Python base.load_iris函数代码示例

  def testAdditionalOutputs(self):
    """Tests multi-class classification using matrix data as input."""
    hparams = tensor_forest.ForestHParams(
        num_trees=1,
        max_nodes=100,
        num_classes=3,
        num_features=4,
        split_after_samples=20,
        inference_tree_paths=True)
    classifier = random_forest.CoreTensorForestEstimator(
        hparams.fill(), keys_column='keys', include_all_in_serving=True)

    iris = base.load_iris()
    data = iris.data.astype(np.float32)
    labels = iris.target.astype(np.int32)

    input_fn = numpy_io.numpy_input_fn(
        x={
            'x': data,
            'keys': np.arange(len(iris.data)).reshape(150, 1)
        },
        y=labels,
        batch_size=10,
        num_epochs=1,
        shuffle=False)

    classifier.train(input_fn=input_fn, steps=100)
    predictions = list(classifier.predict(input_fn=input_fn))
    # Check that there is a key column, tree paths and var.
    for pred in predictions:
      self.assertTrue('keys' in pred)
      self.assertTrue('tree_paths' in pred)
      self.assertTrue('prediction_variance' in pred)

  def testWithFeatureColumns(self):
    head_fn = head_lib._multi_class_head_with_softmax_cross_entropy_loss(
        n_classes=3, loss_reduction=losses.Reduction.SUM_OVER_NONZERO_WEIGHTS)

    hparams = tensor_forest.ForestHParams(
        num_trees=3,
        max_nodes=1000,
        num_classes=3,
        num_features=4,
        split_after_samples=20,
        inference_tree_paths=True)

    est = random_forest.CoreTensorForestEstimator(
        hparams.fill(),
        head=head_fn,
        feature_columns=[core_feature_column.numeric_column('x')])

    iris = base.load_iris()
    data = {'x': iris.data.astype(np.float32)}
    labels = iris.target.astype(np.int32)

    input_fn = numpy_io.numpy_input_fn(
        x=data, y=labels, batch_size=150, num_epochs=None, shuffle=False)

    est.train(input_fn=input_fn, steps=100)
    res = est.evaluate(input_fn=input_fn, steps=1)

    self.assertEqual(1.0, res['accuracy'])
    self.assertAllClose(0.55144483, res['loss'])

 def _input_fn():
   iris = base.load_iris()
   return {
       'feature': constant_op.constant(
           iris.data, dtype=dtypes.float32)
   }, constant_op.constant(
       iris.target, shape=[150], dtype=dtypes.int32)

 def testIrisDNN(self):
   iris = base.load_iris()
   feature_columns = [feature_column.real_valued_column("", dimension=4)]
   classifier = dnn.DNNClassifier(
       feature_columns=feature_columns,
       hidden_units=[10, 20, 10],
       n_classes=3,
       config=run_config.RunConfig(tf_random_seed=1))
   classifier.fit(iris.data, iris.target, max_steps=200)
   variable_names = classifier.get_variable_names()
   self.assertEqual(
       classifier.get_variable_value("dnn/hiddenlayer_0/weights").shape,
       (4, 10))
   self.assertEqual(
       classifier.get_variable_value("dnn/hiddenlayer_1/weights").shape,
       (10, 20))
   self.assertEqual(
       classifier.get_variable_value("dnn/hiddenlayer_2/weights").shape,
       (20, 10))
   self.assertEqual(
       classifier.get_variable_value("dnn/logits/weights").shape, (10, 3))
   self.assertIn("dnn/hiddenlayer_0/biases", variable_names)
   self.assertIn("dnn/hiddenlayer_1/biases", variable_names)
   self.assertIn("dnn/hiddenlayer_2/biases", variable_names)
   self.assertIn("dnn/logits/biases", variable_names)

 def testIrisInputFn(self):
   iris = base.load_iris()
   est = estimator.Estimator(model_fn=logistic_model_no_mode_fn)
   est.fit(input_fn=iris_input_fn, steps=100)
   _ = est.evaluate(input_fn=iris_input_fn, steps=1)
   predictions = list(est.predict(x=iris.data))
   self.assertEqual(len(predictions), iris.target.shape[0])

def iris_input_multiclass_fn():
  iris = base.load_iris()
  return {
      'feature': constant_op.constant(
          iris.data, dtype=dtypes.float32)
  }, constant_op.constant(
      iris.target, shape=(150, 1), dtype=dtypes.int32)

 def testIrisAllDictionaryInput(self):
   iris = base.load_iris()
   est = estimator.Estimator(model_fn=logistic_model_no_mode_fn)
   iris_data = {'input': iris.data}
   iris_target = {'labels': iris.target}
   est.fit(iris_data, iris_target, steps=100)
   scores = est.evaluate(
       x=iris_data,
       y=iris_target,
       metrics={
           ('accuracy', 'class'): metric_ops.streaming_accuracy
       })
   predictions = list(est.predict(x=iris_data))
   predictions_class = list(est.predict(x=iris_data, outputs=['class']))
   self.assertEqual(len(predictions), iris.target.shape[0])
   classes_batch = np.array([p['class'] for p in predictions])
   self.assertAllClose(classes_batch,
                       np.array([p['class'] for p in predictions_class]))
   self.assertAllClose(classes_batch,
                       np.argmax(
                           np.array([p['prob'] for p in predictions]), axis=1))
   other_score = _sklearn.accuracy_score(iris.target, classes_batch)
   self.assertAllClose(other_score, scores['accuracy'])
   self.assertTrue('global_step' in scores)
   self.assertEqual(scores['global_step'], 100)

def _iris_data_input_fn():
  # Converts iris data to a logistic regression problem.
  iris = base.load_iris()
  ids = np.where((iris.target == 0) | (iris.target == 1))
  features = constant_op.constant(iris.data[ids], dtype=dtypes.float32)
  labels = constant_op.constant(iris.target[ids], dtype=dtypes.float32)
  labels = array_ops.reshape(labels, labels.get_shape().concatenate(1))
  return features, labels

def iris_input_fn_labels_dict():
  iris = base.load_iris()
  features = array_ops.reshape(
      constant_op.constant(iris.data), [-1, _IRIS_INPUT_DIM])
  labels = {
      'labels': array_ops.reshape(constant_op.constant(iris.target), [-1])
  }
  return features, labels

 def testIrisIteratorPlainInt(self):
   iris = base.load_iris()
   est = estimator.Estimator(model_fn=logistic_model_no_mode_fn)
   x_iter = itertools.islice(iris.data, 100)
   y_iter = (v for v in iris.target)
   est.fit(x_iter, y_iter, steps=100)
   _ = est.evaluate(input_fn=iris_input_fn, steps=1)
   _ = six.next(est.predict(x=iris.data))['class']

 def testMultiClass_NpMatrixData(self):
   """Tests multi-class classification using numpy matrix data as input."""
   iris = base.load_iris()
   train_x = iris.data
   train_y = iris.target
   classifier = debug.DebugClassifier(n_classes=3)
   classifier.fit(x=train_x, y=train_y, steps=200)
   scores = classifier.evaluate(x=train_x, y=train_y, steps=1)
   self._assertInRange(0.0, 1.0, scores['accuracy'])

 def testIrisIterator(self):
   iris = base.load_iris()
   est = estimator.Estimator(model_fn=logistic_model_no_mode_fn)
   x_iter = itertools.islice(iris.data, 100)
   y_iter = itertools.islice(iris.target, 100)
   est.fit(x_iter, y_iter, steps=100)
   _ = est.evaluate(input_fn=iris_input_fn, steps=1)
   predictions = list(est.predict(x=iris.data))
   self.assertEqual(len(predictions), iris.target.shape[0])

 def testDNNDropout0(self):
   # Dropout prob == 0.
   iris = base.load_iris()
   feature_columns = [feature_column.real_valued_column("", dimension=4)]
   classifier = dnn.DNNClassifier(
       feature_columns=feature_columns,
       hidden_units=[10, 20, 10],
       n_classes=3,
       dropout=0.0,
       config=run_config.RunConfig(tf_random_seed=1))
   classifier.fit(iris.data, iris.target, max_steps=200)

def _get_classification_input_fns():
  iris = base.load_iris()
  data = iris.data.astype(np.float32)
  labels = iris.target.astype(np.int32)

  train_input_fn = numpy_io.numpy_input_fn(
      x=data, y=labels, batch_size=150, num_epochs=None, shuffle=False)

  predict_input_fn = numpy_io.numpy_input_fn(
      x=data[:1,], y=None, batch_size=1, num_epochs=1, shuffle=False)
  return train_input_fn, predict_input_fn

def MLP_iris():
    # load the iris data.
    iris = load_iris()

    np.random.seed(0)
    random_index = np.random.permutation(150)

    iris_data = iris.data[random_index]
    iris_target = iris.target[random_index]
    iris_target_onehot = np.zeros((150, 3))
    iris_target_onehot[np.arange(150), iris_target] = 1

    accuracy_list = []

    # build computation graph
    x = tf.placeholder("float", shape=[None, 4], name='x')
    y_target = tf.placeholder("float", shape=[None, 3], name='y_target')

    W1 = tf.Variable(tf.zeros([4, 128]), name='W1')
    b1 = tf.Variable(tf.zeros([128]), name='b1')
    h1 = tf.sigmoid(tf.matmul(x, W1) + b1, name='h1')

    W2 = tf.Variable(tf.zeros([128, 3]), name='W2')
    b2 = tf.Variable(tf.zeros([3]), name='b2')
    y = tf.nn.softmax(tf.matmul(h1, W2) + b2, name='y')

    cross_entropy = -tf.reduce_sum(y_target * tf.log(y), name='cross_entropy')

    train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_target, 1))

    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))

    sess = tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True)))
    sess.run(tf.global_variables_initializer())

    for i in range(500):
        sess.run(train_step, feed_dict={x: iris_data[0:100], y_target: iris_target_onehot[0:100]})

        train_accuracy = sess.run(accuracy, feed_dict={x: iris_data[0:100], y_target: iris_target_onehot[0:100]})
        validation_accuracy = sess.run(accuracy, feed_dict={x: iris_data[100:], y_target: iris_target_onehot[100:]})
        print (
        "step %d, training accuracy: %.3f / validation accuracy: %.3f" % (i, train_accuracy, validation_accuracy))

        accuracy_list.append(validation_accuracy)

        if i >= 50:
            if validation_accuracy - np.mean(accuracy_list[int(round(len(accuracy_list) / 2)):]) <= 0.01:
                break

    sess.close()