Clarifying initializers description: notebook save failed in previous commit.
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"TensorFlow allows complex computation graphs (also known as data flow graphs in TensorFlow parlance) to be defined via a Python interface, with efficient C++ implementations for running the corresponding operations on different devices. TensorFlow also includes tools for automatic gradient computation and a large and growing suite of pre-define operations useful for gradient-based training of machine learning models.\n",
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"In this notebook we will introduce some of the basic elements of constructing, training and evaluating models with TensorFlow. This will use similar material to some of the [official TensorFlow tutorials](https://www.tensorflow.org/tutorials/) but with an additional emphasis of making links to the material covered in this course last semester. For those who have not used a computational graph framework such as TensorFlow or Theano before you may find the [basic usage tutorial](https://www.tensorflow.org/get_started/basic_usage) useful to go through\n",
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"In this notebook we will introduce some of the basic elements of constructing, training and evaluating models with TensorFlow. This will use similar material to some of the [official TensorFlow tutorials](https://www.tensorflow.org/tutorials/) but with an additional emphasis of making links to the material covered in this course last semester. For those who have not used a computational graph framework such as TensorFlow or Theano before you may find the [basic usage tutorial](https://www.tensorflow.org/get_started/basic_usage) useful to go through.\n",
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"### Installing TensorFlow\n",
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"A standard operation which needs to be called before any other operations on a graph which includes variable nodes is a variable *initializer* operation. This, as the name suggests, initialises the values of the variables in the session to the values defined by the `initial_value` argument when adding the variables to the graph. For instance for the graph we have defined here this will initialise the `weights` variable value in the session to a 2D array of zeros of shape `(784, 10)` and the `biases` variable to a 1D array of shape `(10,)`.\n",
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"We can create initializer ops for each variable individually using the `initializer` method of the variables in question and then individually run these, however a common pattern is to use the `tf.global_variables_initializer()` function to create a single initializer op which will initialise all globally defined variables in the default graph and then run this as done below."
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"We can access initializer ops for each variable individually using the `initializer` property of the variables in question and then individually run these, however a common pattern is to use the `tf.global_variables_initializer()` function to create a single initializer op which will initialise all globally defined variables in the default graph and then run this as done below."
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