ValueError: `logits` and `labels` must have the same shape, received ((None, 10) vs (None, 1)) - tensorflow

Question

I am running an Involution Model (based of this example), and I am constantly running into errors during the training stage. This is my error:
ValueError: `logits` and `labels` must have the same shape, received ((None, 10) vs (None, 1)).
Below is the relevant code for dataset loading:
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
train_ds = train_datagen.flow_from_directory(
'data/train',
target_size=(150, 150),
batch_size=128,
class_mode='binary')
test_ds = test_datagen.flow_from_directory(
'data/test',
target_size=(150, 150),
batch_size=64,
class_mode='binary')`
And this is the code for training:
print("building the involution model...")
inputs = keras.Input(shape=(224, 224, 3))
x, _ = Involution(channel=3, group_number=1, kernel_size=3, stride=1, reduction_ratio=2, name="inv_1")(inputs)
x = keras.layers.ReLU()(x)
x = keras.layers.MaxPooling2D((2, 2))(x)
x, _ = Involution(
channel=3, group_number=1, kernel_size=3, stride=1, reduction_ratio=2, name="inv_2")(x)
x = keras.layers.ReLU()(x)
x = keras.layers.MaxPooling2D((2, 2))(x)
x, _ = Involution(
channel=3, group_number=1, kernel_size=3, stride=1, reduction_ratio=2, name="inv_3")(x)
x = keras.layers.ReLU()(x)
x = keras.layers.Flatten()(x)
x = keras.layers.Dense(64, activation="relu")(x)
outputs = keras.layers.Dense(10)(x)
inv_model = keras.Model(inputs=[inputs], outputs=[outputs], name="inv_model")
print("compiling the involution model...")
inv_model.compile(
optimizer="adam",
loss=keras.losses.BinaryCrossentropy(from_logits=True),
metrics=["accuracy"],
)
print("inv model training...")
inv_hist = inv_model.fit(train_ds, epochs=20, validation_data=test_ds)`
The model itself the same used by Keras, and I have not changed anything except to use my own dataset instead of the CIFAR dataset (model works for me with this dataset). So I am sure there is an error in my data loading, but I am unable to identify what that is.
Model Summary:
Model: "inv_model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_14 (InputLayer) [(None, 224, 224, 3)] 0
inv_1 (Involution) ((None, 224, 224, 3), 26
(None, 224, 224, 9, 1,
1))
re_lu_39 (ReLU) (None, 224, 224, 3) 0
max_pooling2d_26 (MaxPoolin (None, 112, 112, 3) 0
g2D)
inv_2 (Involution) ((None, 112, 112, 3), 26
(None, 112, 112, 9, 1,
1))
re_lu_40 (ReLU) (None, 112, 112, 3) 0
max_pooling2d_27 (MaxPoolin (None, 56, 56, 3) 0
g2D)
inv_3 (Involution) ((None, 56, 56, 3), 26
(None, 56, 56, 9, 1, 1)
)
re_lu_41 (ReLU) (None, 56, 56, 3) 0
flatten_15 (Flatten) (None, 9408) 0
dense_26 (Dense) (None, 64) 602176
dense_27 (Dense) (None, 10) 650
=================================================================

Answer 1

When you called the train_datagen.flow_from_directory() function, you used class_mode='binary' which means you will have the labels of your images as 0 and 1 only, whereas you are have total 10 predictions i.e. 10 neurons in your final output layer. Hence the labels and logits dosen't match.
Solution: Use class_mode='categorical' which means that there will be as many labels as the number of classes. Do the same in test_datagen as well.