Merge pull request aymericdamien#63 from vra/master

Fix format and some typos

Author: aymericdamien

examples/1_Introduction/helloworld.py

@@ -9,7 +9,7 @@
 
 import tensorflow as tf
 
-#Simple hello world using TensorFlow
+# Simple hello world using TensorFlow
 
 # Create a Constant op
 # The op is added as a node to the default graph.

examples/2_BasicModels/linear_regression.py

@@ -52,7 +52,7 @@
         for (x, y) in zip(train_X, train_Y):
             sess.run(optimizer, feed_dict={X: x, Y: y})
 
-        #Display logs per epoch step
+        # Display logs per epoch step
         if (epoch+1) % display_step == 0:
             c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
             print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \
@@ -62,7 +62,7 @@
     training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
     print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')
 
-    #Graphic display
+    # Graphic display
     plt.plot(train_X, train_Y, 'ro', label='Original data')
     plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
     plt.legend()

examples/2_BasicModels/logistic_regression.py

@@ -11,7 +11,7 @@
 
 import tensorflow as tf
 
-# Import MINST data
+# Import MNIST data
 from tensorflow.examples.tutorials.mnist import input_data
 mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
 

examples/2_BasicModels/nearest_neighbor.py

@@ -12,7 +12,7 @@
 import numpy as np
 import tensorflow as tf
 
-# Import MINST data
+# Import MNIST data
 from tensorflow.examples.tutorials.mnist import input_data
 mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
 

examples/3_NeuralNetworks/autoencoder.py

@@ -15,7 +15,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
-# Import MINST data
+# Import MNIST data
 from tensorflow.examples.tutorials.mnist import input_data
 mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
 

examples/3_NeuralNetworks/bidirectional_rnn.py

@@ -1,5 +1,5 @@
 '''
-A Bidirectional Reccurent Neural Network (LSTM) implementation example using TensorFlow library.
+A Bidirectional Recurrent Neural Network (LSTM) implementation example using TensorFlow library.
 This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)
 Long Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
 
@@ -13,12 +13,12 @@
 from tensorflow.python.ops import rnn, rnn_cell
 import numpy as np
 
-# Import MINST data
+# Import MNIST data
 from tensorflow.examples.tutorials.mnist import input_data
 mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
 
 '''
-To classify images using a bidirectional reccurent neural network, we consider
+To classify images using a bidirectional recurrent neural network, we consider
 every image row as a sequence of pixels. Because MNIST image shape is 28*28px,
 we will then handle 28 sequences of 28 steps for every sample.
 '''
@@ -41,7 +41,7 @@
 
 # Define weights
 weights = {
-    # Hidden layer weights => 2*n_hidden because of foward + backward cells
+    # Hidden layer weights => 2*n_hidden because of forward + backward cells
     'out': tf.Variable(tf.random_normal([2*n_hidden, n_classes]))
 }
 biases = {

examples/3_NeuralNetworks/convolutional_network.py

@@ -11,7 +11,7 @@
 
 import tensorflow as tf
 
-# Import MINST data
+# Import MNIST data
 from tensorflow.examples.tutorials.mnist import input_data
 mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
 

examples/3_NeuralNetworks/dynamic_rnn.py

@@ -1,5 +1,5 @@
 '''
-A Dynamic Reccurent Neural Network (LSTM) implementation example using
+A Dynamic Recurrent Neural Network (LSTM) implementation example using
 TensorFlow library. This example is using a toy dataset to classify linear
 sequences. The generated sequences have variable length.
 
@@ -26,7 +26,7 @@ class ToySequenceData(object):
 
     NOTICE:
     We have to pad each sequence to reach 'max_seq_len' for TensorFlow
-    consistency (we cannot feed a numpy array with unconsistent
+    consistency (we cannot feed a numpy array with inconsistent
     dimensions). The dynamic calculation will then be perform thanks to
     'seqlen' attribute that records every actual sequence length.
     """
@@ -130,7 +130,7 @@ def dynamicRNN(x, seqlen, weights, biases):
                                 sequence_length=seqlen)
 
     # When performing dynamic calculation, we must retrieve the last
-    # dynamically computed output, i.e, if a sequence length is 10, we need
+    # dynamically computed output, i.e., if a sequence length is 10, we need
     # to retrieve the 10th output.
     # However TensorFlow doesn't support advanced indexing yet, so we build
     # a custom op that for each sample in batch size, get its length and

examples/3_NeuralNetworks/multilayer_perceptron.py

@@ -9,7 +9,7 @@
 
 from __future__ import print_function
 
-# Import MINST data
+# Import MNIST data
 from tensorflow.examples.tutorials.mnist import input_data
 mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
 

examples/3_NeuralNetworks/recurrent_network.py

@@ -1,5 +1,5 @@
 '''
-A Reccurent Neural Network (LSTM) implementation example using TensorFlow library.
+A Recurrent Neural Network (LSTM) implementation example using TensorFlow library.
 This example is using the MNIST database of handwritten digits (http://yann.lecun.com/exdb/mnist/)
 Long Short Term Memory paper: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
 
@@ -18,7 +18,7 @@
 mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
 
 '''
-To classify images using a reccurent neural network, we consider every image
+To classify images using a recurrent neural network, we consider every image
 row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then
 handle 28 sequences of 28 steps for every sample.
 '''