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+import tensorflow as tf

+import iris_data

+

+tf.logging.set_verbosity(tf.logging.INFO)

+

+def model_fn(features, labels, mode, params):

+    net = tf.feature_column.input_layer(features, params['feature_columns'])

+

+    for units in params['hidden_units']:

+        net = tf.layers.dense(net, units=units, activation=tf.nn.relu)

+

+    logits = tf.layers.dense(net, units=params['n_classes'], activation=None)

+

+    predictions = {'probabilities':tf.nn.softmax(logits, 1)}

+

+    if mode == tf.estimator.ModeKeys.PREDICT:

+        return tf.estimator.EstimatorSpec(mode, predictions = predictions)

+

+    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)

+

+    eval_metric_ops = {'accuracy':tf.metrics.accuracy(labels=labels, predictions=tf.argmax(logits, 1))}

+

+    if mode == tf.estimator.ModeKeys.EVAL:

+        return tf.estimator.EstimatorSpec(mode, loss=loss, eval_metric_ops = eval_metric_ops)

+

+    # Create training op.

+    assert mode == tf.estimator.ModeKeys.TRAIN

+

+    optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)

+    train_op = optimizer.minimize(loss=loss, global_step = tf.train.get_global_step())

+

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

+

+train, test = iris_data.load_data()

+

+train_x, train_y = train

+test_x, test_y = test

+

+feature_columns = []

+

+for key in train_x.keys():

+    feature_columns.append(tf.feature_column.numeric_column(key))

+

+classifier = tf.estimator.Estimator(

+    model_fn = model_fn,

+    params = {"n_classes":3, "feature_columns":feature_columns, "hidden_units":[10, 10]}

+)

+

+classifier.train(input_fn = lambda:iris_data.train_input_fn(train_x, train_y, 100), steps=1000)

+

+result = classifier.evaluate(input_fn = lambda : iris_data.eval_input_fn(test_x, test_y, 100))

+print result

+

+predict_x = {

+    'SepalLength': [5.1, 5.9, 6.9],

+    'SepalWidth': [3.3, 3.0, 3.1],

+    'PetalLength': [1.7, 4.2, 5.4],

+    'PetalWidth': [0.5, 1.5, 2.1],

+}

+

+predictions = classifier.predict(input_fn = lambda : iris_data.eval_input_fn(predict_x, None, 100))

+

+for p in predictions:

+    print p