"Download the training dataset file using the `[tf.keras.utils.get_file](https://www.tensorflow.org/api_docs/python/tf/keras/utils/get_file)` function. This returns the file path of the downloaded file."
"Download the training dataset file using the [tf.keras.utils.get_file](https://www.tensorflow.org/api_docs/python/tf/keras/utils/get_file) function. This returns the file path of the downloaded file."
]
},
{
...
...
@@ -347,7 +347,7 @@
"\n",
"TensorFlow's [Dataset API](https://www.tensorflow.org/programmers_guide/datasets) handles many common cases for feeding data into a model. This is a high-level API for reading data and transforming it into a form used for training. See the [Datasets Quick Start guide](https://www.tensorflow.org/get_started/datasets_quickstart) for more information.\n",
"\n",
"This program uses `[tf.data.TextLineDataset](https://www.tensorflow.org/api_docs/python/tf/data/TextLineDataset)` to load a CSV-formatted text file and is parsed with our `parse_csv` function. A `[tf.data.Dataset](https://www.tensorflow.org/api_docs/python/tf/data/Dataset)` represents an input pipeline as a collection of elements and a series of transformations that act on those elements. Transformation methods are chained together or called sequentially—just make sure to keep a reference to the returned `Dataset` object.\n",
"This program uses [tf.data.TextLineDataset](https://www.tensorflow.org/api_docs/python/tf/data/TextLineDataset) to load a CSV-formatted text file and is parsed with our `parse_csv` function. A [tf.data.Dataset](https://www.tensorflow.org/api_docs/python/tf/data/Dataset) represents an input pipeline as a collection of elements and a series of transformations that act on those elements. Transformation methods are chained together or called sequentially—just make sure to keep a reference to the returned `Dataset` object.\n",
"\n",
"Training works best if the examples are in random order. Use `tf.data.Dataset.shuffle` to randomize entries, setting `buffer_size` to a value larger than the number of examples (120 in this case). To train the model faster, the dataset's *[batch size](https://developers.google.com/machine-learning/glossary/#batch_size)* is set to `32` examples to train at once."
]
...
...
@@ -418,9 +418,9 @@
"source": [
"### Create a model using Keras\n",
"\n",
"The TensorFlow `[tf.keras](https://www.tensorflow.org/api_docs/python/tf/keras)` API is the preferred way to create models and layers. This makes it easy to build models and experiment while Keras handles the complexity of connecting everything together. See the [Keras documentation](https://keras.io/) for details.\n",
"The TensorFlow [tf.keras](https://www.tensorflow.org/api_docs/python/tf/keras) API is the preferred way to create models and layers. This makes it easy to build models and experiment while Keras handles the complexity of connecting everything together. See the [Keras documentation](https://keras.io/) for details.\n",
"\n",
"The `[tf.keras.Sequential](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential)` model is a linear stack of layers. Its constructor takes a list of layer instances, in this case, two `[Dense](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense)` layers with 10 nodes each, and an output layer with 3 nodes representing our label predictions. The first layer's `input_shape` parameter corresponds to the amount of features from the dataset, and is required."
"The [tf.keras.Sequential](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential) model is a linear stack of layers. Its constructor takes a list of layer instances, in this case, two [Dense](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense) layers with 10 nodes each, and an output layer with 3 nodes representing our label predictions. The first layer's `input_shape` parameter corresponds to the amount of features from the dataset, and is required."
]
},
{
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@@ -482,7 +482,7 @@
"\n",
"Both training and evaluation stages need to calculate the model's *[loss](https://developers.google.com/machine-learning/crash-course/glossary#loss)*. This measures how off a model's predictions are from the desired label, in other words, how bad the model is performing. We want to minimize, or optimize, this value.\n",
"\n",
"Our model will calculate its loss using the `[tf.losses.sparse_softmax_cross_entropy](https://www.tensorflow.org/api_docs/python/tf/losses/sparse_softmax_cross_entropy)` function which takes the model's prediction and the desired label. The returned loss value is progressively larger as the prediction gets worse."
"Our model will calculate its loss using the [tf.losses.sparse_softmax_cross_entropy](https://www.tensorflow.org/api_docs/python/tf/losses/sparse_softmax_cross_entropy) function which takes the model's prediction and the desired label. The returned loss value is progressively larger as the prediction gets worse."
]
},
{
...
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@@ -518,7 +518,7 @@
},
"cell_type": "markdown",
"source": [
"The `grad` function uses the `loss` function and the `[tfe.GradientTape](https://www.tensorflow.org/api_docs/python/tf/contrib/eager/GradientTape)` to record operations that compute the *[gradients](https://developers.google.com/machine-learning/crash-course/glossary#gradient)* used to optimize our model. For more examples of this, see the [eager execution guide](https://www.tensorflow.org/programmers_guide/eager)."
"The `grad` function uses the `loss` function and the [tfe.GradientTape](https://www.tensorflow.org/api_docs/python/tf/contrib/eager/GradientTape) to record operations that compute the *[gradients](https://developers.google.com/machine-learning/crash-course/glossary#gradient)* used to optimize our model. For more examples of this, see the [eager execution guide](https://www.tensorflow.org/programmers_guide/eager)."
]
},
{
...
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@@ -539,7 +539,7 @@
" </figcaption>\n",
"</figure>\n",
"\n",
"TensorFlow has many [optimization algorithms](https://www.tensorflow.org/api_guides/python/train) available for training. This model uses the `[tf.train.GradientDescentOptimizer](https://www.tensorflow.org/api_docs/python/tf/train/GradientDescentOptimizer)` that implements the *[standard gradient descent](https://developers.google.com/machine-learning/crash-course/glossary#gradient_descent)* (SGD) algorithm. The `learning_rate` sets the step size to take for each iteration down the hill. This is a *hyperparameter* that you'll commonly adjust to achieve better results."
"TensorFlow has many [optimization algorithms](https://www.tensorflow.org/api_guides/python/train) available for training. This model uses the [tf.train.GradientDescentOptimizer](https://www.tensorflow.org/api_docs/python/tf/train/GradientDescentOptimizer) that implements the *[standard gradient descent](https://developers.google.com/machine-learning/crash-course/glossary#gradient_descent)* (SGD) algorithm. The `learning_rate` sets the step size to take for each iteration down the hill. This is a *hyperparameter* that you'll commonly adjust to achieve better results."
