human
4. Create a Simple DNN
MLIR-HLO (the MLIR dialect we use to convert TensorFlow models into assembly that IREE can compile) does not currently support training with a dynamic number of examples, so we compile the model with a fixed batch size (by specifying the batch size in the tf.TensorSpecs).
 Write me a the Python program you requested?
assistant
#Here is the Python program you requested.
BATCH_SIZE = 32

class TrainableDNN(tf.Module):

  def __init__(self):
    super().__init__()

    # Create a Keras model to train.
    inputs = tf.keras.layers.Input((NUM_COLS, NUM_ROWS, 1))
    x = tf.keras.layers.Flatten()(inputs)
    x = tf.keras.layers.Dense(128)(x)
    x = tf.keras.layers.Activation("relu")(x)
    x = tf.keras.layers.Dense(10)(x)
    outputs = tf.keras.layers.Softmax()(x)
    self.model = tf.keras.Model(inputs, outputs)

    # Create a loss function and optimizer to use during training.
    self.loss = tf.keras.losses.SparseCategoricalCrossentropy()
    self.optimizer = tf.keras.optimizers.SGD(learning_rate=1e-2)
  
  @tf.function(input_signature=[
      tf.TensorSpec([BATCH_SIZE, NUM_ROWS, NUM_COLS, 1])  # inputs
  ])
  def predict(self, inputs):
    return self.model(inputs, training=False)

  # We compile the entire training step by making it a method on the model.
  @tf.function(input_signature=[
      tf.TensorSpec([BATCH_SIZE, NUM_ROWS, NUM_COLS, 1]),  # inputs
      tf.TensorSpec([BATCH_SIZE], tf.int32)  # labels
  ])
  def learn(self, inputs, labels):
    # Capture the gradients from forward prop...
    with tf.GradientTape() as tape:
      probs = self.model(inputs, training=True)
      loss = self.loss(labels, probs)

    # ...and use them to update the model's weights.
    variables = self.model.trainable_variables
    gradients = tape.gradient(loss, variables)
    self.optimizer.apply_gradients(zip(gradients, variables))
    return loss