move python code snippets to python folder

python/ml-1.py

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+import tensorflow as tf
+import numpy as np
+
+print(tf.__version__)
+
+from tensorflow.contrib.learn.python.learn.datasets import base
+
+# Data files
+IRIS_TRAINING = "iris_training.csv"
+IRIS_TEST = "iris_test.csv"
+
+# Load datasets.
+training_set = base.load_csv_with_header(filename=IRIS_TRAINING,
+                                         features_dtype=np.float32,
+                                         target_dtype=np.int)
+test_set = base.load_csv_with_header(filename=IRIS_TEST,
+                                     features_dtype=np.float32,
+                                     target_dtype=np.int)
+
+# Specify that all features have real-value data
+feature_name = "flower_features"
+feature_columns = [tf.feature_column.numeric_column(feature_name,
+                                                    shape=[4])]
+classifier = tf.estimator.LinearClassifier(
+    feature_columns=feature_columns,
+    n_classes=3,
+    model_dir="/tmp/iris_model")
+
+def input_fn(dataset):
+    def _fn():
+        features = {feature_name: tf.constant(dataset.data)}
+        label = tf.constant(dataset.target)
+        return features, label
+    return _fn
+
+# Fit model.
+classifier.train(input_fn=input_fn(training_set),
+               steps=1000)
+print('fit done')
+
+# Evaluate accuracy.
+accuracy_score = classifier.evaluate(input_fn=input_fn(test_set),
+                                     steps=100)["accuracy"]
+print('\nAccuracy: {0:f}'.format(accuracy_score))
+
+# Export the model for serving
+feature_spec = {'flower_features': tf.FixedLenFeature(shape=[4], dtype=np.float32)}
+
+serving_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)
+
+classifier.export_savedmodel(export_dir_base='/tmp/iris_model' + '/export',
+                            serving_input_receiver_fn=serving_fn)