initialize_all_variables -> global_variables_initializer

tarabishy2020 · Jan 4, 2017 · b13cadb · b13cadb
1 parent b95c1ee
commit b13cadb
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Showing 11 changed files with 15 additions and 15 deletions.
diff --git a/GLOSSARY.md b/GLOSSARY.md
@@ -617,7 +617,7 @@ y = x * 5
 g = tf.gradients(y, x)
 
 sess = tf.InteractiveSession()
-sess.run(tf.initialize_all_variables())
+sess.run(tf.global_variables_initializer())
 g[0].eval()
 # prints 5
 ```

diff --git a/session-3/libs/vae.py b/session-3/libs/vae.py
@@ -328,7 +328,7 @@ def train_vae(files,
     # We create a session to use the graph
     sess = tf.Session()
     saver = tf.train.Saver()
-    sess.run(tf.initialize_all_variables())
+    sess.run(tf.global_variables_initializer())
 
     # This will handle our threaded image pipeline
     coord = tf.train.Coordinator()
@@ -434,7 +434,7 @@ def test_mnist():
 
     # We create a session to use the graph
     sess = tf.Session()
-    sess.run(tf.initialize_all_variables())
+    sess.run(tf.global_variables_initializer())
 
     # Fit all training data
     t_i = 0

diff --git a/session-4/libs/deepdream.py b/session-4/libs/deepdream.py
@@ -397,7 +397,7 @@ def guided_dream(input_img,
         l2_loss = l2_loss_weight * tf.reduce_mean(tf.nn.l2_loss(x))
 
         ascent = tf.gradients(feature_loss + softmax_loss + tv_loss + l2_loss, x)[0]
-        sess.run(tf.initialize_all_variables())
+        sess.run(tf.global_variables_initializer())
         imgs = []
         for it_i in range(n_iterations):
             this_res, this_feature_loss, this_softmax_loss, this_tv_loss, this_l2_loss = sess.run(

diff --git a/session-4/libs/stylenet.py b/session-4/libs/stylenet.py
@@ -142,7 +142,7 @@ def stylize(content_img, style_img, base_img=None, saveto=None, gif_step=5,
         loss = content_weight * content_loss + style_weight * style_loss
         optimizer = tf.train.AdamOptimizer(0.01).minimize(loss)
 
-        sess.run(tf.initialize_all_variables())
+        sess.run(tf.global_variables_initializer())
         imgs = []
         for it_i in range(n_iterations):
             _, this_loss, synth = sess.run(

diff --git a/session-4/libs/vae.py b/session-4/libs/vae.py
@@ -328,7 +328,7 @@ def train_vae(files,
     # We create a session to use the graph
     sess = tf.Session()
     saver = tf.train.Saver()
-    sess.run(tf.initialize_all_variables())
+    sess.run(tf.global_variables_initializer())
 
     # This will handle our threaded image pipeline
     coord = tf.train.Coordinator()
@@ -434,7 +434,7 @@ def test_mnist():
 
     # We create a session to use the graph
     sess = tf.Session()
-    sess.run(tf.initialize_all_variables())
+    sess.run(tf.global_variables_initializer())
 
     # Fit all training data
     t_i = 0

diff --git a/session-5/libs/charrnn.py b/session-5/libs/charrnn.py
@@ -107,7 +107,7 @@ def train(txt, batch_size=100, sequence_length=150, n_cells=100, n_layers=3,
                             gradient_clip=gradient_clip,
                             learning_rate=learning_rate)
 
-        init_op = tf.initialize_all_variables()
+        init_op = tf.global_variables_initializer()
         saver = tf.train.Saver()
         sess.run(init_op)
         if os.path.exists(ckpt_name):
@@ -186,7 +186,7 @@ def infer(txt, ckpt_name, n_iterations, n_cells=512, n_layers=3,
                             gradient_clip=gradient_clip,
                             learning_rate=learning_rate)
 
-        init_op = tf.initialize_all_variables()
+        init_op = tf.global_variables_initializer()
         saver = tf.train.Saver()
         sess.run(init_op)
         if os.path.exists(ckpt_name):

diff --git a/session-5/libs/deepdream.py b/session-5/libs/deepdream.py
@@ -397,7 +397,7 @@ def guided_dream(input_img,
         l2_loss = l2_loss_weight * tf.reduce_mean(tf.nn.l2_loss(x))
 
         ascent = tf.gradients(feature_loss + softmax_loss + tv_loss + l2_loss, x)[0]
-        sess.run(tf.initialize_all_variables())
+        sess.run(tf.global_variables_initializer())
         imgs = []
         for it_i in range(n_iterations):
             this_res, this_feature_loss, this_softmax_loss, this_tv_loss, this_l2_loss = sess.run(

diff --git a/session-5/libs/gan.py b/session-5/libs/gan.py
@@ -436,7 +436,7 @@ def train_ds():
     # %%
     # We create a session to use the graph
     sess = tf.Session()
-    init_op = tf.initialize_all_variables()
+    init_op = tf.global_variables_initializer()
 
     saver = tf.train.Saver()
     sums = gan['sums']

diff --git a/session-5/libs/stylenet.py b/session-5/libs/stylenet.py
@@ -142,7 +142,7 @@ def stylize(content_img, style_img, base_img=None, saveto=None, gif_step=5,
         loss = content_weight * content_loss + style_weight * style_loss
         optimizer = tf.train.AdamOptimizer(0.01).minimize(loss)
 
-        sess.run(tf.initialize_all_variables())
+        sess.run(tf.global_variables_initializer())
         imgs = []
         for it_i in range(n_iterations):
             _, this_loss, synth = sess.run(

diff --git a/session-5/libs/vae.py b/session-5/libs/vae.py
@@ -328,7 +328,7 @@ def train_vae(files,
     # We create a session to use the graph
     sess = tf.Session()
     saver = tf.train.Saver()
-    sess.run(tf.initialize_all_variables())
+    sess.run(tf.global_variables_initializer())
 
     # This will handle our threaded image pipeline
     coord = tf.train.Coordinator()
@@ -434,7 +434,7 @@ def test_mnist():
 
     # We create a session to use the graph
     sess = tf.Session()
-    sess.run(tf.initialize_all_variables())
+    sess.run(tf.global_variables_initializer())
 
     # Fit all training data
     t_i = 0

diff --git a/session-5/libs/vaegan.py b/session-5/libs/vaegan.py
@@ -531,7 +531,7 @@ def train_vaegan(files,
 
     sess = tf.Session()
     saver = tf.train.Saver()
-    sess.run(tf.initialize_all_variables())
+    sess.run(tf.global_variables_initializer())
     coord = tf.train.Coordinator()
     tf.get_default_graph().finalize()
     threads = tf.train.start_queue_runners(sess=sess, coord=coord)