Error in keras sparse_categorical_crossentropy loss function

Question

I am trying a deep neural network prediction but getting error:

InvalidArgumentError: logits and labels must have the same first dimension, got logits shape [32,4] and labels shape [128]

Here are the features:

new_features.shape
(19973, 8)

new_features[0].shape
(8,)

Here are the label/output

output.shape
(19973, 4)

output[0].shape
(4,)

Here is the keras code

model = Sequential(
  [
    Dense(units=8, input_shape=new_features[0].shape, name="layer1"),
    Dense(units=1024, activation="relu", name="layer2"),
    Dense(units=1024, activation="relu", name="layer3"),
    Dense(units=4,  name="layer4", activation="softmax"),
  ]
)

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
model.fit(new_features, output, epochs=2)

The features and labels contain float values.

Answer 1

The problem is in your target shape. First of all your target in classification problems must be int

if you have 1D integer encoded target you can use sparse_categorical_crossentropy as loss function

X = np.random.randint(0,10, (1000,100))
y = np.random.randint(0,3, 1000)

model = Sequential([
    Dense(128, input_dim = 100),
    Dense(3, activation='softmax'),
])
model.summary()
model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
history = model.fit(X, y, epochs=3)

Otherwise, if you have one-hot encoded your target in order to have 2D shape (n_samples, n_class) you can use categorical_crossentropy

X = np.random.randint(0,10, (1000,100))
y = pd.get_dummies(np.random.randint(0,3, 1000)).values

model = Sequential([
    Dense(128, input_dim = 100),
    Dense(3, activation='softmax'),
])
model.summary()
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
history = model.fit(X, y, epochs=3)