Converted the models repo to TF 1.0 using the upgrade script

Author: nealwu

autoencoder/autoencoder_models/Autoencoder.py

@@ -18,7 +18,7 @@ def __init__(self, n_input, n_hidden, transfer_function=tf.nn.softplus, optimize
         self.reconstruction = tf.add(tf.matmul(self.hidden, self.weights['w2']), self.weights['b2'])
 
         # cost
-        self.cost = 0.5 * tf.reduce_sum(tf.pow(tf.sub(self.reconstruction, self.x), 2.0))
+        self.cost = 0.5 * tf.reduce_sum(tf.pow(tf.subtract(self.reconstruction, self.x), 2.0))
         self.optimizer = optimizer.minimize(self.cost)
 
         init = tf.global_variables_initializer()

autoencoder/autoencoder_models/DenoisingAutoencoder.py

@@ -22,7 +22,7 @@ def __init__(self, n_input, n_hidden, transfer_function = tf.nn.softplus, optimi
         self.reconstruction = tf.add(tf.matmul(self.hidden, self.weights['w2']), self.weights['b2'])
 
         # cost
-        self.cost = 0.5 * tf.reduce_sum(tf.pow(tf.sub(self.reconstruction, self.x), 2.0))
+        self.cost = 0.5 * tf.reduce_sum(tf.pow(tf.subtract(self.reconstruction, self.x), 2.0))
         self.optimizer = optimizer.minimize(self.cost)
 
         init = tf.global_variables_initializer()
@@ -89,7 +89,7 @@ def __init__(self, n_input, n_hidden, transfer_function = tf.nn.softplus, optimi
         self.reconstruction = tf.add(tf.matmul(self.hidden, self.weights['w2']), self.weights['b2'])
 
         # cost
-        self.cost = 0.5 * tf.reduce_sum(tf.pow(tf.sub(self.reconstruction, self.x), 2.0))
+        self.cost = 0.5 * tf.reduce_sum(tf.pow(tf.subtract(self.reconstruction, self.x), 2.0))
         self.optimizer = optimizer.minimize(self.cost)
 
         init = tf.global_variables_initializer()

autoencoder/autoencoder_models/VariationalAutoencoder.py

@@ -17,13 +17,13 @@ def __init__(self, n_input, n_hidden, optimizer = tf.train.AdamOptimizer()):
         self.z_log_sigma_sq = tf.add(tf.matmul(self.x, self.weights['log_sigma_w1']), self.weights['log_sigma_b1'])
 
         # sample from gaussian distribution
-        eps = tf.random_normal(tf.pack([tf.shape(self.x)[0], self.n_hidden]), 0, 1, dtype = tf.float32)
-        self.z = tf.add(self.z_mean, tf.mul(tf.sqrt(tf.exp(self.z_log_sigma_sq)), eps))
+        eps = tf.random_normal(tf.stack([tf.shape(self.x)[0], self.n_hidden]), 0, 1, dtype = tf.float32)
+        self.z = tf.add(self.z_mean, tf.multiply(tf.sqrt(tf.exp(self.z_log_sigma_sq)), eps))
 
         self.reconstruction = tf.add(tf.matmul(self.z, self.weights['w2']), self.weights['b2'])
 
         # cost
-        reconstr_loss = 0.5 * tf.reduce_sum(tf.pow(tf.sub(self.reconstruction, self.x), 2.0))
+        reconstr_loss = 0.5 * tf.reduce_sum(tf.pow(tf.subtract(self.reconstruction, self.x), 2.0))
         latent_loss = -0.5 * tf.reduce_sum(1 + self.z_log_sigma_sq
                                            - tf.square(self.z_mean)
                                            - tf.exp(self.z_log_sigma_sq), 1)

compression/decoder.py

@@ -273,7 +273,7 @@ def Train(mnist_train_file, mnist_test_file, network_parameters, num_steps,
         images, network_parameters)
 
     cost = tf.nn.softmax_cross_entropy_with_logits(
-        logits, tf.one_hot(labels, 10))
+        logits=logits, labels=tf.one_hot(labels, 10))
 
     # The actual cost is the average across the examples.
     cost = tf.reduce_sum(cost, [0]) / batch_size
@@ -343,7 +343,7 @@ def Train(mnist_train_file, mnist_test_file, network_parameters, num_steps,
 
     # We need to maintain the intialization sequence.
     for v in tf.trainable_variables():
-      sess.run(tf.initialize_variables([v]))
+      sess.run(tf.variables_initializer([v]))
     sess.run(tf.global_variables_initializer())
     sess.run(init_ops)
 

compression/encoder.py

@@ -236,7 +236,7 @@ def BatchClipByL2norm(t, upper_bound, name=None):
   with tf.op_scope([t, upper_bound], name, "batch_clip_by_l2norm") as name:
     saved_shape = tf.shape(t)
     batch_size = tf.slice(saved_shape, [0], [1])
-    t2 = tf.reshape(t, tf.concat(0, [batch_size, [-1]]))
+    t2 = tf.reshape(t, tf.concat(axis=0, values=[batch_size, [-1]]))
     upper_bound_inv = tf.fill(tf.slice(saved_shape, [0], [1]),
                               tf.constant(1.0/upper_bound))
     # Add a small number to avoid divide by 0
@@ -266,7 +266,7 @@ def SoftThreshold(t, threshold_ratio, name=None):
   assert threshold_ratio >= 0
   with tf.op_scope([t, threshold_ratio], name, "soft_thresholding") as name:
     saved_shape = tf.shape(t)
-    t2 = tf.reshape(t, tf.concat(0, [tf.slice(saved_shape, [0], [1]), -1]))
+    t2 = tf.reshape(t, tf.concat(axis=0, values=[tf.slice(saved_shape, [0], [1]), -1]))
     t_abs = tf.abs(t2)
     t_x = tf.sign(t2) * tf.nn.relu(t_abs -
                                    (tf.reduce_mean(t_abs, [0],

compression/msssim.py

@@ -189,7 +189,7 @@ def __call__(self, x, z_grads):
     z_grads, = z_grads
     x_expanded = tf.expand_dims(x, 2)
     z_grads_expanded = tf.expand_dims(z_grads, 1)
-    return tf.mul(x_expanded, z_grads_expanded)
+    return tf.multiply(x_expanded, z_grads_expanded)
 
 
 pxg_registry.Register("MatMul", MatMulPXG)
@@ -245,7 +245,7 @@ def _PxConv2DBuilder(self, input_, w, strides, padding):
       num_x = int(conv_x.get_shape()[0])
       assert num_x == 1, num_x
     assert len(conv_px) == batch_size
-    conv = tf.concat(0, conv_px)
+    conv = tf.concat(axis=0, values=conv_px)
     assert int(conv.get_shape()[0]) == batch_size
     return conv, w_px
 
@@ -274,7 +274,7 @@ def __call__(self, w, z_grads):
                                   self.colocate_gradients_with_ops,
                                   gate_gradients=self.gate_gradients)
 
-    return tf.pack(gradients_list)
+    return tf.stack(gradients_list)
 
 pxg_registry.Register("Conv2D", Conv2DPXG)
 

differential_privacy/dp_sgd/dp_mnist/dp_mnist.py

@@ -75,7 +75,7 @@ def _variable_with_weight_decay(name, shape, stddev, wd):
   var = _variable_on_cpu(name, shape,
                          tf.truncated_normal_initializer(stddev=stddev))
   if wd is not None:
-    weight_decay = tf.mul(tf.nn.l2_loss(var), wd, name='weight_loss')
+    weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')
     tf.add_to_collection('losses', weight_decay)
   return var
 
@@ -398,7 +398,7 @@ def train_op_fun(total_loss, global_step):
                                   decay_steps,
                                   LEARNING_RATE_DECAY_FACTOR,
                                   staircase=True)
-  tf.scalar_summary('learning_rate', lr)
+  tf.summary.scalar('learning_rate', lr)
 
   # Generate moving averages of all losses and associated summaries.
   loss_averages_op = moving_av(total_loss)
@@ -413,7 +413,7 @@ def train_op_fun(total_loss, global_step):
 
   # Add histograms for trainable variables.
   for var in tf.trainable_variables():
-    tf.histogram_summary(var.op.name, var)
+    tf.summary.histogram(var.op.name, var)
 
   # Track the moving averages of all trainable variables.
   variable_averages = tf.train.ExponentialMovingAverage(
@@ -485,7 +485,7 @@ def train(images, labels, ckpt_path, dropout=False):
     train_op = train_op_fun(loss, global_step)
 
     # Create a saver.
-    saver = tf.train.Saver(tf.all_variables())
+    saver = tf.train.Saver(tf.global_variables())
 
     print("Graph constructed and saver created")