s/eval_metrics/eval_metric_ops/g

README.md

@@ -51,8 +51,8 @@ run(model_fn, train_input_fn, eval_input_fn=None)
                 `Tensor, ...`,
                 `Tensor, ..., mode=ModeKeys`.
             Types of its return values must be one of the following:
-                `Tensor, Tensor, Operation, eval_metrics=dict<str, Tensor>`
-                (predictions, loss, train_op, and eval_metrics (if any)),
+                `Tensor, Tensor, Operation, eval_metric_ops=dict<str, Tensor>`
+                (predictions, loss, train_op, and eval_metric_ops (if any)),
                 `ModelFnOps`.
         train_input_fn, eval_input_fn: Functions to serve input Tensors
             fed into the model. If `eval_input_fn` is `None`,

docs/qnd/index.html

@@ -1162,8 +1162,8 @@ <h2 class="section-title" id="header-functions">Functions</h2>
                     `Tensor, ...`,
                     `Tensor, ..., mode=ModeKeys`.
                 Types of its return values must be one of the following:
-                    `Tensor, Tensor, Operation, eval_metrics=dict<str, Tensor>`
-                    (predictions, loss, train_op, and eval_metrics (if any)),
+                    `Tensor, Tensor, Operation, eval_metric_ops=dict<str, Tensor>`
+                    (predictions, loss, train_op, and eval_metric_ops (if any)),
                     `ModelFnOps`.
             train_input_fn, eval_input_fn: Functions to serve input Tensors
                 fed into the model. If `eval_input_fn` is `None`,

examples/mnist/mnist.py

@@ -41,7 +41,7 @@ def mnist_model(image, number):
         tf.nn.sparse_softmax_cross_entropy_with_logits(h, number))
     predictions = tf.argmax(h, axis=1)
     train_op = minimize(loss)
-    eval_metrics = {
+    eval_metric_ops = {
         "accuracy": tf.reduce_mean(tf.to_float(tf.equal(predictions, number)))
     }
 
@@ -50,9 +50,9 @@ def mnist_model(image, number):
             predictions=predictions,
             loss=loss,
             train_op=train_op,
-            eval_metrics=eval_metrics)
+            eval_metric_ops=eval_metric_ops)
 
-    return predictions, loss, train_op, eval_metrics
+    return predictions, loss, train_op, eval_metric_ops
 
 
 run = qnd.def_run()

qnd/run.py

@@ -36,8 +36,8 @@ def run(model_fn, train_input_fn, eval_input_fn=None):
                     `Tensor, ...`,
                     `Tensor, ..., mode=ModeKeys`.
                 Types of its return values must be one of the following:
-                    `Tensor, Tensor, Operation, eval_metrics=dict<str, Tensor>`
-                    (predictions, loss, train_op, and eval_metrics (if any)),
+                    `Tensor, Tensor, Operation, eval_metric_ops=dict<str, Tensor>`
+                    (predictions, loss, train_op, and eval_metric_ops (if any)),
                     `ModelFnOps`.
             train_input_fn, eval_input_fn: Functions to serve input Tensors
                 fed into the model. If `eval_input_fn` is `None`,