The problem is in your target shape and is related to the correct choice of an appropriate loss function. you have 2 possibilities:
1. possibility: if you have 1D integer encoded target, you can use sparse_categorical_crossentropy as loss function
n_class = 3
n_features = 100
n_sample = 1000
X = np.random.randint(0,10, (n_sample,n_features))
y = np.random.randint(0,n_class, n_sample)
inp = Input((n_features,))
x = Dense(128, activation='relu')(inp)
out = Dense(n_class, activation='softmax')(x)
model = Model(inp, out)
model.compile(loss="sparse_categorical_crossentropy",optimizer="adam",metrics=['accuracy'])
history = model.fit(X, y, epochs=3)
2. possibility: if you have one-hot encoded your target in order to have 2D shape (n_samples, n_class), you can use categorical_crossentropy
n_class = 3
n_features = 100
n_sample = 1000
X = np.random.randint(0,10, (n_sample,n_features))
y = pd.get_dummies(np.random.randint(0,n_class, n_sample)).values
inp = Input((n_features,))
x = Dense(128, activation='relu')(inp)
out = Dense(n_class, activation='softmax')(x)
model = Model(inp, out)
model.compile(loss="categorical_crossentropy",optimizer="adam",metrics=['accuracy'])
history = model.fit(X, y, epochs=3)