Set random seed

print(np.__version__) tf.random.set_seed(42)

1. Create a model using the Sequential Api

    model = tf.keras.Sequential([tf.keras.layers.Dense(1)])
   

2. Compile the model

    model.compile(loss=tf.keras.losses.mae,# mae is short for mean absolute error
    optimizer = tf.keras.optimizers.SGD(), # sgd is short for stochastic gradient descent             
    metrics=["mae"] 
   

3. Fit the model

    model.fit(X, y, epochs=5) // here the error
   

CodePudding user response：

You need to add an input layer, ex:

model = tf.keras.Sequential([  tf.keras.layers.InputLayer(input_shape=(1,)),  tf.keras.layers.Dense(1) ])