diff options
Diffstat (limited to 'tensorflow/docs_src/get_started/get_started.md')
| -rw-r--r-- | tensorflow/docs_src/get_started/get_started.md | 16 |
1 files changed, 8 insertions, 8 deletions
diff --git a/tensorflow/docs_src/get_started/get_started.md b/tensorflow/docs_src/get_started/get_started.md index 852d41e9ed..815b83e5fb 100644 --- a/tensorflow/docs_src/get_started/get_started.md +++ b/tensorflow/docs_src/get_started/get_started.md @@ -32,8 +32,8 @@ tensor's **rank** is its number of dimensions. Here are some examples of tensors: ```python -3 # a rank 0 tensor; this is a scalar with shape [] -[1., 2., 3.] # a rank 1 tensor; this is a vector with shape [3] +3 # a rank 0 tensor; a scalar with shape [] +[1., 2., 3.] # a rank 1 tensor; a vector with shape [3] [[1., 2., 3.], [4., 5., 6.]] # a rank 2 tensor; a matrix with shape [2, 3] [[[1., 2., 3.]], [[7., 8., 9.]]] # a rank 3 tensor with shape [2, 1, 3] ``` @@ -181,7 +181,7 @@ initial value: W = tf.Variable([.3], dtype=tf.float32) b = tf.Variable([-.3], dtype=tf.float32) x = tf.placeholder(tf.float32) -linear_model = W * x + b +linear_model = W*x + b ``` Constants are initialized when you call `tf.constant`, and their value can never @@ -302,7 +302,7 @@ W = tf.Variable([.3], dtype=tf.float32) b = tf.Variable([-.3], dtype=tf.float32) # Model input and output x = tf.placeholder(tf.float32) -linear_model = W * x + b +linear_model = W*x + b y = tf.placeholder(tf.float32) # loss @@ -330,9 +330,9 @@ When run, it produces W: [-0.9999969] b: [ 0.99999082] loss: 5.69997e-11 ``` -Notice that the loss is a very small number (very close to zero). If you run this -program, your loss may not be the exact same because the model is initialized -with pseudorandom values. +Notice that the loss is a very small number (very close to zero). If you run +this program, your loss may not be exactly the same as the aforementioned loss +because the model is initialized with pseudorandom values. This more complicated program can still be visualized in TensorBoard  @@ -426,7 +426,7 @@ def model_fn(features, labels, mode): # Build a linear model and predict values W = tf.get_variable("W", [1], dtype=tf.float64) b = tf.get_variable("b", [1], dtype=tf.float64) - y = W * features['x'] + b + y = W*features['x'] + b # Loss sub-graph loss = tf.reduce_sum(tf.square(y - labels)) # Training sub-graph |