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Commit 909a8b7

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패키지들 최신버전으로 업데이트 (Check README.md)
1 parent 00d4418 commit 909a8b7

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11 files changed

+15
-15
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11 files changed

+15
-15
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‎04 - Neural Network Basic/02 - Deep NN.py

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

‎05 - TensorBoard, Saver/01 - Saver.py

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

‎05 - TensorBoard, Saver/02 - TensorBoard.py

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

‎05 - TensorBoard, Saver/03 - TensorBoard2.py

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

‎06 - MNIST/01 - MNIST.py

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

‎06 - MNIST/02 - Dropout.py

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

‎07 - CNN/01 - CNN.py

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

‎07 - CNN/02 - tf.layers.py

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

‎10 - RNN/01 - MNIST.py

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

‎11 - Inception/retrain.py

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tf.summary.histogram('activations', final_tensor)
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with tf.name_scope('cross_entropy'):
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cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
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cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(
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labels=ground_truth_input, logits=logits)
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with tf.name_scope('total'):
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cross_entropy_mean = tf.reduce_mean(cross_entropy)

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