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"""
Explanation: Copyright 2019 The TensorFlow Authors.
End of explanation
"""


import tensorflow as tf
print("TensorFlow version:", tf.__version__)

"""
Explanation: TensorFlow 2 quickstart for beginners
<table class="tfo-notebook-buttons" align="left">
  <td>
    <a target="_blank" href="https://www.tensorflow.org/tutorials/quickstart/beginner"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a>
  </td>
  <td>
    <a target="_blank" href="https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/quickstart/beginner.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>
  </td>
  <td>
    <a target="_blank" href="https://github.com/tensorflow/docs/blob/master/site/en/tutorials/quickstart/beginner.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on GitHub</a>
  </td>
  <td>
    <a href="https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/quickstart/beginner.ipynb"><img src="https://www.tensorflow.org/images/download_logo_32px.png" />Download notebook</a>
  </td>
</table>

This short introduction uses Keras to:

Load a prebuilt dataset.
Build a neural network machine learning model that classifies images.
Train this neural network.
Evaluate the accuracy of the model.

This tutorial is a Google Colaboratory notebook. Python programs are run directly in the browser—a great way to learn and use TensorFlow. To follow this tutorial, run the notebook in Google Colab by clicking the button at the top of this page.

In Colab, connect to a Python runtime: At the top-right of the menu bar, select CONNECT.
Run all the notebook code cells: Select Runtime > Run all.

Set up TensorFlow
Import TensorFlow into your program to get started:
End of explanation
"""


mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

"""
Explanation: If you are following along in your own development environment, rather than Colab, see the install guide for setting up TensorFlow for development.
Note: Make sure you have upgraded to the latest pip to install the TensorFlow 2 package if you are using your own development environment. See the install guide for details.
Load a dataset
Load and prepare the MNIST dataset. Convert the sample data from integers to floating-point numbers:
End of explanation
"""


model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10)
])

"""
Explanation: Build a machine learning model
Build a tf.keras.Sequential model by stacking layers.
End of explanation
"""


predictions = model(x_train[:1]).numpy()
predictions

"""
Explanation: For each example, the model returns a vector of logits or log-odds scores, one for each class.
End of explanation
"""


tf.nn.softmax(predictions).numpy()

"""
Explanation: The tf.nn.softmax function converts these logits to probabilities for each class:
End of explanation
"""


loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)

"""
Explanation: Note: It is possible to bake the tf.nn.softmax function into the activation function for the last layer of the network. While this can make the model output more directly interpretable, this approach is discouraged as it's impossible to provide an exact and numerically stable loss calculation for all models when using a softmax output. 
Define a loss function for training using losses.SparseCategoricalCrossentropy, which takes a vector of logits and a True index and returns a scalar loss for each example.
End of explanation
"""


loss_fn(y_train[:1], predictions).numpy()

"""
Explanation: This loss is equal to the negative log probability of the true class: The loss is zero if the model is sure of the correct class.
This untrained model gives probabilities close to random (1/10 for each class), so the initial loss should be close to -tf.math.log(1/10) ~= 2.3.
End of explanation
"""


model.compile(optimizer='adam',
              loss=loss_fn,
              metrics=['accuracy'])

"""
Explanation: Before you start training, configure and compile the model using Keras Model.compile. Set the optimizer class to adam, set the loss to the loss_fn function you defined earlier, and specify a metric to be evaluated for the model by setting the metrics parameter to accuracy.
End of explanation
"""


model.fit(x_train, y_train, epochs=5)

"""
Explanation: Train and evaluate your model
Use the Model.fit method to adjust your model parameters and minimize the loss:
End of explanation
"""


model.evaluate(x_test,  y_test, verbose=2)

"""
Explanation: The Model.evaluate method checks the models performance, usually on a "Validation-set" or "Test-set".
End of explanation
"""


probability_model = tf.keras.Sequential([
  model,
  tf.keras.layers.Softmax()
])

probability_model(x_test[:5])

"""
Explanation: The image classifier is now trained to ~98% accuracy on this dataset. To learn more, read the TensorFlow tutorials.
If you want your model to return a probability, you can wrap the trained model, and attach the softmax to it:
End of explanation
"""


