Keras: ValueError: logits and labels must have the same shape ((None, 2) vs (None, 1))

Question

I have been using the famous dogs-vs-cats kaggle dataset and trying to come up with my own CNN Model. I'm new to using the image_dataset_from_directory method to import the dataset after configuring it into two folders that contain the cat and dog images separately.

Here is the code for the model.

from keras.models import Sequential
from keras.layers import Conv2D,MaxPooling2D,\
     Dropout,Flatten,Dense,Activation,\
     BatchNormalization
model=Sequential()
model.add(Conv2D(32,(3,3),activation='relu',input_shape=(128,128,3)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Conv2D(64,(3,3),activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Conv2D(128,(3,3),activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512,activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(2,activation='sigmoid'))
model.compile(loss = 'binary_crossentropy',
  optimizer='adam',metrics=['accuracy'])

And here's the code for the dataset:

Dataset = tf.keras.preprocessing.image_dataset_from_directory(
    TRAIN_DIR,
    labels="inferred",
    label_mode="binary",
    class_names=None,
    color_mode="rgb",
    batch_size=32,
    image_size=(128, 128),
    shuffle=True,
    seed=None,
    validation_split=None,
    subset=None,
    interpolation="bilinear",
    follow_links=False,
)

After running the fit function to train my CNN. I was shown this error:

ValueError: in user code:

    C:\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\engine\training.py:806 train_function  *
        return step_function(self, iterator)
    C:\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\engine\training.py:796 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    C:\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1211 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    C:\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2585 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    C:\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2945 _call_for_each_replica
        return fn(*args, **kwargs)
    C:\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\engine\training.py:789 run_step  **
        outputs = model.train_step(data)
    C:\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\engine\training.py:748 train_step
        loss = self.compiled_loss(
    C:\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\engine\compile_utils.py:204 __call__
        loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    C:\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\losses.py:149 __call__
        losses = ag_call(y_true, y_pred)
    C:\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\losses.py:253 call  **
        return ag_fn(y_true, y_pred, **self._fn_kwargs)
    C:\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
        return target(*args, **kwargs)
    C:\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\losses.py:1605 binary_crossentropy
        K.binary_crossentropy(y_true, y_pred, from_logits=from_logits), axis=-1)
    C:\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
        return target(*args, **kwargs)
    C:\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\keras\backend.py:4823 binary_crossentropy
        return nn.sigmoid_cross_entropy_with_logits(labels=target, logits=output)
    C:\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
        return target(*args, **kwargs)
    C:\anaconda3\envs\tensorflow\lib\site-packages\tensorflow\python\ops\nn_impl.py:173 sigmoid_cross_entropy_with_logits
        raise ValueError("logits and labels must have the same shape (%s vs %s)" %

    ValueError: logits and labels must have the same shape ((None, 2) vs (None, 1))

How can I fix this?

Answer 1

I finally found the solution to my question! I managed to train the model by setting the loss function to sparse_categorical_crossentropy instead of binary_crossentropy. Then I also changed the activation function of the last layer to softmax that was sigmoid when I asked the question.

Here's the whole code if you're interested:

from keras.models import Sequential
from keras.layers import Conv2D,MaxPooling2D,\
     Dropout,Flatten,Dense,Activation,\
     BatchNormalization
model=Sequential()
model.add(Conv2D(32,(3,3),activation='relu',input_shape=(128,128,3)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Conv2D(64,(3,3),activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Conv2D(128,(3,3),activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512,activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(2,activation='softmax'))
model.compile(loss = 'sparse_categorical_crossentropy',
  optimizer='adam',metrics=['accuracy'])