Tensor flow interested

examples/3_NeuralNetworks/dcgan.py

@@ -82,7 +82,7 @@ def discriminator(x, reuse=False):
 # Build Generator Network
 gen_sample = generator(noise_input)
 
-# Build 2 Discriminator Networks (one from noise input, one from generated samples)
+# Build 2 Discriminator Networks (one from real image input, one from generated samples)
 disc_real = discriminator(real_image_input)
 disc_fake = discriminator(gen_sample, reuse=True)
 disc_concat = tf.concat([disc_real, disc_fake], axis=0)
@@ -135,7 +135,7 @@ def discriminator(x, reuse=False):
         z = np.random.uniform(-1., 1., size=[batch_size, noise_dim])
 
         # Prepare Targets (Real image: 1, Fake image: 0)
-        # The first half of data fed to the generator are real images,
+        # The first half of data fed to the discriminator are real images,
         # the other half are fake images (coming from the generator).
         batch_disc_y = np.concatenate(
             [np.ones([batch_size]), np.zeros([batch_size])], axis=0)

examples/README.md

@@ -0,0 +1,5 @@
+## Deprecated - Please Read
+
+Due to TensorFlow radically changing their API in v2, the examples index have been split between [v1](../tensorflow_v1) and [v2](../tensorflow_v2).
+
+The following examples are the original TF v1 examples, and will be deprecated entirely in favor of [tensorflow_v1](../tensorflow_v1) directory in a future release.

input_data.py

@@ -90,7 +90,7 @@ def num_examples(self):
   def epochs_completed(self):
     return self._epochs_completed
   def next_batch(self, batch_size, fake_data=False):
-    """Return the next `batch_size` examples from this data set."""
+    """Return the next `batch_size`examples from this data set."""
     if fake_data:
       fake_image = [1.0 for _ in xrange(784)]
       fake_label = 0
@@ -141,4 +141,4 @@ class DataSets(object):
   data_sets.train = DataSet(train_images, train_labels)
   data_sets.validation = DataSet(validation_images, validation_labels)
   data_sets.test = DataSet(test_images, test_labels)
-  return data_sets
+  return data_sets

notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb

@@ -23,7 +23,7 @@
     "<img src=\"https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/191dd7df9cb91ac22f56ed0dfa4a5651e8767a51/1-Figure2-1.png\" alt=\"nn\" style=\"width: 600px;\"/>\n",
     "\n",
     "References:\n",
-    "- [Long Short Term Memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf), Sepp Hochreiter & Jurgen Schmidhuber, Neural Computation 9(8): 1735-1780, 1997.\n",
+    "- [Long Short Term Memory](https://www.researchgate.net/profile/Sepp_Hochreiter/publication/13853244_Long_Short-term_Memory/links/5700e75608aea6b7746a0624/Long-Short-term-Memory.pdf), Sepp Hochreiter & Jurgen Schmidhuber, Neural Computation 9(8): 1735-1780, 1997.\n",
     "\n",
     "## MNIST Dataset Overview\n",
     "\n",