Merge pull request #2254 from alanyee/master

Update autoencoders

autoencoder/AdditiveGaussianNoiseAutoencoderRunner.py

-import numpy as np
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 
+import numpy as np
 import sklearn.preprocessing as prep
 import tensorflow as tf
 from tensorflow.examples.tutorials.mnist import input_data
 
 from autoencoder_models.DenoisingAutoencoder import AdditiveGaussianNoiseAutoencoder
 
-mnist = input_data.read_data_sets('MNIST_data', one_hot = True)
+mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
+
 
 def standard_scale(X_train, X_test):
     preprocessor = prep.StandardScaler().fit(X_train)
@@ -14,10 +18,12 @@ def standard_scale(X_train, X_test):
     X_test = preprocessor.transform(X_test)
     return X_train, X_test
 
+
 def get_random_block_from_data(data, batch_size):
     start_index = np.random.randint(0, len(data) - batch_size)
     return data[start_index:(start_index + batch_size)]
 
+
 X_train, X_test = standard_scale(mnist.train.images, mnist.test.images)
 
 n_samples = int(mnist.train.num_examples)
@@ -25,11 +31,12 @@ training_epochs = 20
 batch_size = 128
 display_step = 1
 
-autoencoder = AdditiveGaussianNoiseAutoencoder(n_input = 784,
-                                               n_hidden = 200,
-                                               transfer_function = tf.nn.softplus,
-                                               optimizer = tf.train.AdamOptimizer(learning_rate = 0.001),
-                                               scale = 0.01)
+autoencoder = AdditiveGaussianNoiseAutoencoder(
+    n_input=784,
+    n_hidden=200,
+    transfer_function=tf.nn.softplus,
+    optimizer=tf.train.AdamOptimizer(learning_rate = 0.001),
+    scale=0.01)
 
 for epoch in range(training_epochs):
     avg_cost = 0.
@@ -45,6 +52,7 @@ for epoch in range(training_epochs):
 
     # Display logs per epoch step
     if epoch % display_step == 0:
-        print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(avg_cost))
+        print("Epoch:", '%d,' % (epoch + 1),
+              "Cost:", "{:.9f}".format(avg_cost))
 
 print("Total cost: " + str(autoencoder.calc_total_cost(X_test)))

autoencoder/AutoencoderRunner.py

-import numpy as np
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 
+import numpy as np
 import sklearn.preprocessing as prep
 import tensorflow as tf
 from tensorflow.examples.tutorials.mnist import input_data
 
 from autoencoder_models.Autoencoder import Autoencoder
 
-mnist = input_data.read_data_sets('MNIST_data', one_hot = True)
+mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
+
 
 def standard_scale(X_train, X_test):
     preprocessor = prep.StandardScaler().fit(X_train)
@@ -14,10 +18,12 @@ def standard_scale(X_train, X_test):
     X_test = preprocessor.transform(X_test)
     return X_train, X_test
 
+
 def get_random_block_from_data(data, batch_size):
     start_index = np.random.randint(0, len(data) - batch_size)
     return data[start_index:(start_index + batch_size)]
 
+
 X_train, X_test = standard_scale(mnist.train.images, mnist.test.images)
 
 n_samples = int(mnist.train.num_examples)
@@ -25,10 +31,11 @@ training_epochs = 20
 batch_size = 128
 display_step = 1
 
-autoencoder = Autoencoder(n_input = 784,
-                          n_hidden = 200,
-                          transfer_function = tf.nn.softplus,
-                          optimizer = tf.train.AdamOptimizer(learning_rate = 0.001))
+autoencoder = Autoencoder(
+    n_input=784,
+    n_hidden=200,
+    transfer_function=tf.nn.softplus,
+    optimizer=tf.train.AdamOptimizer(learning_rate=0.001))
 
 for epoch in range(training_epochs):
     avg_cost = 0.
@@ -44,6 +51,7 @@ for epoch in range(training_epochs):
 
     # Display logs per epoch step
     if epoch % display_step == 0:
-        print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(avg_cost))
+        print("Epoch:", '%d,' % (epoch + 1),
+              "Cost:", "{:.9f}".format(avg_cost))
 
 print("Total cost: " + str(autoencoder.calc_total_cost(X_test)))

autoencoder/MaskingNoiseAutoencoderRunner.py

-import numpy as np
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 
+import numpy as np
 import sklearn.preprocessing as prep
 import tensorflow as tf
 from tensorflow.examples.tutorials.mnist import input_data
 
 from autoencoder_models.DenoisingAutoencoder import MaskingNoiseAutoencoder
 
-mnist = input_data.read_data_sets('MNIST_data', one_hot = True)
+mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
+
 
 def standard_scale(X_train, X_test):
     preprocessor = prep.StandardScaler().fit(X_train)
@@ -14,23 +18,25 @@ def standard_scale(X_train, X_test):
     X_test = preprocessor.transform(X_test)
     return X_train, X_test
 
+
 def get_random_block_from_data(data, batch_size):
     start_index = np.random.randint(0, len(data) - batch_size)
     return data[start_index:(start_index + batch_size)]
 
-X_train, X_test = standard_scale(mnist.train.images, mnist.test.images)
 
+X_train, X_test = standard_scale(mnist.train.images, mnist.test.images)
 
 n_samples = int(mnist.train.num_examples)
 training_epochs = 100
 batch_size = 128
 display_step = 1
 
-autoencoder = MaskingNoiseAutoencoder(n_input = 784,
-                                      n_hidden = 200,
-                                      transfer_function = tf.nn.softplus,
-                                      optimizer = tf.train.AdamOptimizer(learning_rate = 0.001),
-                                      dropout_probability = 0.95)
+autoencoder = MaskingNoiseAutoencoder(
+    n_input=784,
+    n_hidden=200,
+    transfer_function=tf.nn.softplus,
+    optimizer=tf.train.AdamOptimizer(learning_rate=0.001),
+    dropout_probability=0.95)
 
 for epoch in range(training_epochs):
     avg_cost = 0.
@@ -43,6 +49,7 @@ for epoch in range(training_epochs):
         avg_cost += cost / n_samples * batch_size
 
     if epoch % display_step == 0:
-        print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(avg_cost))
+        print("Epoch:", '%d,' % (epoch + 1),
+              "Cost:", "{:.9f}".format(avg_cost))
 
 print("Total cost: " + str(autoencoder.calc_total_cost(X_test)))

autoencoder/VariationalAutoencoderRunner.py

-import numpy as np
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
 
+import numpy as np
 import sklearn.preprocessing as prep
 import tensorflow as tf
 from tensorflow.examples.tutorials.mnist import input_data
 
 from autoencoder_models.VariationalAutoencoder import VariationalAutoencoder
 
-mnist = input_data.read_data_sets('MNIST_data', one_hot = True)
-
+mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
 
 
 def min_max_scale(X_train, X_test):
@@ -29,9 +31,10 @@ training_epochs = 20
 batch_size = 128
 display_step = 1
 
-autoencoder = VariationalAutoencoder(n_input = 784,
-                                     n_hidden = 200,
-                                     optimizer = tf.train.AdamOptimizer(learning_rate = 0.001))
+autoencoder = VariationalAutoencoder(
+    n_input=784,
+    n_hidden=200,
+    optimizer=tf.train.AdamOptimizer(learning_rate = 0.001))
 
 for epoch in range(training_epochs):
     avg_cost = 0.
@@ -47,6 +50,7 @@ for epoch in range(training_epochs):
 
     # Display logs per epoch step
     if epoch % display_step == 0:
-        print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(avg_cost))
+        print("Epoch:", '%d,' % (epoch + 1),
+              "Cost:", "{:.9f}".format(avg_cost))
 
 print("Total cost: " + str(autoencoder.calc_total_cost(X_test)))