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Incompatibility between input and final Dense Layer (Value Error)

I’m following this tutorial from Nabeel Ahmed to create your own emotion detector using Keras (I’m a noob) and I’ve found a strange behaviour that I’d like to understand. The input data is a bunch of 48×48 images, each one with an integer value between 0 and 6 (each number stands for an emotion label), which represents the emotion present in the image.

train_X.shape -> (28709, 2304) // training-data, 28709 images of 48x48
train_Y.shape -> (28709,) //The emotion present in each image as an integer, 1 = happiness, 2 = sadness, etc.
val_X.shape -> (3589, 2304)
val_Y.shape -> (3589, )

In order to feed the data into the model, train_X and val_X are reshaped (as the tutorial explains)

train_X.shape -> (28709, 48, 48, 1)
val_X.shape -> (3589, 48, 48, 1)

The model, as it is in the tutorial, is this one:

model = Sequential()
input_shape = (48,48,1)
#1st convolution layer
model.add(Conv2D(64, (5, 5), input_shape=input_shape,activation='relu', padding='same'))
model.add(Conv2D(64, (5, 5), activation='relu', padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))

#2nd convolution layer
model.add(Conv2D(128, (5, 5),activation='relu',padding='same'))
model.add(Conv2D(128, (5, 5),activation='relu',padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))

#3rd convolution layer
model.add(Conv2D(256, (3, 3),activation='relu',padding='same'))
model.add(Conv2D(256, (3, 3),activation='relu',padding='same'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))

model.add(Flatten())
model.add(Dense(128))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(0.2))
################################################################
model.add(Dense(7)) # <- problematic line
################################################################
model.add(Activation('softmax'))

my_optimiser = tf.keras.optimizers.Adam(
learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, amsgrad=False,
name='Adam')

model.compile(loss='categorical_crossentropy', metrics=['accuracy'],optimizer=my_optimiser)

However, when I try to use it, using the tutorial snippet, I get an error in the line of the validation_data like this

history = model.fit(train_X,     
        train_Y, 
        batch_size=64, 
        epochs=80, 
        verbose=1, 
        validation_data=(val_X, val_Y),
        shuffle=True)

ValueError: Shapes (None, 1) and (None, 7) are incompatible

After reviewing the code and the documentation about the fit method, my only idea was to change the 7 in the last Dense layer of the model to 1, which mysteriously works. I’d like to know what is happening here if anyone could give me a hint.

Answer

You seem to be working with sparse integer labels, where each sample belongs to one of seven classes {0, 1, 2, 3, 4, 5, 6}, so I would recommend using SparseCategoricalCrossentropy instead of CategoricalCrossentropy as your loss function. Just change this parameter and your model should work fine. If you want to use CategoricalCrossentropy, you will have to one-hot encode your labels, for example with:

train_Y = tf.keras.utils.to_categorical(train_Y, num_classes=7)