Merge pull request vahidk#2 from vrv/patch-1

Small clarifications on the shape section

README.md

@@ -108,17 +108,19 @@ This is just tip of the iceberg for what Tensorflow can do. Many problems such a
 
 ## Understanding static and dynamic shapes
 <a name="shapes"></a>
-Tensors in Tensorflow have a static shape attribute which is determined during graph construction. The static shape may be underspecified. For example we might define a tensor of shape [None, 128]:
+Tensors in Tensorflow have a static shape attribute which is determined during graph construction. The static shape may be underspecified. For example we might define a float32 tensor of shape [None, 128]:
 ```python
 import tensorflow as tf
 
-a = tf.placeholder([None, 128])
+a = tf.placeholder(tf.float32, [None, 128])
 ```
-This means that the first dimension can be of any size and will be determined dynamically during Session.run(). Tensorflow has a rather ugly API for exposing the static shape:
+This means that the first dimension can be of any size and will be determined dynamically during Session.run(). You
+can query the symbolic shape of a Tensor as follows:
+
 ```python
-static_shape = a.get_shape().as_list()  # returns [None, 128]
+static_shape = a.shape  # returns TensorShape([Dimension(None), Dimension(128)])
+static_shape = a.shape.as_list()  # returns [None, 128]
 ```
-(This used to be a.shape but someone decided it's too convenient.)
 
 To get the dynamic shape of the tensor you can call tf.shape op, which returns a tensor representing the shape of the given tensor:
 ```python
@@ -130,14 +132,18 @@ The static shape of a tensor can be set with Tensor.set_shape() method:
 a.set_shape([32, 128])
 ```
 Use this function only if you know what you are doing, in practice it's safer to do dynamic reshaping with tf.reshape() op:
+
 ```python
 a =  tf.reshape(a, [32, 128])
 ```
 
+If you feed 'a' with values that don't match the shape, you will get an InvalidArgumentError indicating that the
+number of values fed doesn't match the expected shape.
+
 It can be convenient to have a function that returns the static shape when available and dynamic shape when it's not. The following utility function does just that:
 ```python
 def get_shape(tensor):
-  static_shape = tensor.get_shape().as_list()
+  static_shape = tensor.shape.as_list()
   dynamic_shape = tf.unstack(tf.shape(tensor))
   dims = [s[1] if s[0] is None else s[0]
           for s in zip(static_shape, dynamic_shape)]
@@ -146,7 +152,7 @@ def get_shape(tensor):
 
 Now imagine we want to convert a Tensor of rank 3 to a tensor of rank 2 by collapsing the second and third dimensions into one. We can use our get_shape() function to do that:
 ```python
-b = placeholder([None, 10, 32])
+b = placeholder(tf.float32, [None, 10, 32])
 shape = get_shape(tensor)
 b = tf.reshape(b, [shape[0], shape[1] * shape[2]])
 ```
@@ -173,7 +179,7 @@ def reshape(tensor, dims_list):
 
 Then collapsing the second dimension becomes very easy:
 ```python
-b = placeholder([None, 10, 32])
+b = placeholder(tf.float32, [None, 10, 32])
 b = tf.reshape(b, [0, [1, 2]])
 ```