Binary classification with additional dimension (subclasses)

Question

Suppose I have an array of 10 observations (belong to class A or B), 5 columns and 2 subclasses (C, D) as an additional dimension and I would like to make a binary classification (to A or B class) in Keras R. What should the network architecture look like in this case?

library("keras")

df = data.frame(class = c(rep("A", 10), rep("B", 10)),
                subclass = rep(c("C", "D"), 10),
                feature1 = rnorm(20),
                feature2 = rnorm(20),
                feature3 = rnorm(20))

df1 = df[df$subclass == "C", ]
df2 = df[df$subclass == "D", ]
df_list = list(df1, df2)

build_model = function() {
  model = keras_model_sequential() 

  model %>%
    # input_shape is 3 features and 2 subclasses
    layer_dense(units = 2, activation = 'sigmoid', input_shape = c(3, 2))

  model %>%
    compile(
      optimizer = "adam",
      loss = "binary_crossentropy",
      metrics = list("accuracy")
    )
}

# one hot encoding to A, B classes
labels = to_categorical(as.integer(df_list[[1]][, "class"]) - 1)

# drop factor columns
data = lapply(df_list, function(x) x[, -(1:2)])

# convert to array
data_array = array(unlist(c(data[[1]], data[[2]])), dim = c(10, 3, 2))

model = build_model()

# error appears in the following function:
history = model %>% fit(
  x = data_array,
  y = labels
)

Error:

Error in py_call_impl(callable, dots$args, dots$keywords):

ValueError: A target array with shape (10, 2) was passed for an output of shape (None, 3, 2) while using as loss binary_crossentropy. This loss expects targets to have the same shape as the output.

That error is related to the difference between dimensions of the input and output data, but I don't know what it should look like correctly. My sample data dimensions are 10 observations, 3 features and 2 subclasses.

Model info:

Model: "sequential"
____________________________________________________________________
Layer (type)                Output Shape               Param #    
====================================================================
dense (Dense)               (None, 3, 2)               6          
====================================================================
Total params: 6
Trainable params: 6
Non-trainable params: 0
____________________________________________________________________

Answer 1

Your error says that your model output shape is (None, 3, 2) but you give him output with shape (10,2) (labels). To fix this you should fix your input shape: Your input shpt is 3, not (3,2) because you have 3 features and you expect to have 2 outputs.

Change this line:

layer_dense(units = 2, activation = 'sigmoid', input_shape = c(3, 2))

to this:

layer_dense(units = 2, activation = 'sigmoid', input_shape = c(3))

I tested it with python, and it seems to work well:

import keras
model = keras.Sequential()
model.add(keras.layers.Dense(units = 2,activation="sigmoid",input_shape=(3,)))
model.summary()


Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_4 (Dense)              (None, 2)                 8         
=================================================================
Total params: 8
Trainable params: 8
Non-trainable params: 0
_________________________________________________________________