change float to int

tensnsorflow/test.py

@@ -29,18 +29,18 @@ def gen_tfrecords(in_file):
 
     for i in range(df.shape[0]):
         features = tf.train.Features(feature={
-            "y": tf.train.Feature(float_list=tf.train.FloatList(value=[df["y"][i]])),
-            "z": tf.train.Feature(float_list=tf.train.FloatList(value=[df["z"][i]])),
-            "top": tf.train.Feature(float_list=tf.train.FloatList(value=[df["top"][i]])),
-            "channel": tf.train.Feature(float_list=tf.train.FloatList(value=[df["channel"][i]])),
-            "ucity_id": tf.train.Feature(float_list=tf.train.FloatList(value=[df["ucity_id"][i]])),
-            "clevel1_id": tf.train.Feature(float_list=tf.train.FloatList(value=[df["clevel1_id"][i]])),
-            "ccity_name": tf.train.Feature(float_list=tf.train.FloatList(value=[df["ccity_name"][i]])),
-            "device_type": tf.train.Feature(float_list=tf.train.FloatList(value=[df["device_type"][i]])),
-            "manufacturer": tf.train.Feature(float_list=tf.train.FloatList(value=[df["manufacturer"][i]])),
-            "level2_ids": tf.train.Feature(float_list=tf.train.FloatList(value=[df["level2_ids"][i]])),
-            "time": tf.train.Feature(float_list=tf.train.FloatList(value=[df["time"][i]])),
-            "stat_date": tf.train.Feature(float_list=tf.train.FloatList(value=[df["stat_date"][i]]))
+            "y": tf.train.Feature(int64_list=tf.train.Int64List(value=[df["y"][i]])),
+            "z": tf.train.Feature(int64_list=tf.train.Int64List(value=[df["z"][i]])),
+            "top": tf.train.Feature(int64_list=tf.train.Int64List(value=[df["top"][i]])),
+            "channel": tf.train.Feature(int64_list=tf.train.Int64List(value=[df["channel"][i]])),
+            "ucity_id": tf.train.Feature(int64_list=tf.train.Int64List(value=[df["ucity_id"][i]])),
+            "clevel1_id": tf.train.Feature(int64_list=tf.train.Int64List(value=[df["clevel1_id"][i]])),
+            "ccity_name": tf.train.Feature(int64_list=tf.train.Int64List(value=[df["ccity_name"][i]])),
+            "device_type": tf.train.Feature(int64_list=tf.train.Int64List(value=[df["device_type"][i]])),
+            "manufacturer": tf.train.Feature(int64_list=tf.train.Int64List(value=[df["manufacturer"][i]])),
+            "level2_ids": tf.train.Feature(int64_list=tf.train.Int64List(value=[df["level2_ids"][i]])),
+            "time": tf.train.Feature(int64_list=tf.train.Int64List(value=[df["time"][i]])),
+            "stat_date": tf.train.Feature(int64_list=tf.train.Int64List(value=[df["stat_date"][i]]))
         })
 
         example = tf.train.Example(features = features)
@@ -62,4 +62,4 @@ def main(_):
 
 if __name__ == "__main__":
     tf.logging.set_verbosity(tf.logging.INFO)
-    tf.app.run()
\ No newline at end of file
+    tf.app.run()

tensnsorflow/train.py

@@ -51,18 +51,18 @@ def input_fn(filenames, batch_size=32, num_epochs=1, perform_shuffle=False):
     print('Parsing', filenames)
     def _parse_fn(record):
         features = {
-            "y": tf.FixedLenFeature([], tf.float32),
-            "z": tf.FixedLenFeature([], tf.float32),
-            "top": tf.FixedLenFeature([], tf.float32),
-            "channel": tf.FixedLenFeature([], tf.float32),
-            "ucity_id": tf.FixedLenFeature([], tf.float32),
-            "clevel1_id": tf.FixedLenFeature([], tf.float32),
-            "ccity_name": tf.FixedLenFeature([], tf.float32),
-            "device_type": tf.FixedLenFeature([], tf.float32),
-            "manufacturer": tf.FixedLenFeature([], tf.float32),
-            "level2_ids": tf.FixedLenFeature([], tf.float32),
-            "time": tf.FixedLenFeature([], tf.float32),
-            "stat_date": tf.FixedLenFeature([], tf.float32)
+            "y": tf.FixedLenFeature([], tf.int64),
+            "z": tf.FixedLenFeature([], tf.int64),
+            "top": tf.FixedLenFeature([], tf.int64),
+            "channel": tf.FixedLenFeature([], tf.int64),
+            "ucity_id": tf.FixedLenFeature([], tf.int64),
+            "clevel1_id": tf.FixedLenFeature([], tf.int64),
+            "ccity_name": tf.FixedLenFeature([], tf.int64),
+            "device_type": tf.FixedLenFeature([], tf.int64),
+            "manufacturer": tf.FixedLenFeature([], tf.int64),
+            "level2_ids": tf.FixedLenFeature([], tf.int64),
+            "time": tf.FixedLenFeature([], tf.int64),
+            "stat_date": tf.FixedLenFeature([], tf.int64)
         }
 
         parsed = tf.parse_single_example(record, features)