Update utils.py (#819)

* Update utils.py

update fit function replace tensorboard with tensorboard_dir parameter to enable customized log folder

* Update utils.py

* Update utils.py

* Update CHANGELOG.md

* Update CHANGELOG.md

* Update CHANGELOG.md

* Update CHANGELOG.md

Author: ndiy

CHANGELOG.md

@@ -72,6 +72,7 @@ To release a new version, please update the changelog as followed:
 ### Added
 
 ### Changed
+ - remove 'tensorboard' param, replaced by 'tensorboard_dir' in `tensorlayer/utils.py` with customizable tensorboard directory (PR #819)
 
 ### Deprecated
 
@@ -86,6 +87,7 @@ To release a new version, please update the changelog as followed:
 - pytest-cov>=2.5,<2.6 => pytest-cov>=2.5,<2.7 (PR #820)
 
 ### Contributors
+- @ndiy: #819 
 
 - @DEKHTIARJonathan: #815 #820
 

tensorlayer/utils.py

@@ -41,7 +41,7 @@
 
 def fit(
         sess, network, train_op, cost, X_train, y_train, x, y_, acc=None, batch_size=100, n_epoch=100, print_freq=5,
-        X_val=None, y_val=None, eval_train=True, tensorboard=False, tensorboard_epoch_freq=5,
+        X_val=None, y_val=None, eval_train=True, tensorboard_dir=None, tensorboard_epoch_freq=5,
         tensorboard_weight_histograms=True, tensorboard_graph_vis=True
 ):
     """Training a given non time-series network by the given cost function, training data, batch_size, n_epoch etc.
@@ -80,9 +80,8 @@ def fit(
     eval_train : boolean
         Whether to evaluate the model during training.
         If X_val and y_val are not None, it reflects whether to evaluate the model on training data.
-    tensorboard : boolean
-        If True, summary data will be stored to the log/ directory for visualization with tensorboard.
-        See also detailed tensorboard_X settings for specific configurations of features. (default False)
+    tensorboard_dir : string
+        path to log dir, if set, summary data will be stored to the tensorboard_dir/ directory for visualization with tensorboard. (default None)
         Also runs `tl.layers.initialize_global_variables(sess)` internally in fit() to setup the summary nodes.
     tensorboard_epoch_freq : int
         How many epochs between storing tensorboard checkpoint for visualization to log/ directory (default 5).
@@ -106,27 +105,27 @@ def fit(
 
     Notes
     --------
-    If tensorboard=True, the `global_variables_initializer` will be run inside the fit function
+    If tensorboard_dir not None, the `global_variables_initializer` will be run inside the fit function
     in order to initialize the automatically generated summary nodes used for tensorboard visualization,
     thus `tf.global_variables_initializer().run()` before the `fit()` call will be undefined.
 
     """
     if X_train.shape[0] < batch_size:
         raise AssertionError("Number of training examples should be bigger than the batch size")
 
-    if (tensorboard):
+    if tensorboard_dir is not None:
         tl.logging.info("Setting up tensorboard ...")
         #Set up tensorboard summaries and saver
-        tl.files.exists_or_mkdir('logs/')
+        tl.files.exists_or_mkdir(tensorboard_dir)
 
         #Only write summaries for more recent TensorFlow versions
         if hasattr(tf, 'summary') and hasattr(tf.summary, 'FileWriter'):
             if tensorboard_graph_vis:
-                train_writer = tf.summary.FileWriter('logs/train', sess.graph)
-                val_writer = tf.summary.FileWriter('logs/validation', sess.graph)
+                train_writer = tf.summary.FileWriter(tensorboard_dir + '/train', sess.graph)
+                val_writer = tf.summary.FileWriter(tensorboard_dir + '/validation', sess.graph)
             else:
-                train_writer = tf.summary.FileWriter('logs/train')
-                val_writer = tf.summary.FileWriter('logs/validation')
+                train_writer = tf.summary.FileWriter(tensorboard_dir + '/train')
+                val_writer = tf.summary.FileWriter(tensorboard_dir + '/validation')
 
         #Set up summary nodes
         if (tensorboard_weight_histograms):
@@ -142,7 +141,7 @@ def fit(
 
         #Initalize all variables and summaries
         tl.layers.initialize_global_variables(sess)
-        tl.logging.info("Finished! use $tensorboard --logdir=logs/ to start server")
+        tl.logging.info("Finished! use `tensorboard --logdir=%s/` to start tensorboard" % tensorboard_dir)
 
     tl.logging.info("Start training the network ...")
     start_time_begin = time.time()
@@ -159,7 +158,7 @@ def fit(
             n_step += 1
         loss_ep = loss_ep / n_step
 
-        if tensorboard and hasattr(tf, 'summary'):
+        if tensorboard_dir is not None and hasattr(tf, 'summary'):
             if epoch + 1 == 1 or (epoch + 1) % tensorboard_epoch_freq == 0:
                 for X_train_a, y_train_a in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
                     dp_dict = dict_to_one(network.all_drop)  # disable noise layers