Update to 1.0.0

mingxuan-yi · mingxuan-yi · commit 35aa1ae51725 · 2017-02-16T18:55:17.000+08:00
diff --git a/Deep_Learning_with_TensorFlow/0.12.0/Chapter10/4. &#24322;&#27493;&#26356;&#26032;&#27169;&#24335;&#26679;&#20363;&#31243;&#24207;.py b/Deep_Learning_with_TensorFlow/0.12.0/Chapter10/4. &#24322;&#27493;&#26356;&#26032;&#27169;&#24335;&#26679;&#20363;&#31243;&#24207;.py
@@ -15,7 +15,7 @@
 
 # &#27169;&#22411;&#20445;&#23384;&#30340;&#36335;&#24452;&#21644;&#25991;&#20214;&#21517;&#12290;
 MODEL_SAVE_PATH = "logs/log_async"
-DATA_PATH = "../../Data_sets/MNIST_data"
+DATA_PATH = "../../datasets/MNIST_data"
 
 FLAGS = tf.app.flags.FLAGS
 
@@ -40,7 +40,7 @@ def build_model(x, y_, is_chief):
     global_step = tf.Variable(0, trainable=False)
 
     # &#35745;&#31639;&#25439;&#22833;&#20989;&#25968;&#24182;&#23450;&#20041;&#21453;&#21521;&#20256;&#25773;&#36807;&#31243;&#12290;
-    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(y, tf.argmax(y_, 1))
+    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
     cross_entropy_mean = tf.reduce_mean(cross_entropy)
     loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))
     learning_rate = tf.train.exponential_decay(
@@ -89,22 +89,20 @@ def main(argv=None):
         summary_op = tf.summary.merge_all()
         # &#23450;&#20041;&#21464;&#37327;&#21021;&#22987;&#21270;&#25805;&#20316;&#12290;
         init_op = tf.global_variables_initializer()
-        summary_writer = tf.summary.FileWriter
         # &#36890;&#36807;tf.train.Supervisor&#31649;&#29702;&#35757;&#32451;&#28145;&#24230;&#23398;&#20064;&#27169;&#22411;&#26102;&#30340;&#36890;&#29992;&#21151;&#33021;&#12290;
         sv = tf.train.Supervisor(
             is_chief=is_chief,
             logdir=MODEL_SAVE_PATH,
             init_op=init_op,
             summary_op=summary_op,
             saver=saver,
-            summary_writer=tf.summary.FileWriter
             global_step=global_step,
             save_model_secs=60,
             save_summaries_secs=60)
 
         sess_config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
         # &#36890;&#36807;tf.train.Supervisor&#29983;&#25104;&#20250;&#35805;&#12290;
-        sess = sv.prepare_or_wait_for_session(server.target, config=sess_config) ###stucked####
+        sess = sv.prepare_or_wait_for_session(server.target, config=sess_config)
 
         step = 0
         start_time = time.time()
diff --git a/Deep_Learning_with_TensorFlow/0.12.0/Chapter10/5. &#21516;&#27493;&#26356;&#26032;&#27169;&#24335;&#26679;&#20363;&#31243;&#24207;.py b/Deep_Learning_with_TensorFlow/0.12.0/Chapter10/5. &#21516;&#27493;&#26356;&#26032;&#27169;&#24335;&#26679;&#20363;&#31243;&#24207;.py
@@ -13,7 +13,7 @@
 TRAINING_STEPS = 10000
 MOVING_AVERAGE_DECAY = 0.99 
 MODEL_SAVE_PATH = "logs/log_sync"
-DATA_PATH = "../../Data_sets/MNIST_data"
+DATA_PATH = "../../datasets/MNIST_data"
 
 
 # &#21644;&#24322;&#27493;&#27169;&#24335;&#31867;&#20284;&#30340;&#35774;&#32622;flags&#12290;
diff --git a/Deep_Learning_with_TensorFlow/0.12.1/Chapter10/4. &#24322;&#27493;&#26356;&#26032;&#27169;&#24335;&#26679;&#20363;&#31243;&#24207;.py b/Deep_Learning_with_TensorFlow/0.12.1/Chapter10/4. &#24322;&#27493;&#26356;&#26032;&#27169;&#24335;&#26679;&#20363;&#31243;&#24207;.py
@@ -15,7 +15,7 @@
 
 # &#27169;&#22411;&#20445;&#23384;&#30340;&#36335;&#24452;&#21644;&#25991;&#20214;&#21517;&#12290;
 MODEL_SAVE_PATH = "logs/log_async"
-DATA_PATH = "../../Datasets/MNIST_data"
+DATA_PATH = "../../datasets/MNIST_data"
 
 FLAGS = tf.app.flags.FLAGS
 
@@ -40,7 +40,7 @@ def build_model(x, y_, is_chief):
     global_step = tf.Variable(0, trainable=False)
 
     # &#35745;&#31639;&#25439;&#22833;&#20989;&#25968;&#24182;&#23450;&#20041;&#21453;&#21521;&#20256;&#25773;&#36807;&#31243;&#12290;
-    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(y, tf.argmax(y_, 1))
+    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
     cross_entropy_mean = tf.reduce_mean(cross_entropy)
     loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))
     learning_rate = tf.train.exponential_decay(
@@ -89,22 +89,20 @@ def main(argv=None):
         summary_op = tf.summary.merge_all()
         # &#23450;&#20041;&#21464;&#37327;&#21021;&#22987;&#21270;&#25805;&#20316;&#12290;
         init_op = tf.global_variables_initializer()
-        summary_writer = tf.summary.FileWriter
         # &#36890;&#36807;tf.train.Supervisor&#31649;&#29702;&#35757;&#32451;&#28145;&#24230;&#23398;&#20064;&#27169;&#22411;&#26102;&#30340;&#36890;&#29992;&#21151;&#33021;&#12290;
         sv = tf.train.Supervisor(
             is_chief=is_chief,
             logdir=MODEL_SAVE_PATH,
             init_op=init_op,
             summary_op=summary_op,
             saver=saver,
-            summary_writer=tf.summary.FileWriter
             global_step=global_step,
             save_model_secs=60,
             save_summaries_secs=60)
 
         sess_config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
         # &#36890;&#36807;tf.train.Supervisor&#29983;&#25104;&#20250;&#35805;&#12290;
-        sess = sv.prepare_or_wait_for_session(server.target, config=sess_config) ###stucked####
+        sess = sv.prepare_or_wait_for_session(server.target, config=sess_config)
 
         step = 0
         start_time = time.time()
diff --git a/Deep_Learning_with_TensorFlow/0.12.1/Chapter10/5. &#21516;&#27493;&#26356;&#26032;&#27169;&#24335;&#26679;&#20363;&#31243;&#24207;.py b/Deep_Learning_with_TensorFlow/0.12.1/Chapter10/5. &#21516;&#27493;&#26356;&#26032;&#27169;&#24335;&#26679;&#20363;&#31243;&#24207;.py
@@ -13,7 +13,7 @@
 TRAINING_STEPS = 10000
 MOVING_AVERAGE_DECAY = 0.99 
 MODEL_SAVE_PATH = "logs/log_sync"
-DATA_PATH = "../../Datasets/MNIST_data"
+DATA_PATH = "../../datasets/MNIST_data"
 
 
 # &#21644;&#24322;&#27493;&#27169;&#24335;&#31867;&#20284;&#30340;&#35774;&#32622;flags&#12290;
diff --git a/Deep_Learning_with_TensorFlow/1.0.0/Chapter10/4. &#24322;&#27493;&#26356;&#26032;&#27169;&#24335;&#26679;&#20363;&#31243;&#24207;.py b/Deep_Learning_with_TensorFlow/1.0.0/Chapter10/4. &#24322;&#27493;&#26356;&#26032;&#27169;&#24335;&#26679;&#20363;&#31243;&#24207;.py
@@ -14,8 +14,8 @@
 MOVING_AVERAGE_DECAY = 0.99
 
 # &#27169;&#22411;&#20445;&#23384;&#30340;&#36335;&#24452;&#21644;&#25991;&#20214;&#21517;&#12290;
-MODEL_SAVE_PATH = "/Users/ymx/Desktop/MNIST_data/log"
-DATA_PATH = "/Users/ymx/Desktop/MNIST_data/data"
+MODEL_SAVE_PATH = "logs/log_async"
+DATA_PATH = "../../datasets/MNIST_data"
 
 FLAGS = tf.app.flags.FLAGS
 
@@ -40,7 +40,7 @@ def build_model(x, y_, is_chief):
     global_step = tf.Variable(0, trainable=False)
 
     # &#35745;&#31639;&#25439;&#22833;&#20989;&#25968;&#24182;&#23450;&#20041;&#21453;&#21521;&#20256;&#25773;&#36807;&#31243;&#12290;
-    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(y, tf.argmax(y_, 1))
+    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
     cross_entropy_mean = tf.reduce_mean(cross_entropy)
     loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))
     learning_rate = tf.train.exponential_decay(
@@ -89,22 +89,20 @@ def main(argv=None):
         summary_op = tf.summary.merge_all()
         # &#23450;&#20041;&#21464;&#37327;&#21021;&#22987;&#21270;&#25805;&#20316;&#12290;
         init_op = tf.global_variables_initializer()
-        summary_writer = tf.summary.FileWriter
         # &#36890;&#36807;tf.train.Supervisor&#31649;&#29702;&#35757;&#32451;&#28145;&#24230;&#23398;&#20064;&#27169;&#22411;&#26102;&#30340;&#36890;&#29992;&#21151;&#33021;&#12290;
         sv = tf.train.Supervisor(
             is_chief=is_chief,
             logdir=MODEL_SAVE_PATH,
             init_op=init_op,
             summary_op=summary_op,
             saver=saver,
-            summary_writer=tf.summary.FileWriter
             global_step=global_step,
             save_model_secs=60,
             save_summaries_secs=60)
 
         sess_config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)
         # &#36890;&#36807;tf.train.Supervisor&#29983;&#25104;&#20250;&#35805;&#12290;
-        sess = sv.prepare_or_wait_for_session(server.target, config=sess_config) ###stucked####
+        sess = sv.prepare_or_wait_for_session(server.target, config=sess_config)
 
         step = 0
         start_time = time.time()
diff --git a/Deep_Learning_with_TensorFlow/1.0.0/Chapter10/5. &#21516;&#27493;&#26356;&#26032;&#27169;&#24335;&#26679;&#20363;&#31243;&#24207;.py b/Deep_Learning_with_TensorFlow/1.0.0/Chapter10/5. &#21516;&#27493;&#26356;&#26032;&#27169;&#24335;&#26679;&#20363;&#31243;&#24207;.py
@@ -12,8 +12,8 @@
 REGULARAZTION_RATE = 0.0001
 TRAINING_STEPS = 10000
 MOVING_AVERAGE_DECAY = 0.99 
-MODEL_SAVE_PATH = "/Users/ymx/Desktop/MNIST_data/log2"
-DATA_PATH = "/Users/ymx/Desktop/MNIST_data/data"
+MODEL_SAVE_PATH = "logs/log_sync"
+DATA_PATH = "../../datasets/MNIST_data"
 
 
 # &#21644;&#24322;&#27493;&#27169;&#24335;&#31867;&#20284;&#30340;&#35774;&#32622;flags&#12290;
@@ -38,7 +38,7 @@ def build_model(x, y_, n_workers, is_chief):
     variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
     variables_averages_op = variable_averages.apply(tf.trainable_variables())
 
-    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(y, tf.argmax(y_, 1))
+    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
     cross_entropy_mean = tf.reduce_mean(cross_entropy)
     loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))
     learning_rate = tf.train.exponential_decay(
@@ -48,8 +48,7 @@ def build_model(x, y_, n_workers, is_chief):
     opt = tf.train.SyncReplicasOptimizer(
         tf.train.GradientDescentOptimizer(learning_rate),
         replicas_to_aggregate=n_workers,
-        total_num_replicas=n_workers,
-        replica_id=FLAGS.task_id)
+        total_num_replicas=n_workers)
 
     train_op = opt.minimize(loss, global_step=global_step)     
     if is_chief:
