ValueError：形状（无，1）和（无，101）不兼容

Question

The output dense layer is 101 but this error pops up. The error could be with respect to train_dataset and test_dataset or model compiling. Help me out with this !!

Also if I want to sample the dataset and then assign the sample to training and testing. How do I it? I'm new to Tensorflow and python and the syntax is confusing.

    BATCH_SIZE = 32
    IMG_SIZE = (224, 224)
    directory = "/Food/Food 101/images"

    train_dataset = image_dataset_from_directory(directory,
                                                 shuffle=True,
                                                 batch_size=BATCH_SIZE,
                                                 label_mode = "int",
                                                 image_size=IMG_SIZE,
                                                 validation_split=0.2,
                                                 subset='training',
                                                 seed=42)
    validation_dataset = image_dataset_from_directory(directory,
                                                 shuffle=True,
                                                 batch_size=BATCH_SIZE,
                                                 label_mode = "int",
                                                 image_size=IMG_SIZE,
                                                 validation_split=0.2,
                                                 subset='validation',
                                                 seed=42)

class_names = train_dataset.class_names

plt.figure(figsize=(10, 10))
for images, labels in train_dataset.take(1):
    for i in range(9):
        ax = plt.subplot(3, 3, i + 1)
        plt.imshow(images[i].numpy().astype("uint8"))
        plt.title(class_names[labels[i]])
        plt.axis("off")
        
        
AUTOTUNE = tf.data.experimental.AUTOTUNE
train_dataset = train_dataset.prefetch(buffer_size=AUTOTUNE)


def data_augmenter():
  
    data_augmentation = tf.keras.Sequential()
    data_augmentation.add(tf.keras.layers.experimental.preprocessing.RandomFlip("horizontal"))
    data_augmentation.add(tf.keras.layers.experimental.preprocessing.RandomRotation(0.2))

    return data_augmentation


data_augmentation = data_augmenter()

for image, _ in train_dataset.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')
        
preprocess_input = tf.keras.applications.mobilenet_v2.preprocess_input

IMG_SHAPE = IMG_SIZE + (3,)
IMG_SHAPE
base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,
                                               include_top=True,
                                               weights='imagenet')


base_model.summary()

nb_layers = len(base_model.layers)
print(base_model.layers[nb_layers - 2].name)
print(base_model.layers[nb_layers - 1].name)

image_batch, label_batch = next(iter(train_dataset))
feature_batch = base_model(image_batch)
print(feature_batch.shape)

label_batch



base_model.trainable = False
image_var = tf.Variable(image_batch)
pred = base_model(image_var)

tf.keras.applications.mobilenet_v2.decode_predictions(pred.numpy(), top=2)

image_shape=IMG_SIZE
def FC_model(image_shape=IMG_SIZE, data_augmentation=data_augmenter()):
    
    input_shape = image_shape + (3,)

    
    base_model = tf.keras.applications.MobileNetV2(input_shape=input_shape,
                                                   include_top=False, 
                                                   weights='imagenet') 

    base_model.trainable = False
    inputs = tf.keras.Input(shape = image_shape + (3, )) 
    x = data_augmentation(inputs)
    x = preprocess_input(x) 
    x = base_model(x, training=False) 
    x = tf.keras.layers.GlobalAveragePooling2D()(x)
    x = tf.keras.layers.Dropout(0.2)(x)
    prediction_layer = tf.keras.layers.Dense(101)
    outputs = prediction_layer(x)
    model = tf.keras.Model(inputs, outputs)
    
    return model


model2 = FC_model(IMG_SIZE, data_augmentation)
model2.summary()


base_learning_rate = 0.001
model2.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate),
              loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = True),
              metrics=['accuracy'])

initial_epochs = 10
history = model2.fit(train_dataset, validation_data=validation_dataset, epochs=initial_epochs)

Answer 1

The problem is in how you are loading the data:

train_dataset = image_dataset_from_directory(directory,
                                             shuffle=True,
                                             batch_size=BATCH_SIZE,
                                             label_mode = "int",
                                             image_size=IMG_SIZE,
                                             validation_split=0.2,
                                             subset='training',
                                             seed=42)

In particular label_mode="int" means that your target variable is encoded as an integer (ie, 1 if cat, 2 if dog, 3 if tree). You want to change it to label_mode="categorical" .