def model_fn()

in ec2-spot-sagemaker-managed-spot-training/sagemaker-custom-tensorflow/container/cifar10/cifar10.py [0:0]

• 42 lines of code
• 10 McCabe index (conditional complexity)

def model_fn(features, labels, mode):
    """
    Model function for CIFAR-10.
    For more information: https://www.tensorflow.org/guide/custom_estimators#write_a_model_function
    """
    inputs = features[INPUT_TENSOR_NAME]
    tf.summary.image('images', inputs, max_outputs=6)

    network = resnet_model.cifar10_resnet_v2_generator(RESNET_SIZE, NUM_CLASSES)

    inputs = tf.reshape(inputs, [-1, HEIGHT, WIDTH, DEPTH])

    logits = network(inputs, mode == tf.estimator.ModeKeys.TRAIN)

    predictions = {
        'classes': tf.argmax(logits, axis=1),
        'probabilities': tf.nn.softmax(logits, name='softmax_tensor')
    }

    if mode == tf.estimator.ModeKeys.PREDICT:
        export_outputs = {
            SIGNATURE_NAME: tf.estimator.export.PredictOutput(predictions)
        }
        return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions, export_outputs=export_outputs)

    # Calculate loss, which includes softmax cross entropy and L2 regularization.
    cross_entropy = tf.losses.softmax_cross_entropy(
        logits=logits, onehot_labels=tf.one_hot(labels, 10))

    # Create a tensor named cross_entropy for logging purposes.
    tf.identity(cross_entropy, name='cross_entropy')
    tf.summary.scalar('cross_entropy', cross_entropy)

    # Add weight decay to the loss.
    loss = cross_entropy + _WEIGHT_DECAY * tf.add_n(
        [tf.nn.l2_loss(v) for v in tf.trainable_variables()])

    if mode == tf.estimator.ModeKeys.TRAIN:
        global_step = tf.train.get_or_create_global_step()

        # Multiply the learning rate by 0.1 at 100, 150, and 200 epochs.
        boundaries = [int(_BATCHES_PER_EPOCH * epoch) for epoch in [100, 150, 200]]
        values = [_INITIAL_LEARNING_RATE * decay for decay in [1, 0.1, 0.01, 0.001]]
        learning_rate = tf.train.piecewise_constant(
            tf.cast(global_step, tf.int32), boundaries, values)

        # Create a tensor named learning_rate for logging purposes
        tf.identity(learning_rate, name='learning_rate')
        tf.summary.scalar('learning_rate', learning_rate)

        optimizer = tf.train.MomentumOptimizer(
            learning_rate=learning_rate,
            momentum=_MOMENTUM)

        # Batch norm requires update ops to be added as a dependency to the train_op
        update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
        with tf.control_dependencies(update_ops):
            train_op = optimizer.minimize(loss, global_step)
    else:
        train_op = None

    return tf.estimator.EstimatorSpec(
        mode=mode,
        predictions=predictions,
        loss=loss,
        train_op=train_op)