def model_fn(features, labels, mode): features = tf.layers.flatten(features) h1 = tf.layers.dense(features, 64, activation=tf.nn.relu) h2 = tf.layers.dense(h1, 64, activation=tf.nn.relu) logits = tf.layers.dense(h2, 10) if mode == tf.estimator.ModeKeys.EVAL or mode == tf.estimator.ModeKeys.TRAIN: loss = tf.reduce_mean( tf.losses.sparse_softmax_cross_entropy(logits=logits, labels=labels)) return TFEstimatorSpec(mode, predictions=logits, loss=loss) else: return TFEstimatorSpec(mode, predictions=logits)
def model_fn(features, labels, mode): from nets import lenet slim = tf.contrib.slim with slim.arg_scope(lenet.lenet_arg_scope()): logits, end_points = lenet.lenet(features, num_classes=10, is_training=True) if mode == tf.estimator.ModeKeys.EVAL or mode == tf.estimator.ModeKeys.TRAIN: loss = tf.reduce_mean( tf.losses.sparse_softmax_cross_entropy(logits=logits, labels=labels)) return TFEstimatorSpec(mode, predictions=logits, loss=loss) else: return TFEstimatorSpec(mode, predictions=logits)
def model_fn(features, labels, mode): create_graph(features) if mode == tf.estimator.ModeKeys.PREDICT: softmax_tensor = tf.get_default_graph().get_tensor_by_name( 'softmax:0') return TFEstimatorSpec(mode, predictions=softmax_tensor) else: raise NotImplementedError
def model_fn(features, labels, mode): assert features.shape.ndims == 1 if labels is not None: assert labels.shape.ndims == 0 features = tf.expand_dims(features, axis=0) h1 = tf.layers.dense(features, 64, activation=tf.nn.relu) h2 = tf.layers.dense(h1, 64, activation=tf.nn.relu) logits = tf.layers.dense(h2, 10) if mode == tf.estimator.ModeKeys.EVAL or mode == tf.estimator.ModeKeys.TRAIN: labels = tf.expand_dims(labels, axis=0) loss = tf.reduce_mean( tf.losses.sparse_softmax_cross_entropy(logits=logits, labels=labels)) return TFEstimatorSpec(mode, predictions=logits, loss=loss) else: return TFEstimatorSpec(mode, predictions=logits)
def model_fn(features, labels, mode): from nets import inception slim = tf.contrib.slim labels = tf.squeeze(labels, axis=1) with slim.arg_scope(inception.inception_v1_arg_scope()): logits, end_points = inception.inception_v1(features, num_classes=2, is_training=True) if mode == tf.estimator.ModeKeys.TRAIN: loss = tf.reduce_mean( tf.losses.sparse_softmax_cross_entropy(logits=logits, labels=labels)) return TFEstimatorSpec(mode, predictions=logits, loss=loss) else: raise NotImplementedError