編集履歴

質問編集履歴

誤字

2018/01/15 02:53

投稿

TyoNgc

スコア14

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@@ -249,201 +249,3 @@
 以下略
 ```
-修正後
-```
-def inference(images_placeholder, keep_prob):
-    """ 予測モデルを作成する関数
-    引数:
-        images_placeholder: 画像のplaceholder
-        keep_prob: dropout率のplace_holder
-    返り値:
-        y_conv: 各クラスの確率(のようなもの)
-    """
-    # 重みを標準偏差0.1の正規分布で初期化
-    def weight_variable(shape):
-      initial = tf.truncated_normal(shape, stddev=0.1)
-      return tf.Variable(initial)
-    # バイアスを標準偏差0.1の正規分布で初期化
-    def bias_variable(shape):
-      initial = tf.constant(0.1, shape=shape)
-      return tf.Variable(initial)
-    # 畳み込み層の作成
-    def conv2d(x, W):
-      return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
-    # プーリング層の作成
-    def max_pool_2x2(x):
-      return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
-                            strides=[1, 2, 2, 1], padding='SAME')
-    # 入力を28x28x3に変形
-    x_image = tf.reshape(images_placeholder, [-1, IMAGE_SIZE, IMAGE_SIZE, 3])
-    # 畳み込み層1の作成
-    with tf.name_scope('conv1') as scope:
-        W_conv1 = weight_variable([5, 5, 3, 32])
-        b_conv1 = bias_variable([32])
-        h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
-    # プーリング層1の作成
-    with tf.name_scope('pool1') as scope:
-        h_pool1 = max_pool_2x2(h_conv1)
-    # 畳み込み層2の作成
-    with tf.name_scope('conv2') as scope:
-        W_conv2 = weight_variable([5, 5, 32, 64])
-        b_conv2 = bias_variable([64])
-        h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
-    # プーリング層2の作成
-    with tf.name_scope('pool2') as scope:
-        h_pool2 = max_pool_2x2(h_conv2)
-    # 畳み込み層3の作成
-    with tf.name_scope('conv3') as scope:
-        W_conv3 = weight_variable([5, 5, 64, 64])
-        b_conv3 = bias_variable([64])
-        h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3)
-    # 畳み込み層4の作成
-    with tf.name_scope('conv4') as scope:
-        W_conv4 = weight_variable([5, 5, 64, 64])
-        b_conv4 = bias_variable([64])
-        h_conv4 = tf.nn.relu(conv2d(h_conv3, W_conv4) + b_conv4)
-    # 畳み込み層5の作成
-    with tf.name_scope('conv5') as scope:
-        W_conv5 = weight_variable([5, 5, 64, 64])
-        b_conv5 = bias_variable([64])
-        h_conv5 = tf.nn.relu(conv2d(h_conv4, W_conv5) + b_conv5)
-    # プーリング層3の作成
-    with tf.name_scope('pool3') as scope:
-        h_pool3 = max_pool_2x2(h_conv5)
-    # 全結合層1の作成
-    with tf.name_scope('fc1') as scope:
-        W_fc1 = weight_variable([12*12*64, 1024])
-        b_fc1 = bias_variable([1024])
-        h_pool3_flat = tf.reshape(h_pool3, [-1, 7*7*64])
-        h_fc1 = tf.nn.relu(tf.matmul(h_pool3_flat, W_fc1) + b_fc1)
-        # dropoutの設定
-        h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
-    # 全結合層2の作成
-    with tf.name_scope('fc2') as scope:
-        W_fc2 = weight_variable([1024, 1024])
-        b_fc2 = bias_variable([1024])
-        h_fc2 = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
-        # dropoutの設定
-        h_fc2_drop = tf.nn.dropout(h_fc2, keep_prob)
-    # 全結合層3の作成
-    with tf.name_scope('fc3') as scope:
-        W_fc3 = weight_variable([1024, NUM_CLASSES])
-        b_fc3 = bias_variable([NUM_CLASSES])
-        y_conv = tf.matmul(h_fc2_drop, W_fc3) + b_fc3
-    # ソフトマックス関数による正規化
-    with tf.name_scope('softmax') as scope:
-        y_conv=tf.nn.softmax(tf.matmul(h_fc2_drop, W_fc3) + b_fc3)
-    # 各ラベルの確率のようなものを返す
-    return y_con
-```

修正

2018/01/15 02:53

投稿

TyoNgc

スコア14

test CHANGED Viewed

File without changes

test CHANGED Viewed

@@ -249,3 +249,201 @@
 以下略
 ```
+修正後
+```
+def inference(images_placeholder, keep_prob):
+    """ 予測モデルを作成する関数
+    引数:
+        images_placeholder: 画像のplaceholder
+        keep_prob: dropout率のplace_holder
+    返り値:
+        y_conv: 各クラスの確率(のようなもの)
+    """
+    # 重みを標準偏差0.1の正規分布で初期化
+    def weight_variable(shape):
+      initial = tf.truncated_normal(shape, stddev=0.1)
+      return tf.Variable(initial)
+    # バイアスを標準偏差0.1の正規分布で初期化
+    def bias_variable(shape):
+      initial = tf.constant(0.1, shape=shape)
+      return tf.Variable(initial)
+    # 畳み込み層の作成
+    def conv2d(x, W):
+      return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
+    # プーリング層の作成
+    def max_pool_2x2(x):
+      return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
+                            strides=[1, 2, 2, 1], padding='SAME')
+    # 入力を28x28x3に変形
+    x_image = tf.reshape(images_placeholder, [-1, IMAGE_SIZE, IMAGE_SIZE, 3])
+    # 畳み込み層1の作成
+    with tf.name_scope('conv1') as scope:
+        W_conv1 = weight_variable([5, 5, 3, 32])
+        b_conv1 = bias_variable([32])
+        h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
+    # プーリング層1の作成
+    with tf.name_scope('pool1') as scope:
+        h_pool1 = max_pool_2x2(h_conv1)
+    # 畳み込み層2の作成
+    with tf.name_scope('conv2') as scope:
+        W_conv2 = weight_variable([5, 5, 32, 64])
+        b_conv2 = bias_variable([64])
+        h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
+    # プーリング層2の作成
+    with tf.name_scope('pool2') as scope:
+        h_pool2 = max_pool_2x2(h_conv2)
+    # 畳み込み層3の作成
+    with tf.name_scope('conv3') as scope:
+        W_conv3 = weight_variable([5, 5, 64, 64])
+        b_conv3 = bias_variable([64])
+        h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3)
+    # 畳み込み層4の作成
+    with tf.name_scope('conv4') as scope:
+        W_conv4 = weight_variable([5, 5, 64, 64])
+        b_conv4 = bias_variable([64])
+        h_conv4 = tf.nn.relu(conv2d(h_conv3, W_conv4) + b_conv4)
+    # 畳み込み層5の作成
+    with tf.name_scope('conv5') as scope:
+        W_conv5 = weight_variable([5, 5, 64, 64])
+        b_conv5 = bias_variable([64])
+        h_conv5 = tf.nn.relu(conv2d(h_conv4, W_conv5) + b_conv5)
+    # プーリング層3の作成
+    with tf.name_scope('pool3') as scope:
+        h_pool3 = max_pool_2x2(h_conv5)
+    # 全結合層1の作成
+    with tf.name_scope('fc1') as scope:
+        W_fc1 = weight_variable([12*12*64, 1024])
+        b_fc1 = bias_variable([1024])
+        h_pool3_flat = tf.reshape(h_pool3, [-1, 7*7*64])
+        h_fc1 = tf.nn.relu(tf.matmul(h_pool3_flat, W_fc1) + b_fc1)
+        # dropoutの設定
+        h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
+    # 全結合層2の作成
+    with tf.name_scope('fc2') as scope:
+        W_fc2 = weight_variable([1024, 1024])
+        b_fc2 = bias_variable([1024])
+        h_fc2 = tf.nn.relu(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
+        # dropoutの設定
+        h_fc2_drop = tf.nn.dropout(h_fc2, keep_prob)
+    # 全結合層3の作成
+    with tf.name_scope('fc3') as scope:
+        W_fc3 = weight_variable([1024, NUM_CLASSES])
+        b_fc3 = bias_variable([NUM_CLASSES])
+        y_conv = tf.matmul(h_fc2_drop, W_fc3) + b_fc3
+    # ソフトマックス関数による正規化
+    with tf.name_scope('softmax') as scope:
+        y_conv=tf.nn.softmax(tf.matmul(h_fc2_drop, W_fc3) + b_fc3)
+    # 各ラベルの確率のようなものを返す
+    return y_con
+```