diff --git a/workshop_sections/extras/lstm_text_classification/trainer/model.py b/workshop_sections/extras/lstm_text_classification/trainer/model.py index 8a92dfb..2389114 100644 --- a/workshop_sections/extras/lstm_text_classification/trainer/model.py +++ b/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( diff --git a/workshop_sections/mnist_series/mnist_cnn/mnist_cnn.py b/workshop_sections/mnist_series/mnist_cnn/mnist_cnn.py index bdb4beb..f79bdb8 100644 --- a/workshop_sections/mnist_series/mnist_cnn/mnist_cnn.py +++ b/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() diff --git a/workshop_sections/mnist_series/mnist_simple.ipynb b/workshop_sections/mnist_series/mnist_simple.ipynb index dda2409..16dad0b 100644 --- a/workshop_sections/mnist_series/mnist_simple.ipynb +++ b/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)" ] diff --git a/workshop_sections/mnist_series/mnist_simple.py b/workshop_sections/mnist_series/mnist_simple.py index f0d3aeb..eb59abd 100644 --- a/workshop_sections/mnist_series/mnist_simple.py +++ b/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): diff --git a/workshop_sections/mnist_series/mnist_tflearn.ipynb b/workshop_sections/mnist_series/mnist_tflearn.ipynb index 584f972..ed99c3f 100644 --- a/workshop_sections/mnist_series/mnist_tflearn.ipynb +++ b/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)" ] }, diff --git a/workshop_sections/mnist_series/the_hard_way/mnist_hidden.py b/workshop_sections/mnist_series/the_hard_way/mnist_hidden.py index b907a8a..f7ad390 100644 --- a/workshop_sections/mnist_series/the_hard_way/mnist_hidden.py +++ b/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() diff --git a/workshop_sections/mnist_series/the_hard_way/mnist_onehlayer.ipynb b/workshop_sections/mnist_series/the_hard_way/mnist_onehlayer.ipynb index 4925dcc..9b97135 100644 --- a/workshop_sections/mnist_series/the_hard_way/mnist_onehlayer.ipynb +++ b/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", diff --git a/workshop_sections/mnist_series/the_hard_way/mnist_onehlayer.py b/workshop_sections/mnist_series/the_hard_way/mnist_onehlayer.py index bc2c878..c6c4e3e 100644 --- a/workshop_sections/mnist_series/the_hard_way/mnist_onehlayer.py +++ b/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() diff --git a/workshop_sections/starter_tf_graph/tf_matrix_mul.ipynb b/workshop_sections/starter_tf_graph/tf_matrix_mul.ipynb index 1d175d0..2397670 100644 --- a/workshop_sections/starter_tf_graph/tf_matrix_mul.ipynb +++ b/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" ] }, { diff --git a/workshop_sections/starter_tf_graph/tf_matrix_mul.py b/workshop_sections/starter_tf_graph/tf_matrix_mul.py index 3e7feac..fd0d821 100755 --- a/workshop_sections/starter_tf_graph/tf_matrix_mul.py +++ b/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. diff --git a/workshop_sections/starter_tf_graph/tf_matrix_mul_add.py b/workshop_sections/starter_tf_graph/tf_matrix_mul_add.py index 697e054..7e03e7e 100755 --- a/workshop_sections/starter_tf_graph/tf_matrix_mul_add.py +++ b/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. diff --git a/workshop_sections/transfer_learning/cloudml/trainer/model.py b/workshop_sections/transfer_learning/cloudml/trainer/model.py index dac4378..4ee65ae 100644 --- a/workshop_sections/transfer_learning/cloudml/trainer/model.py +++ b/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]. diff --git a/workshop_sections/transfer_learning/cloudml/trainer/preprocess.py b/workshop_sections/transfer_learning/cloudml/trainer/preprocess.py index bfa54d9..bada385 100644 --- a/workshop_sections/transfer_learning/cloudml/trainer/preprocess.py +++ b/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].