Python Keras Model -- ValueError: Layer sequential expects 1 input(s), but it received 16 input tensors

Question

I have seen that many others on stackoverflow have posted about this same problem, but I haven't been able to figure out how to apply those solutions to my example.

I have been working on creating a model to predict an outcome of either 0 or 1 based on a dataset which contains 16 features - Everything has seemed to work fine (accuracy evaluation, epoch completion, etc.).

As mentioned, my training features include 16 different variables, but when I pass in a list that contains 16 unique values separate from the training dataset in order to try and make an individual prediction (of either 0 or 1), I get this error:

ValueError: Layer sequential_11 expects 1 input(s), but it received 16 input tensors.

Here is my code -

y = datas.Result
X = datas.drop(columns = ['Date', 'home_team', 'away_team', 'home_pitcher', 'away_pitcher', 'Result'])

X = X.values.astype('float32')
y = y.values.astype('float32')

X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.2)
X_train, X_validation, y_train, y_validation = train_test_split(X, y, test_size = 0.2)

model=keras.Sequential([
           keras.layers.Dense(32, input_shape = (16,)),
           keras.layers.Dense(20,activation=tf.nn.relu),                           
           keras.layers.Dense(2,activation='softmax')
        ])

model.compile(optimizer='adam',
                loss='sparse_categorical_crossentropy',
                metrics=['acc'])

history = model.fit(X_train,y_train,epochs=20, validation_data=(X_validation, y_validation))

#all variables within features list are single values, ex: .351, 11, .991, etc.
features = [t1_pqm,t2_pqm,t1_elo,t2_elo,t1_era,t2_era,t1_bb9,t2_bb9,t1_fip,t2_fip,t1_ba,t2_ba,t1_ops,t2_ops,t1_so,t2_so]
prediction = model.predict(features)

Answer 1

The model expects an input of shape (None,16) but features has the shape (16,) (1D list). The easiest solution is to make it an numpy array with the right shape (1, 16) :

features = np.array([[t1_pqm,t2_pqm,t1_elo,t2_elo,t1_era,t2_era,t1_bb9,t2_bb9,t1_fip,t2_fip,t1_ba,t2_ba,t1_ops,t2_ops,t1_so,t2_so]])