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Diffstat (limited to 'tensorflow/docs_src/guide/low_level_intro.md')
| -rw-r--r-- | tensorflow/docs_src/guide/low_level_intro.md | 46 |
1 files changed, 23 insertions, 23 deletions
diff --git a/tensorflow/docs_src/guide/low_level_intro.md b/tensorflow/docs_src/guide/low_level_intro.md index 665a5568b4..dc6cb9ee0d 100644 --- a/tensorflow/docs_src/guide/low_level_intro.md +++ b/tensorflow/docs_src/guide/low_level_intro.md @@ -63,17 +63,17 @@ TensorFlow uses numpy arrays to represent tensor **values**. You might think of TensorFlow Core programs as consisting of two discrete sections: -1. Building the computational graph (a @{tf.Graph}). -2. Running the computational graph (using a @{tf.Session}). +1. Building the computational graph (a `tf.Graph`). +2. Running the computational graph (using a `tf.Session`). ### Graph A **computational graph** is a series of TensorFlow operations arranged into a graph. The graph is composed of two types of objects. - * @{tf.Operation$Operations} (or "ops"): The nodes of the graph. + * `tf.Operation` (or "ops"): The nodes of the graph. Operations describe calculations that consume and produce tensors. - * @{tf.Tensor$Tensors}: The edges in the graph. These represent the values + * `tf.Tensor`: The edges in the graph. These represent the values that will flow through the graph. Most TensorFlow functions return `tf.Tensors`. @@ -149,7 +149,7 @@ For more about TensorBoard's graph visualization tools see @{$graph_viz}. ### Session -To evaluate tensors, instantiate a @{tf.Session} object, informally known as a +To evaluate tensors, instantiate a `tf.Session` object, informally known as a **session**. A session encapsulates the state of the TensorFlow runtime, and runs TensorFlow operations. If a `tf.Graph` is like a `.py` file, a `tf.Session` is like the `python` executable. @@ -232,7 +232,7 @@ z = x + y The preceding three lines are a bit like a function in which we define two input parameters (`x` and `y`) and then an operation on them. We can evaluate this graph with multiple inputs by using the `feed_dict` argument of -the @{tf.Session.run$run method} to feed concrete values to the placeholders: +the `tf.Session.run` method to feed concrete values to the placeholders: ```python print(sess.run(z, feed_dict={x: 3, y: 4.5})) @@ -251,15 +251,15 @@ that placeholders throw an error if no value is fed to them. ## Datasets -Placeholders work for simple experiments, but @{tf.data$Datasets} are the +Placeholders work for simple experiments, but `tf.data` are the preferred method of streaming data into a model. To get a runnable `tf.Tensor` from a Dataset you must first convert it to a -@{tf.data.Iterator}, and then call the Iterator's -@{tf.data.Iterator.get_next$`get_next`} method. +`tf.data.Iterator`, and then call the Iterator's +`tf.data.Iterator.get_next` method. The simplest way to create an Iterator is with the -@{tf.data.Dataset.make_one_shot_iterator$`make_one_shot_iterator`} method. +`tf.data.Dataset.make_one_shot_iterator` method. For example, in the following code the `next_item` tensor will return a row from the `my_data` array on each `run` call: @@ -275,7 +275,7 @@ next_item = slices.make_one_shot_iterator().get_next() ``` Reaching the end of the data stream causes `Dataset` to throw an -@{tf.errors.OutOfRangeError$`OutOfRangeError`}. For example, the following code +`tf.errors.OutOfRangeError`. For example, the following code reads the `next_item` until there is no more data to read: ``` python @@ -308,7 +308,7 @@ For more details on Datasets and Iterators see: @{$guide/datasets}. ## Layers A trainable model must modify the values in the graph to get new outputs with -the same input. @{tf.layers$Layers} are the preferred way to add trainable +the same input. `tf.layers` are the preferred way to add trainable parameters to a graph. Layers package together both the variables and the operations that act @@ -321,7 +321,7 @@ The connection weights and biases are managed by the layer object. ### Creating Layers -The following code creates a @{tf.layers.Dense$`Dense`} layer that takes a +The following code creates a `tf.layers.Dense` layer that takes a batch of input vectors, and produces a single output value for each. To apply a layer to an input, call the layer as if it were a function. For example: @@ -375,8 +375,8 @@ will generate a two-element output vector such as the following: ### Layer Function shortcuts -For each layer class (like @{tf.layers.Dense}) TensorFlow also supplies a -shortcut function (like @{tf.layers.dense}). The only difference is that the +For each layer class (like `tf.layers.Dense`) TensorFlow also supplies a +shortcut function (like `tf.layers.dense`). The only difference is that the shortcut function versions create and run the layer in a single call. For example, the following code is equivalent to the earlier version: @@ -390,17 +390,17 @@ sess.run(init) print(sess.run(y, {x: [[1, 2, 3], [4, 5, 6]]})) ``` -While convenient, this approach allows no access to the @{tf.layers.Layer} +While convenient, this approach allows no access to the `tf.layers.Layer` object. This makes introspection and debugging more difficult, and layer reuse impossible. ## Feature columns The easiest way to experiment with feature columns is using the -@{tf.feature_column.input_layer} function. This function only accepts +`tf.feature_column.input_layer` function. This function only accepts @{$feature_columns$dense columns} as inputs, so to view the result of a categorical column you must wrap it in an -@{tf.feature_column.indicator_column}. For example: +`tf.feature_column.indicator_column`. For example: ``` python features = { @@ -422,9 +422,9 @@ inputs = tf.feature_column.input_layer(features, columns) Running the `inputs` tensor will parse the `features` into a batch of vectors. Feature columns can have internal state, like layers, so they often need to be -initialized. Categorical columns use @{tf.contrib.lookup$lookup tables} +initialized. Categorical columns use `tf.contrib.lookup` internally and these require a separate initialization op, -@{tf.tables_initializer}. +`tf.tables_initializer`. ``` python var_init = tf.global_variables_initializer() @@ -501,7 +501,7 @@ To optimize a model, you first need to define the loss. We'll use the mean square error, a standard loss for regression problems. While you could do this manually with lower level math operations, -the @{tf.losses} module provides a set of common loss functions. You can use it +the `tf.losses` module provides a set of common loss functions. You can use it to calculate the mean square error as follows: ``` python @@ -520,10 +520,10 @@ This will produce a loss value, something like: TensorFlow provides [**optimizers**](https://developers.google.com/machine-learning/glossary/#optimizer) implementing standard optimization algorithms. These are implemented as -sub-classes of @{tf.train.Optimizer}. They incrementally change each +sub-classes of `tf.train.Optimizer`. They incrementally change each variable in order to minimize the loss. The simplest optimization algorithm is [**gradient descent**](https://developers.google.com/machine-learning/glossary/#gradient_descent), -implemented by @{tf.train.GradientDescentOptimizer}. It modifies each +implemented by `tf.train.GradientDescentOptimizer`. It modifies each variable according to the magnitude of the derivative of loss with respect to that variable. For example: |