update readme.md and pys

burness · May 7, 2017 · 8530533 · 8530533
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diff --git a/machinelearning_toolkit/README.md b/machinelearning_toolkit/README.md
@@ -2,4 +2,14 @@
 
 ### LinearClassifier
 ### RandomForest
-### SVM
+### SVM
+
+
+### Add monitor in wide and deep model
+[https://www.tensorflow.org/get_started/monitors](https://www.tensorflow.org/get_started/monitors)
+
+ 1. add `tf.logging.set_verbosity(tf.logging.INFO)`, then you can log the loss:
+    ![](../images/mltookit_log_00.png)
+ 2. validation_monitor: a bug with input_fn, eval_steps=None gen the bug, set eval_steps =1 would solve it.
+    ![](../images/mltookit_log_01.png)
+ 3. how to set validation_metrics, see the code [tensorflow/contrib/learn/python/learn/metric_spec.py](https://github.com/tensorflow/tensorflow/blob/r1.1/tensorflow/contrib/learn/python/learn/metric_spec.py) here. Specify the prediction_key and labal_key (None maybe the default )
diff --git a/machinelearning_toolkit/scripts/linear_classifier.py b/machinelearning_toolkit/scripts/linear_classifier.py
@@ -205,14 +205,8 @@
     df_train[LABEL_COLUMN].apply(lambda x: '+' in x)).astype(int)
 df_test[LABEL_COLUMN] = (
     df_test[LABEL_COLUMN].apply(lambda x: '+' in x)).astype(int)
-# print df_train.dtypes
 dtypess = df_train.dtypes
 
-# print dtypess[CATEGORICAL_COLUMNS]
-
-# print df_train.head(5)
-# print df_test.head(5)
-
 
 def input_fn(df):
     # Creates a dictionary mapping from each continuous feature column name (k) to

diff --git a/machinelearning_toolkit/scripts/tf-rf.py b/machinelearning_toolkit/scripts/tf-rf.py
@@ -254,15 +254,18 @@ def eval_input_fn():
     "accuracy":
     tf.contrib.learn.MetricSpec(
         metric_fn=tf.contrib.metrics.streaming_accuracy,
-        prediction_key="classes"),
+        prediction_key='probabilities'
+        ),
     "precision":
     tf.contrib.learn.MetricSpec(
          metric_fn=tf.contrib.metrics.streaming_precision,
-         prediction_key="classes"),
+         prediction_key='probabilities'
+         ),
     "recall":
     tf.contrib.learn.MetricSpec(
         metric_fn=tf.contrib.metrics.streaming_recall,
-        prediction_key="classes")
+        prediction_key='probabilities'
+        )
     }
 
 hparams = tf.contrib.tensor_forest.python.tensor_forest.ForestHParams(

diff --git a/zhihu_code/README.md b/zhihu_code/README.md
@@ -0,0 +1,7 @@
+## how to build a dl model to solve the zhihu verification recognition
+
+**issue 1**
+
+How to gen the random flip text image, it seems that `draw.text(pos, txt, font=self.font, fill=fill)` and the `rotate` are not useful.
+Google whether there is a solution to gen the random flip text in a image.
+Or I can concat the images (random flip text images) to gen a new Image.