패키지들 최신버전으로 업데이트 (Check README.md)

Author: golbin

Diff for: 04 - Neural Network Basic/02 - Deep NN.py

@@ -44,7 +44,7 @@
 # 텐서플로우에서 기본적으로 제공되는 크로스 엔트로피 함수를 이용해
 # 복잡한 수식을 사용하지 않고도 최적화를 위한 비용 함수를 다음처럼 간단하게 적용할 수 있습니다.
 cost = tf.reduce_mean(
-    tf.nn.softmax_cross_entropy_with_logits(labels=Y, logits=model))
+    tf.nn.softmax_cross_entropy_with_logits_v2(labels=Y, logits=model))
 
 optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
 train_op = optimizer.minimize(cost)

Diff for: 05 - TensorBoard, Saver/01 - Saver.py

@@ -33,7 +33,7 @@
 model = tf.matmul(L2, W3)
 
 cost = tf.reduce_mean(
-    tf.nn.softmax_cross_entropy_with_logits(labels=Y, logits=model))
+    tf.nn.softmax_cross_entropy_with_logits_v2(labels=Y, logits=model))
 
 optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
 # global_step로 넘겨준 변수를, 학습용 변수들을 최적화 할 때 마다 학습 횟수를 하나씩 증가시킵니다.

Diff for: 05 - TensorBoard, Saver/02 - TensorBoard.py

@@ -34,7 +34,7 @@
 
 with tf.name_scope('optimizer'):
     cost = tf.reduce_mean(
-        tf.nn.softmax_cross_entropy_with_logits(labels=Y, logits=model))
+        tf.nn.softmax_cross_entropy_with_logits_v2(labels=Y, logits=model))
 
     optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
     train_op = optimizer.minimize(cost, global_step=global_step)

Diff for: 05 - TensorBoard, Saver/03 - TensorBoard2.py

@@ -37,7 +37,7 @@
 
 with tf.name_scope('optimizer'):
     cost = tf.reduce_mean(
-        tf.nn.softmax_cross_entropy_with_logits(labels=Y, logits=model))
+        tf.nn.softmax_cross_entropy_with_logits_v2(labels=Y, logits=model))
 
     optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
     train_op = optimizer.minimize(cost, global_step=global_step)

Diff for: 06 - MNIST/01 - MNIST.py

@@ -32,7 +32,7 @@
 # 최종 모델의 출력값은 W3 변수를 곱해 10개의 분류를 가지게 됩니다.
 model = tf.matmul(L2, W3)
 
-cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=model, labels=Y))
+cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=model, labels=Y))
 optimizer = tf.train.AdamOptimizer(0.001).minimize(cost)
 
 #########

Diff for: 06 - MNIST/02 - Dropout.py

@@ -26,7 +26,7 @@
 W3 = tf.Variable(tf.random_normal([256, 10], stddev=0.01))
 model = tf.matmul(L2, W3)
 
-cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=model, labels=Y))
+cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=model, labels=Y))
 optimizer = tf.train.AdamOptimizer(0.001).minimize(cost)
 
 #########

Diff for: 07 - CNN/01 - CNN.py

@@ -48,7 +48,7 @@
 W4 = tf.Variable(tf.random_normal([256, 10], stddev=0.01))
 model = tf.matmul(L3, W4)
 
-cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=model, labels=Y))
+cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=model, labels=Y))
 optimizer = tf.train.AdamOptimizer(0.001).minimize(cost)
 # 최적화 함수를 RMSPropOptimizer 로 바꿔서 결과를 확인해봅시다.
 # optimizer = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)

Diff for: 07 - CNN/02 - tf.layers.py

@@ -32,7 +32,7 @@
 
 model = tf.layers.dense(L3, 10, activation=None)
 
-cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=model, labels=Y))
+cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=model, labels=Y))
 optimizer = tf.train.AdamOptimizer(0.001).minimize(cost)
 
 #########

Diff for: 10 - RNN/01 - MNIST.py

@@ -54,7 +54,7 @@
 outputs = outputs[-1]
 model = tf.matmul(outputs, W) + b
 
-cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=model, labels=Y))
+cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=model, labels=Y))
 optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
 
 #########

Diff for: 11 - Inception/retrain.py

@@ -722,7 +722,7 @@ def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor):
     tf.summary.histogram('activations', final_tensor)
 
     with tf.name_scope('cross_entropy'):
-        cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
+        cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(
             labels=ground_truth_input, logits=logits)
         with tf.name_scope('total'):
             cross_entropy_mean = tf.reduce_mean(cross_entropy)

Diff for: README.md

@@ -14,11 +14,11 @@
 
 ## 요구사항
 
-- TensorFlow >= 1.2
-- Python >= 3.6
-    - numpy >= 1.12
-    - matplotlib >= 2.0
-    - pillow >= 4.1
+- TensorFlow >= 1.8.0
+- Python >= 3.6.1
+    - numpy >= 1.14.3
+    - matplotlib >= 2.2.2
+    - pillow >= 5.1
 
 ## 골빈해커의 3분 딥러닝