      data_dir,
      validation_split=0.2,
      subset="training",
      seed=123,
      image_size=(img_height, img_width),
      batch_size=batch_size)
    num_classes = 1
    
    val_ds = tf.keras.utils.image_dataset_from_directory(
      data_dir,
      validation_split=0.2,
      subset="validation",
      seed=123,
      image_size=(img_height, img_width),
      batch_size=batch_size)
    
    
    class_names = train_ds.class_names
    print(class_names)
    
    
    for image_batch, labels_batch in train_ds:
      print(image_batch.shape)
      print(labels_batch.shape)
      break
    
    normalization_layer = tf.keras.layers.Rescaling(1./255)
    
    normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
    image_batch, labels_batch = next(iter(normalized_ds))
    first_image = image_batch[0]
    print(np.min(first_image), np.max(first_image))
    
    model = tf.keras.Sequential([
      tf.keras.layers.Rescaling(1./255),
      tf.keras.layers.Conv2D(32, 3, activation='relu'),
      tf.keras.layers.MaxPooling2D(),
      tf.keras.layers.Conv2D(32, 3, activation='relu'),
      tf.keras.layers.MaxPooling2D(),
      tf.keras.layers.Conv2D(32, 3, activation='relu'),
      tf.keras.layers.MaxPooling2D(),
      tf.keras.layers.Flatten(),
      tf.keras.layers.Dense(128, activation='relu'),
      tf.keras.layers.Dense(num_classes)
    ])
    
    AUTOTUNE = tf.data.AUTOTUNE
    
    train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)
    val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
    
    model.compile(
      optimizer='adam',
      loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
      metrics=['accuracy'])
    
    model.fit(
      train_ds,
      validation_data=val_ds,
      epochs=10
    )

From np.min and np.max I'm getting these values: 0.08627451 0.5568628 so this obviously wouldn't be the case. What should be wrong in my attempt?

CodePudding user response：

You have set num_classes = 1, although your dataset has two classes:

LABEL is 0 for free, 1 for busy.

So, if you want to use tf.keras.losses.SparseCategoricalCrossentropy, try:

tf.keras.layers.Dense(2)

You could also consider using binary_crossentropy if you only have two classes. You would have to change your loss function and output layer to:

tf.keras.layers.Dense(1, activation="sigmoid")

CodePudding user response：

You need the following:

num_classes = 2
tf.keras.layers.Dense(num_classes, activation="softmax)

I can see a normalization_layer, looks like you are rescaling twice.