def test_estimator_for_imageset(self): model_fn = self.create_model_fn() input_fn = self.create_imageset_input_fn() estimator = TFEstimator(model_fn, tf.train.AdamOptimizer()) estimator.train(input_fn, steps=1) estimator.evaluate(input_fn, ["acc"]) results = estimator.predict(input_fn).get_predict().collect() assert all(r[1] is not None for r in results)
def test_estimator_without_batch(self): 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 input_fn(mode): np.random.seed(20) x = np.random.rand(20, 10) y = np.random.randint(0, 10, (20)) rdd_x = self.sc.parallelize(x) rdd_y = self.sc.parallelize(y) rdd = rdd_x.zip(rdd_y) if mode == tf.estimator.ModeKeys.TRAIN or mode == tf.estimator.ModeKeys.EVAL: dataset = TFDataset.from_rdd(rdd, features=(tf.float32, [10]), labels=(tf.int32, [])) else: dataset = TFDataset.from_rdd(rdd_x, features=(tf.float32, [10])) return dataset estimator = TFEstimator(model_fn, tf.train.AdamOptimizer()) self.intercept( lambda: estimator.train(input_fn, steps=1), "The batch_size of TFDataset must be specified when used for training." ) estimator.evaluate(input_fn, ["acc"]) estimator.predict(input_fn).collect()
def test_init_TFDataset_from_ndarrays(self): model_fn = self.create_model_fn() def input_fn(mode): x = np.random.rand(20, 10) y = np.random.randint(0, 10, (20,)) if mode == tf.estimator.ModeKeys.TRAIN: return TFDataset.from_ndarrays((x, y), batch_size=8) elif mode == tf.estimator.ModeKeys.EVAL: return TFDataset.from_ndarrays((x, y), batch_per_thread=1) else: return TFDataset.from_ndarrays(x, batch_per_thread=1) estimator = TFEstimator(model_fn, tf.train.AdamOptimizer()) estimator.train(input_fn, 10) estimator.evaluate(input_fn, ["acc"]) estimator.predict(input_fn)
def main(): sc = init_nncontext() 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 input_fn(mode): if mode == tf.estimator.ModeKeys.TRAIN: training_rdd = get_data_rdd("train", sc) dataset = TFDataset.from_rdd(training_rdd, features=(tf.float32, [28, 28, 1]), labels=(tf.int32, []), batch_size=320) elif mode == tf.estimator.ModeKeys.EVAL: testing_rdd = get_data_rdd("test", sc) dataset = TFDataset.from_rdd(testing_rdd, features=(tf.float32, [28, 28, 1]), labels=(tf.int32, []), batch_size=320) else: testing_rdd = get_data_rdd("test", sc).map(lambda x: x[0]) dataset = TFDataset.from_rdd(testing_rdd, features=(tf.float32, [28, 28, 1]), batch_per_thread=80) return dataset estimator = TFEstimator(model_fn, tf.train.AdamOptimizer(), model_dir="/tmp/estimator") estimator.train(input_fn, steps=60000 // 320) metrics = estimator.evaluate(input_fn, ["acc"]) print(metrics) predictions = estimator.predict(input_fn) print(predictions.first())
def test_evaluating(self): model_fn = self.create_model_fn() input_fn = self.create_input_fn() estimator = TFEstimator(model_fn, tf.train.AdamOptimizer()) eval_results = estimator.evaluate(input_fn, ["acc"]) assert len(eval_results) > 0