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Fix typo in 'time_series.ipynb' notebook #1647

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Aug 26, 2020
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Fix typo in 'time_series.ipynb' notebook #1647

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b47822f
Fix typo in 'time_series.ipynb' notebook
tallamjr Aug 10, 2020
330cdd5
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tfdocsbot Aug 10, 2020
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4 changes: 1 addition & 3 deletions site/en/tutorials/reinforcement_learning/actor_critic.ipynb
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Expand Up @@ -505,9 +505,7 @@
"\n",
"$$L_{critic} = L_{\\delta}(G, V^{\\pi}_{\\theta})$$\n",
"\n",
"where $L_{\\delta}$ is the [Huber loss](https://en.wikipedia.org/wiki/Huber_loss), which is less sensitive to outliers in data than squared-error loss.\n",
"\n",
"\n"
"where $L_{\\delta}$ is the [Huber loss](https://en.wikipedia.org/wiki/Huber_loss), which is less sensitive to outliers in data than squared-error loss.\n"
]
},
{
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23 changes: 8 additions & 15 deletions site/en/tutorials/structured_data/time_series.ipynb
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Expand Up @@ -890,7 +890,7 @@
"source": [
"Typically data in TensorFlow is packed into arrays where the outermost index is across examples (the \"batch\" dimension). The middle indices are the \"time\" or \"space\" (width, height) dimension(s). The innermost indices are the features.\n",
"\n",
"The code above took a batch of 2, 7-timestep windows, with 19 features at each time step. It split them into a batch of 6-timestep, 19 feature inputs, and a 1-timestep 1-feature label. The label only has one feature because the `WindowGenerator` was initialized with `label_columns=['T (degC)']`. Initially this tutorial will build models that predict single output labels."
"The code above took a batch of 3, 7-timestep windows, with 19 features at each time step. It split them into a batch of 6-timestep, 19 feature inputs, and a 1-timestep 1-feature label. The label only has one feature because the `WindowGenerator` was initialized with `label_columns=['T (degC)']`. Initially this tutorial will build models that predict single output labels."
]
},
{
Expand Down Expand Up @@ -1197,8 +1197,7 @@
"id": "RKTm8ajVGw4N"
},
"source": [
"The `window` object creates `tf.data.Datasets` from the training, validation, and test sets, allowing you to easily iterate over batches of data.\n",
"\n"
"The `window` object creates `tf.data.Datasets` from the training, validation, and test sets, allowing you to easily iterate over batches of data.\n"
]
},
{
Expand Down Expand Up @@ -1522,7 +1521,7 @@
"id": "X-CGj85oKaOG"
},
"source": [
"Here is the plot of its example predictions on the `wide_widow`, note how in many cases the prediction is clearly better than just returning the input temperature, but in a few cases it's worse:"
"Here is the plot of its example predictions on the `wide_window`, note how in many cases the prediction is clearly better than just returning the input temperature, but in a few cases it's worse:"
]
},
{
Expand Down Expand Up @@ -1645,8 +1644,7 @@
"id": "gtN4BwZ37niR"
},
"source": [
"Note that the `Window`'s `shift` parameter is relative to the end of the two windows.\n",
"\n"
"Note that the `Window`'s `shift` parameter is relative to the end of the two windows.\n"
]
},
{
Expand Down Expand Up @@ -2173,8 +2171,7 @@
"\n",
"The models so far all predicted a single output feature, `T (degC)`, for a single time step.\n",
"\n",
"All of these models can be converted to predict multiple features just by changing the number of units in the output layer and adjusting the training windows to include all features in the `labels`.\n",
"\n"
"All of these models can be converted to predict multiple features just by changing the number of units in the output layer and adjusting the training windows to include all features in the `labels`.\n"
]
},
{
Expand Down Expand Up @@ -2304,8 +2301,7 @@
"id": "dsc9pur_mHsx"
},
"source": [
"#### RNN\n",
"\n"
"#### RNN\n"
]
},
{
Expand Down Expand Up @@ -2518,8 +2514,7 @@
"1. Single shot predictions where the entire time series is predicted at once.\n",
"2. Autoregressive predictions where the model only makes single step predictions and its output is fed back as its input.\n",
"\n",
"In this section all the models will predict **all the features across all output time steps**.\n",
"\n"
"In this section all the models will predict **all the features across all output time steps**.\n"
]
},
{
Expand Down Expand Up @@ -2861,9 +2856,7 @@
"\n",
"You could take any of single single-step multi-output models trained in the first half of this tutorial and run in an autoregressive feedback loop, but here we'll focus on building a model that's been explicitly trained to do that.\n",
"\n",
"![Feedback a model's output to its input](images/multistep_autoregressive.png)\n",
"\n",
"\n"
"![Feedback a model's output to its input](images/multistep_autoregressive.png)\n"
]
},
{
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