Refactor for tensorflow1.0-rc1

workshop_sections/extras/lstm_text_classification/trainer/model.py

@@ -87,7 +87,7 @@ def partitioner(shape, **unused_args):
     )
 
     # Shape [sentence_length, batch_size]
-    outputs_concat = tf.squeeze(tf.pack(outputs))
+    outputs_concat = tf.squeeze(tf.stack(outputs))
 
     # Shape [batch_size]
     predictions = tf.reduce_mean(

workshop_sections/mnist_series/mnist_cnn/mnist_cnn.py

@@ -104,7 +104,7 @@ def max_pool_2x2(x):
 loss_summary = tf.scalar_summary("loss", cross_entropy)
 train_summary_op = tf.merge_summary([loss_summary])
 
-sess.run(tf.initialize_all_variables())
+sess.run(tf.global_variables_initializer())
 
 # Create a saver for writing training checkpoints.
 saver = tf.train.Saver()

workshop_sections/mnist_series/mnist_simple.ipynb

@@ -146,7 +146,7 @@
    "outputs": [],
    "source": [
     "NUM_STEPS = 10000\n",
-    "init = tf.initialize_all_variables()\n",
+    "init = tf.global_variables_initializer()\n",
     "sess = tf.Session()\n",
     "sess.run(init)"
    ]

workshop_sections/mnist_series/mnist_simple.py

@@ -57,7 +57,7 @@ def main(_):
     train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
 
     sess = tf.InteractiveSession()
-    tf.initialize_all_variables().run()
+    tf.global_variables_initializer().run()
     # Train
     print("training for %s steps" % FLAGS.num_steps)
     for _ in xrange(FLAGS.num_steps):

workshop_sections/mnist_series/mnist_tflearn.ipynb

@@ -194,7 +194,7 @@
    },
    "outputs": [],
    "source": [
-    "print(\"Running DNN classifier with .1 learning rate...\")\n",
+    "print(\"Running DNN classifier with .5 learning rate...\")\n",
     "classifier = define_and_run_dnn_classifier(5000, getNewPath(), lr=.5)"
    ]
   },

workshop_sections/mnist_series/the_hard_way/mnist_hidden.py

@@ -161,7 +161,7 @@ def main(_):
         train_summary_op = tf.merge_summary([loss_summary, acc_summary])
 
         # Add the variable initializer Op.
-        init = tf.initialize_all_variables()
+        init = tf.global_variables_initializer()
 
         # Create a saver for writing training checkpoints.
         saver = tf.train.Saver()

workshop_sections/mnist_series/the_hard_way/mnist_onehlayer.ipynb

@@ -214,7 +214,7 @@
     "        train_summary_op = tf.merge_summary([loss_summary])\n",
     "\n",
     "        # Add the variable initializer Op.\n",
-    "        init = tf.initialize_all_variables()\n",
+    "        init = tf.global_variables_initializer()\n",
     "\n",
     "        # Create a saver for writing training checkpoints.\n",
     "        saver = tf.train.Saver()\n",

workshop_sections/mnist_series/the_hard_way/mnist_onehlayer.py

@@ -140,7 +140,7 @@ def main(_):
         train_summary_op = tf.merge_summary([loss_summary])
 
         # Add the variable initializer Op.
-        init = tf.initialize_all_variables()
+        init = tf.global_variables_initializer()
 
         # Create a saver for writing training checkpoints.
         saver = tf.train.Saver()

workshop_sections/starter_tf_graph/tf_matrix_mul.ipynb

@@ -53,7 +53,7 @@
     "m3 = tf.Print(m3, [m3], message=\"m3 is: \")\n",
     "\n",
     "# Add variable initializer.\n",
-    "init = tf.initialize_all_variables()\n"
+    "init = tf.global_variables_initializer()\n"
    ]
   },
   {

workshop_sections/starter_tf_graph/tf_matrix_mul.py

@@ -33,7 +33,7 @@
 m3 = tf.Print(m3, [m3], message="m3 is: ")
 
 # Add variable initializer.
-init = tf.initialize_all_variables()  # global_variables_initializer() in TF0.12
+init = tf.global_variables_initializer()  # global_variables_initializer() in TF0.12
 
 with tf.Session() as session:
     # We must initialize all variables before we use them.

workshop_sections/starter_tf_graph/tf_matrix_mul_add.py

@@ -36,7 +36,7 @@
 # m4 = m3 + m3
 
 # Add variable initializer.
-init = tf.initialize_all_variables()
+init = tf.global_variables_initializer()
 
 with tf.Session() as session:
     # We must initialize all variables before we use them.

workshop_sections/transfer_learning/cloudml/trainer/model.py

@@ -195,8 +195,8 @@ def decode_and_resize(image_str_tensor):
     image = tf.image.convert_image_dtype(image, dtype=tf.float32)
 
     # Then shift images to [-1, 1) for Inception.
-    image = tf.sub(image, 0.5)
-    image = tf.mul(image, 2.0)
+    image = tf.subtract(image, 0.5)
+    image = tf.multiply(image, 2.0)
 
     # Build Inception layers, which expect A tensor of type float from [-1, 1)
     # and shape [batch_size, height, width, channels].

workshop_sections/transfer_learning/cloudml/trainer/preprocess.py

@@ -178,7 +178,7 @@ def __init__(self, tf_session):
     # input_jpeg is the tensor that contains raw image bytes.
     # It is used to feed image bytes and obtain embeddings.
     self.input_jpeg, self.embedding = self.build_graph()
-    self.tf_session.run(tf.initialize_all_variables())
+    self.tf_session.run(tf.global_variables_initializer())
     self.restore_from_checkpoint(Default.IMAGE_GRAPH_CHECKPOINT_URI)
 
   def build_graph(self):
@@ -212,8 +212,8 @@ def build_graph(self):
         image, [self.HEIGHT, self.WIDTH], align_corners=False)
 
     # Then rescale range to [-1, 1) for Inception.
-    image = tf.sub(image, 0.5)
-    inception_input = tf.mul(image, 2.0)
+    image = tf.subtract(image, 0.5)
+    inception_input = tf.multiply(image, 2.0)
 
     # Build Inception layers, which expect a tensor of type float from [-1, 1)
     # and shape [batch_size, height, width, channels].