InvalidArgumentError: logits and labels must have the same first dimension, got logits shape [15488,3] and labels shape [32]

Question

My code is not working I am getting InvalidArgumentError: logits and labels must have the same first dimension, got logits shape [15488,3] and labels shape [32] [[node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits (defined at /usr/local/lib/python3.7/dist-packages/keras/backend.py:5114) ]] [Op:__inference_train_function_2889]

AUTOTUNE = tf.data.AUTOTUNE

    train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
    val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)

    data_augmentation = tf.keras.Sequential([
      tf.keras.layers.experimental.preprocessing.RandomFlip('horizontal'),
      tf.keras.layers.experimental.preprocessing.RandomRotation(0.2),
    ])

    for image, _ in train_ds.take(1):
      plt.figure(figsize=(10, 10))
      first_image = image[0]
      for i in range(9):
        ax = plt.subplot(3, 3, i + 1)
        augmented_image = data_augmentation(tf.expand_dims(first_image, 0))
        plt.imshow(augmented_image[0] / 255)
        plt.axis('off')

    normalization_layer = layers.experimental.preprocessing.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]
    # Notice the pixels values are now in `[0,1]`.
    print(np.min(first_image), np.max(first_image))

    num_classes = 3

    model = Sequential([
      layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
      layers.MaxPooling2D(),
      layers.Conv2D(32, 3, padding='same', activation='relu'),
      layers.MaxPooling2D(),
      layers.Conv2D(64, 3, padding='same', activation='relu'),
      layers.MaxPooling2D(),
      layers.Dense(128, activation='relu'),
      layers.Dense(num_classes)
    ])

    model.compile(optimizer='adam',
                  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                  metrics=['accuracy'])

    model.summary()

    epochs= 10
    history = model.fit(
      train_ds,
      validation_data=val_ds,
      epochs=epochs
    )

    acc = history.history['accuracy']
    val_acc = history.history['val_accuracy']

    loss = history.history['loss']
    val_loss = history.history['val_loss']

    epochs_range = range(epochs)

    plt.figure(figsize=(8, 8))
    plt.subplot(1, 2, 1)
    plt.plot(epochs_range, acc, label='Training Accuracy')
    plt.plot(epochs_range, val_acc, label='Validation Accuracy')
    plt.legend(loc='lower right')
    plt.title('Training and Validation Accuracy')

    plt.subplot(1, 2, 2)
    plt.plot(epochs_range, loss, label='Training Loss')
    plt.plot(epochs_range, val_loss, label='Validation Loss')
    plt.legend(loc='upper right')
    plt.title('Training and Validation Loss')
    plt.show()

Answer 1

After your code

layers.MaxPooling2D()

you need to flatten the data before you apply it to the dense layer so add

layers.Flatten()