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Diffstat (limited to 'tensorflow/docs_src/tutorials/wide.md')
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diff --git a/tensorflow/docs_src/tutorials/wide.md b/tensorflow/docs_src/tutorials/wide.md index c2621026c7..24c866eee5 100644 --- a/tensorflow/docs_src/tutorials/wide.md +++ b/tensorflow/docs_src/tutorials/wide.md @@ -1,6 +1,6 @@ # TensorFlow Linear Model Tutorial -In this tutorial, we will use the TF.Learn API in TensorFlow to solve a binary +In this tutorial, we will use the tf.contrib.learn API in TensorFlow to solve a binary classification problem: Given census data about a person such as age, gender, education and occupation (the features), we will try to predict whether or not the person earns more than 50,000 dollars a year (the target label). We will @@ -16,7 +16,7 @@ To try the code for this tutorial: 2. Download [the tutorial code](https://www.tensorflow.org/code/tensorflow/examples/learn/wide_n_deep_tutorial.py). -3. Install the pandas data analysis library. tf.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas: +3. Install the pandas data analysis library. tf.contrib.learn doesn't require pandas, but it does support it, and this tutorial uses pandas. To install pandas: a. Get `pip`: @@ -69,8 +69,8 @@ COLUMNS = ["age", "workclass", "fnlwgt", "education", "education_num", "marital_status", "occupation", "relationship", "race", "gender", "capital_gain", "capital_loss", "hours_per_week", "native_country", "income_bracket"] -df_train = pd.read_csv(train_file, names=COLUMNS, skipinitialspace=True) -df_test = pd.read_csv(test_file, names=COLUMNS, skipinitialspace=True, skiprows=1) +df_train = pd.read_csv(train_file.name, names=COLUMNS, skipinitialspace=True) +df_test = pd.read_csv(test_file.name, names=COLUMNS, skipinitialspace=True, skiprows=1) ``` Since the task is a binary classification problem, we'll construct a label @@ -136,9 +136,9 @@ Here's a list of columns available in the Census Income dataset: ## Converting Data into Tensors -When building a TF.Learn model, the input data is specified by means of an Input +When building a tf.contrib.learn model, the input data is specified by means of an Input Builder function. This builder function will not be called until it is later -passed to TF.Learn methods such as `fit` and `evaluate`. The purpose of this +passed to tf.contrib.learn methods such as `fit` and `evaluate`. The purpose of this function is to construct the input data, which is represented in the form of @{tf.Tensor}s or @@ -211,7 +211,7 @@ to predict the target label. ### Base Categorical Feature Columns To define a feature column for a categorical feature, we can create a -`SparseColumn` using the TF.Learn API. If you know the set of all possible +`SparseColumn` using the tf.contrib.learn API. If you know the set of all possible feature values of a column and there are only a few of them, you can use `sparse_column_with_keys`. Each key in the list will get assigned an auto-incremental ID starting from 0. For example, for the `gender` column we can @@ -361,7 +361,7 @@ in `model_dir`. ## Training and Evaluating Our Model After adding all the features to the model, now let's look at how to actually -train the model. Training a model is just a one-liner using the TF.Learn API: +train the model. Training a model is just a one-liner using the tf.contrib.learn API: ```python m.fit(input_fn=train_input_fn, steps=200) @@ -467,4 +467,4 @@ value would be high. If you're interested in learning more, check out our @{$wide_and_deep$Wide & Deep Learning Tutorial} where we'll show you how to combine the strengths of linear models and deep neural networks by jointly training them -using the TF.Learn API. +using the tf.contrib.learn API. |