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減衰率が低下するGradientDescentOptimizerでLinearClassifierを使用しようとしています。テンソルフローのエスティメータで減衰率を使用するにはどうすればよいですか？
マイコード：
def main():
# load data
features = np.load('data/feature_data.npz')
tx = features['arr_0']
y = features['arr_1']
## Prepare logistic regression
n_point, n_feat = tx.shape
# Input functions
def get_input_fn_from_numpy(tx, y, num_epochs=None, shuffle=True):
# Preprocess data
return tf.estimator.inputs.numpy_input_fn(
x={"x":tx},
y=y,
num_epochs=num_epochs,
shuffle=shuffle,
batch_size=128
)
cols_label = "x"
feature_cols = [tf.contrib.layers.real_valued_column(cols_label)]
my_input_fn_train = get_input_fn_from_numpy(tx, y)
model_dir = 'data/tmp/' + datetime.datetime.now().strftime("%m-%d_%H:%M:%S")
global_step = tf.Variable(0, trainable=False)
learning_rate=tf.train.exponential_decay(0.001*np.ones((20,1), dtype=np.float32), global_step, 10000, 0.95, staircase=False)
regressor = tf.contrib.learn.LinearClassifier(feature_columns=feature_cols,
model_dir=model_dir,
optimizer=tf.train.GradientDescentOptimizer(learning_rate=learning_rate))
regressor.fit(input_fn=get_input_fn_from_numpy(tx_train, y_train), steps=100000)
results = regressor.evaluate(input_fn=my_input_fn_test)
私はエラーを取得する：
File "training.py", line 71, in <module>
main()
File "training.py", line 63, in main
regressor.fit(input_fn=get_input_fn_from_numpy(tx_train, y_train), steps=100000)
File "/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 296, in new_func
return func(*args, **kwargs)
File "/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 458, in fit
loss = self._train_model(input_fn=input_fn, hooks=hooks)
File "/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 958, in _train_model
model_fn_ops = self._get_train_ops(features, labels)
File "/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1165, in _get_train_ops
return self._call_model_fn(features, labels, model_fn_lib.ModeKeys.TRAIN)
File "/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1136, in _call_model_fn
model_fn_results = self._model_fn(features, labels, **kwargs)
File "/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/linear.py", line 186, in _linear_model_fn
logits=logits)
File "/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py", line 854, in create_model_fn_ops
enable_centered_bias=self._enable_centered_bias)
File "/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py", line 649, in _create_model_fn_ops
batch_size, loss_fn, weight_tensor)
File "/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py", line 1911, in _train_op
train_op = train_op_fn(loss)
File "/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/linear.py", line 179, in _train_op_fn
zip(grads, my_vars), global_step=global_step))
File "/lib/python3.6/site-packages/tensorflow/python/training/optimizer.py", line 456, in apply_gradients
update_ops.append(processor.update_op(self, grad))
File "/lib/python3.6/site-packages/tensorflow/python/training/optimizer.py", line 97, in update_op
return optimizer._apply_dense(g, self._v) # pylint: disable=protected-access
File "/lib/python3.6/site-packages/tensorflow/python/training/gradient_descent.py", line 50, in _apply_dense
use_locking=self._use_locking).op
File "/lib/python3.6/site-packages/tensorflow/python/training/gen_training_ops.py", line 370, in apply_gradient_descent
name=name)
File "/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 330, in apply_op
g = ops._get_graph_from_inputs(_Flatten(keywords.values()))
File "/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 4262, in _get_graph_from_inputs
_assert_same_graph(original_graph_element, graph_element)
File "/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 4201, in _assert_same_graph
"%s must be from the same graph as %s." % (item, original_item))
ValueError: Tensor("ExponentialDecay:0", shape=(20, 1), dtype=float32) must be from the same graph as Tensor("linear/x/weight/part_0:0", shape=(20, 1), dtype=float32_ref).
私はtensorflow 1.3を使用しています。 私は学習率を定数、例えば0.01で置き換えたときに動作します。私は、過去には最小限の操作と組み合わせて減衰率を使用していましたが、LinearClassifier内でそれを使用しようとしていました。 私は、グローバルなステップをフィットのステップに結びつけないが、これがどのように機能するのだろうと思っていたという点で、何かが一貫していないように見えます。私はhereのようにプレースホルダーを使うことができると思いますが、私が必要としない場合はなぜ自分自身でアップデートルールをコーディングすべきかわかりません。
これを解決する方法の提案はありますか？