I am trying to use a LinearClassifier with a GradientDescentOptimizer with a decaying learning rate.
My code:
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) I get the error:
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). I am using tensorflow 1.3. It works if i replace the learning rate by a constant, say 0.01. I have used a decaying learning rate in the past in combination with minimize operation but was trying to use it within LinearClassifier. I see that something seems inconsistent in the fact that I don't link the global step to the step in the fit, but was wondering how this can work. I suppose I could use a placeholder as suggested here but I don't see why I should code the update rule myself if i don't need to.
Any suggestions on how to solve this ?