def model_fn()

in _archiving/src/release/dev-day/mnist_hpo.py [0:0]

• 51 lines of code
• 7 McCabe index (conditional complexity)

def model_fn(features, labels, mode, params):
    # this script takes learning_rate as a hyperparameter
    learning_rate = params.get("learning_rate",0.1)
    
    # Input Layer
    input_layer = tf.reshape(features[INPUT_TENSOR_NAME], [-1, 28, 28, 1])

    # Convolutional Layer #1
    conv1 = tf.layers.conv2d(
        inputs=input_layer,
        filters=32,
        kernel_size=[5, 5],
        padding='same',
        activation=tf.nn.relu)

    # Pooling Layer #1
    pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)

    # Convolutional Layer #2 and Pooling Layer #2
    conv2 = tf.layers.conv2d(
        inputs=pool1,
        filters=64,
        kernel_size=[5, 5],
        padding='same',
        activation=tf.nn.relu)
    pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)

    # Dense Layer
    pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
    dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
    dropout = tf.layers.dropout(
        inputs=dense, rate=0.4, training=(mode == Modes.TRAIN))

    # Logits Layer
    logits = tf.layers.dense(inputs=dropout, units=10)

    # Define operations
    if mode in (Modes.PREDICT, Modes.EVAL):
        predicted_indices = tf.argmax(input=logits, axis=1)
        probabilities = tf.nn.softmax(logits, name='softmax_tensor')

    if mode in (Modes.TRAIN, Modes.EVAL):
        global_step = tf.train.get_or_create_global_step()
        label_indices = tf.cast(labels, tf.int32)
        loss = tf.losses.softmax_cross_entropy(
            onehot_labels=tf.one_hot(label_indices, depth=10), logits=logits)
        tf.summary.scalar('OptimizeLoss', loss)

    if mode == Modes.PREDICT:
        predictions = {
            'classes': predicted_indices,
            'probabilities': probabilities
        }
        export_outputs = {
            SIGNATURE_NAME: tf.estimator.export.PredictOutput(predictions)
        }
        return tf.estimator.EstimatorSpec(
            mode, predictions=predictions, export_outputs=export_outputs)

    if mode == Modes.TRAIN:
        optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
        train_op = optimizer.minimize(loss, global_step=global_step)
        return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)

    if mode == Modes.EVAL:
        eval_metric_ops = {
            'accuracy': tf.metrics.accuracy(label_indices, predicted_indices)
        }
        return tf.estimator.EstimatorSpec(
            mode, loss=loss, eval_metric_ops=eval_metric_ops)