added graph reset to the fit() method so that subsequent calls to fit…

…() doesn't throw errors

command_line/run_stacked_autoencoder_unsupervised.py

@@ -154,7 +154,7 @@ def load_from_np(dataset_path):
         finetune_opt=FLAGS.finetune_opt, finetune_batch_size=FLAGS.finetune_batch_size,
         finetune_dropout=FLAGS.finetune_dropout,
         dae_enc_act_func=dae_enc_act_func, dae_dec_act_func=dae_dec_act_func,
-        dae_corr_type=FLAGS.dae_corr_type, dae_corr_frac=FLAGS.dae_corr_frac, dae_l2reg=FLAGS.dae_l2reg,
+        dae_corr_type=dae_corr_type, dae_corr_frac=dae_corr_frac, dae_l2reg=dae_l2reg,
         dataset=FLAGS.dataset, dae_loss_func=dae_loss_func, main_dir=FLAGS.main_dir,
         dae_opt=dae_opt, tied_weights=FLAGS.tied_weights,
         dae_learning_rate=dae_learning_rate, momentum=FLAGS.momentum, verbose=FLAGS.verbose,

yadlt/models/autoencoder_models/denoising_autoencoder.py

@@ -1,3 +1,4 @@
+from tensorflow.python.framework import ops
 import tensorflow as tf
 import numpy as np
 import os
@@ -59,6 +60,8 @@ def fit(self, train_set, validation_set=None, restore_previous_model=False):
         """
 
         with tf.Session() as self.tf_session:
+            # Reset tensorflow's default graph
+            ops.reset_default_graph()
             self._initialize_tf_utilities_and_ops(restore_previous_model)
             self._train_model(train_set, validation_set)
             self.tf_saver.save(self.tf_session, self.model_path)

yadlt/models/autoencoder_models/stacked_deep_autoencoder.py

@@ -145,6 +145,8 @@ def fit(self, train_set, train_ref, validation_set=None, validation_ref=None, re
         print('Starting Reconstruction finetuning...')
 
         with tf.Session() as self.tf_session:
+            # Reset tensorflow's default graph
+            ops.reset_default_graph()
             self.build_model(train_set.shape[1])
             self._initialize_tf_utilities_and_ops(restore_previous_model)
             self._train_model(train_set, train_ref, validation_set, validation_ref)

yadlt/models/autoencoder_models/stacked_denoising_autoencoder.py

@@ -143,6 +143,8 @@ def fit(self, train_set, train_labels, validation_set=None, validation_labels=No
         print('Starting Supervised finetuning...')
 
         with tf.Session() as self.tf_session:
+            # Reset tensorflow's default graph
+            ops.reset_default_graph()
             self.build_model(train_set.shape[1], train_labels.shape[1])
             self._initialize_tf_utilities_and_ops(restore_previous_model)
             self._train_model(train_set, train_labels, validation_set, validation_labels)

yadlt/models/convolutional_models/conv_net.py

@@ -1,3 +1,4 @@
+from tensorflow.python.framework import ops
 import tensorflow as tf
 import numpy as np
 
@@ -58,6 +59,8 @@ def fit(self, train_set, train_labels, original_shape, validation_set=None, vali
         print('Starting training...')
 
         with tf.Session() as self.tf_session:
+            # Reset tensorflow's default graph
+            ops.reset_default_graph()
             self.build_model(train_set.shape[1], train_labels.shape[1], original_shape)
             self._initialize_tf_utilities_and_ops(restore_previous_model)
             self._train_model(train_set, train_labels, validation_set, validation_labels)

yadlt/models/misc_models/logistic_regression.py

@@ -1,3 +1,4 @@
+from tensorflow.python.framework import ops
 import tensorflow as tf
 import numpy as np
 
@@ -86,6 +87,8 @@ def fit(self, train_set, train_labels, validation_set=None, validation_labels=No
         """
 
         with tf.Session() as self.tf_session:
+            # Reset tensorflow's default graph
+            ops.reset_default_graph()
             self.build_model(train_set.shape[1], train_labels.shape[1])
             self._initialize_tf_utilities_and_ops(restore_previous_model)
             self._train_model(train_set, train_labels, validation_set, validation_labels)

yadlt/models/rbm_models/dbn.py

@@ -136,6 +136,8 @@ def fit(self, train_set, train_labels, validation_set=None, validation_labels=No
         print('Starting Supervised finetuning...')
 
         with tf.Session() as self.tf_session:
+            # Reset tensorflow's default graph
+            ops.reset_default_graph()
             self.build_model(train_set.shape[1], train_labels.shape[1])
             self._initialize_tf_utilities_and_ops(restore_previous_model)
             self._train_model(train_set, train_labels, validation_set, validation_labels)

yadlt/models/rbm_models/deep_autoencoder.py

@@ -145,6 +145,8 @@ def fit(self, train_set, train_ref, validation_set=None, validation_ref=None, re
         print('Starting Reconstruction finetuning...')
 
         with tf.Session() as self.tf_session:
+            # Reset tensorflow's default graph
+            ops.reset_default_graph()
             self.build_model(train_set.shape[1])
             self._initialize_tf_utilities_and_ops(restore_previous_model)
             self._train_model(train_set, train_ref, validation_set, validation_ref)

yadlt/models/rbm_models/rbm.py

@@ -1,3 +1,4 @@
+from tensorflow.python.framework import ops
 import tensorflow as tf
 import numpy as np
 
@@ -59,6 +60,8 @@ def fit(self, train_set, validation_set=None, restore_previous_model=False):
         """
 
         with tf.Session() as self.tf_session:
+            # Reset tensorflow's default graph
+            ops.reset_default_graph()
             self.build_model(train_set.shape[1])
             self._initialize_tf_utilities_and_ops(restore_previous_model)
             self._train_model(train_set, validation_set)