CODEWITHSUNDEEP
pythontensorflowclassmachine-learningdeep-learning
ml alltheway
ml alltheway

Reputation: 23

Why can I use model(x, training =True) when I define my own call function without the argument 'training'?

Notice when I created my model, I defined the call function with argument something = False, when I used the model in function train_step, I put in "something =True, training = True", training is not defined in my call, but it is in the default tf.keras.model call.

Why am I able to execute this with no error? And the output basically prints a bunch of 'my call's.

mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# Add a channels dimension
x_train = x_train[..., tf.newaxis].astype("float32")
x_test = x_test[..., tf.newaxis].astype("float32")

train_ds = tf.data.Dataset.from_tensor_slices(
    (x_train, y_train)).shuffle(10000).batch(32)

class MyModel(Model):
  def __init__(self):
    super(MyModel, self).__init__()
    self.fl = Flatten()
    self.d = Dense(10)
  
  ######My problem#######
  def call(self, x, something=False):
    if something:
      tf.print('my call')
    x = self.fl(x)
    return self.d(x)


model = MyModel()
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam()

@tf.function
def train_step(X,Y):
  with tf.GradientTape() as tape:
    ######My problem#######
    predictions = model(X, something =True, training = True)
    loss = loss_object(Y, predictions)
  gradients = tape.gradient(loss, model.trainable_variables)
  optimizer.apply_gradients(zip(gradients, model.trainable_variables))

for epoch in range(3):

  for X,Y in train_ds:
    train_step(X,Y)

Upvotes: 1

Views: 397

Answers (1)

elbe
elbe

Reputation: 1508

In the Model class, the call method documentation :

To call a model on an input, always use the __call__()̀ method, i.e. model(inputs), which relies on the underlying call() method.

And indeed, the __call__ can take any input argument : def __call__(self, *args, **kwargs): (in Model class source code)

You can find a more detailed answer here

Upvotes: 2

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