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2019/11/28 06:36

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1	- 各クラスごとの正解率を出力させ~~たいです、！~~
1	+ cifar-10 各クラスごとの正解率を出力させる方法

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説明の修正

2019/11/28 06:36

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sinugo

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 # 各クラスごとの正解率を出力させ方を教えてください。
-ここに質問の内容を詳しく書いてください。
 今大学でtensorflowを使った雲画像の自動認識を勉強しています。
 ３クラス用意して学習しているのですが、
 評価時に出力される正解率は全体のもののみなので、
 クラスごとの正解率をそれぞれ出力したいと考えています。
+*現在の出力
+epoch ●● duration = ●● sec, prediction = ●●
+*実現したい出力
+epoch ●● class : 0 duration = ●● sec, prediction = ●●
+epoch ●● class : 1 duration = ●● sec, prediction = ●●
+epoch ●● class : 2 duration = ●● sec, prediction = ●●
 ### 該当のソースコード
 ```python3

文の修正

2019/11/26 09:05

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sinugo

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@@ -157,166 +157,15 @@
 if __name__ == '__main__':
   tf.app.run()
 ```
-```python3
-model.py
-# coding: UTF-8
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-import tensorflow.compat.v1 as tf
-tf.disable_v2_behavior()
-NUM_CLASSES = 3
-def _get_weights(shape, stddev=1.0):
-  var = tf.get_variable('weights', shape,
-                        initializer=tf.truncated_normal_initializer(stddev=stddev))
-  return var
-def _get_biases(shape, value=0.0):
-  var = tf.get_variable('biases', shape,
-                        initializer=tf.constant_initializer(value))
-  return var
-def inference(image_node):
-  # conv1
-  with tf.variable_scope('conv1') as scope:
-    weights = _get_weights(shape=[3, 3, 3, 64], stddev=0.05)
-    conv = tf.nn.conv2d(image_node, weights, [1, 1, 1, 1], padding='SAME')
-    biases = _get_biases([64], value=0.1)
-    bias = tf.nn.bias_add(conv, biases)
-    conv1 = tf.nn.relu(bias, name=scope.name)
-  # pool1
-  pool1 = tf.nn.max_pool(conv1, ksize=[1 ,2, 2, 1], strides=[1, 2, 2, 1],
-                         padding='SAME', name='pool1')
-  # norm1
-  norm1 = tf.nn.lrn(pool1, 4,
-                    bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1')
-  # conv2
-  with tf.variable_scope('conv2') as scope:
-    weights = _get_weights(shape=[3, 3, 64, 64], stddev=0.05)
-    conv = tf.nn.conv2d(norm1, weights, [1, 1, 1, 1], padding='SAME')
-    biases = _get_biases([64], value=0.1)
-    bias = tf.nn.bias_add(conv, biases)
-    conv2 = tf.nn.relu(bias, name=scope.name)
-  # norm2
-  norm2 = tf.nn.lrn(conv2, 4,
-                    bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2')
-  # pool2
-  pool2 = tf.nn.max_pool(norm2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
-                         padding='SAME', name='pool2')
-  # conv3
-  with tf.variable_scope('conv3') as scope:
-    weights = _get_weights(shape=[3, 3, 64, 128], stddev=0.05)
-    conv = tf.nn.conv2d(pool2, weights, [1, 1, 1, 1], padding='SAME')
-    biases = _get_biases([128], value=0.1)
-    bias = tf.nn.bias_add(conv, biases)
-    conv3 = tf.nn.relu(bias, name=scope.name)
-  # pool3
-  pool3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
-                         padding='SAME', name='pool3')
-  # norm3
-  norm3 = tf.nn.lrn(pool3, 4,
-                    bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm3')
-  reshape = tf.reshape(norm3, [1, -1])
-  dim = reshape.get_shape()[1].value
-  # fc3
-  with tf.variable_scope('fc3') as scope:
-    weights = _get_weights(shape=[dim, 256], stddev=0.05)
-    biases = _get_biases([256], value=0.1)
-    fc3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)
-  # fc4
-  with tf.variable_scope('fc4') as scope:
-    weights = _get_weights(shape=[256, 128], stddev=0.05)
-    biases = _get_biases([128], value=0.1)
-    fc4 = tf.nn.relu(tf.matmul(fc3, weights) + biases, name=scope.name)
- # output
-  with tf.variable_scope('output') as scope:
-    weights = _get_weights(shape=[128, NUM_CLASSES], stddev=1 / 128.0)
-    biases = _get_biases([NUM_CLASSES], value=0.0)
-    logits = tf.add(tf.matmul(fc4, weights), biases, name='logits')
-  return logits
-```
-```python3
-reader.py
-# coding: UTF-8
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-import os
-import numpy as np
-class Cifar10Record(object):
-  width = 32
-  height = 32
-  depth = 3
-  def set_label(self, label_byte):
-    self.label = np.frombuffer(label_byte, dtype=np.uint8)
-  def set_image(self, image_bytes):
-    byte_buffer = np.frombuffer(image_bytes, dtype=np.int8)
-    reshaped_array = np.reshape(byte_buffer,
-                                [self.depth, self.height, self.width])
-    self.byte_array = np.transpose(reshaped_array, [1, 2, 0])
-    self.byte_array = self.byte_array.astype(np.float32)
-class Cifar10Reader(object):
-  def __init__(self, filename):
-    if not os.path.exists(filename):
-      print(filename + ' is not exist')
-      return
-    self.bytestream = open(filename, mode="rb")
-  def close(self):
-    if not self.bytestream:
-      self.bytestream.close()
-  def read(self, index):
-    result = Cifar10Record()
-    label_bytes = 1
-    image_bytes = result.height * result.width * result.depth
-    record_bytes = label_bytes + image_bytes
-    self.bytestream.seek(record_bytes * index, 0)
-    result.set_label(self.bytestream.read(label_bytes))
-    result.set_image(self.bytestream.read(image_bytes))
-    return result
-```
 ### 試したこと
+_eval関数をもう１つ作って、
+ prediction = _eval(sess, top_k_op,
-いくつかサイトを調べて自分のプログラムに合うように調整してみたのですが、
+                         train_placeholder, label_placeholder)
+      print('epoch %d duration = %d sec, prediction = %.3f'
+            % (epoch, duration, prediction))
-なかなかうまく出力できませんでした、、、
+をfor文で回そうとしたのですがうまく行きませんでした。
 ### 補足情報（FW/ツールのバージョンなど）
 プログラムは「TensorFlowはじめました ― 実践！最新Googleマシンラーニング」を参考にしています。

2019/11/22 08:38

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sinugo

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1	- 各クラスごとの正解率を出力させたいです、、！
1	+ 各クラスごとの正解率を出力させたいです、！

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