Why do we need tensorflow? Why are people crazy about it? In a way, it is lazy computing and offers flexibility in the way you run your code. What is this thing with flexbility and laze computing? We are glad, you asked!
Lazy Computing: TensorFlow is a way of representing computation without actually performing it until asked.
The first step to learn Tensorflow is to understand its main key feature, the "computational graph" approach. Basically, all Tensorflow codes contain two important parts:. First step you create the graph i. Flexibility: When you create a graph, you are not bound to run the whole graph and can control the parts of the graph that are executed separately. This provides a huge flexibility with your models. Bonus: One of the biggest advantages of TensorFlow is its visualizations of the computation graph.
Its called TensorBoard and will be discussed in future. Now that we have discussed what and why about TensorFlow, lets dive in to the actual thing. TensorFlow separates the definition of computations from their execution. These two parts are explained in more detail in the following sections. Before that, remember that the first step is to import the Tensorflow library! This gives Python access to all of TensorFlow's classes, methods, and symbols. Using this command, TensorFlow library will be imported under the alias tf so that later we can use it instead of typing the whole term tensorflow each time.
What is a Tensor?
What is a TensorFlow Session?
TensorFlow programs use a data structure called tensor to represent all the data. Any type of data you plan to use for your model can be stored in Tensors. Simply put, a Tensor is a multi-dimensional array 0-D tensor: scalar, 1-D tensor: vector, 2-D tensor: matrix, and so on. Hence, TensorFlow is simply referring to the flow of the Tensors in the computational graph.
The biggest idea about Tensorflow is that all the numerical computations are expressed as a computational graph. In other words, the backbone of any Tensorflow program is a Graph. Anything that happens in your model is represented by the computational graph.Inside TensorFlow: Functions, not sessions
This makes it, the to go place for anything related to your model.Run Operation s and evaluate Tensors. A Runner runs the necessary graph fragments to execute every Operation required to evaluate the Tensors to fetch. Make run execute operationbut not return any evaluated Tensors. Make run execute operandbut not return any evaluated Tensors. Use t instead of the Tensor referred to by executing the operation referred to by operand. Avoid evaluating operation and substitute t for the value it produces.
Avoid evaluating the index -th output of operation by substituting t for the value it produces. Operations in a Graph can have multiple outputs, index identifies which one t is being provided for.
Make run return the output of operation. Make run return the index -th output of operation. Operations in a Graph can have multiple outputs, index identifies which one to return. Makes run return the Tensor referred to by the output of operand. Makes run return the Tensor referred to by output.
Execute the graph fragments necessary to compute all requested fetches. TODO ashankar : Reconsider the return type here. Two things in particular: a Make it easier for the caller to cleanup perhaps returning something like AutoCloseableList in SessionTest. TODO andrewmyers : It would also be good if whatever is returned here made it easier to extract output tensors in a type-safe way.
Execute graph fragments to compute requested fetches and return metadata about the run. This is exactly like runbut in addition to the requested Tensors, also returns metadata about the graph execution in the form of a serialized RunMetadata protocol buffer. Experimental method : set options typically for debugging for this run.
The options are presented as a serialized RunOptions protocol buffer. The org. A cost of that is this lack of type-safety in this API function.A Session object encapsulates the environment in which Operation objects are executed, and Tensor objects are evaluated.
For example:. A session may own resources, such as tf. Variabletf. QueueBaseand tf. It is important to release these resources when they are no longer required. To do this, either invoke the tf. The following two examples are equivalent:.
The ConfigProto protocol buffer exposes various configuration options for a session. For example, to create a session that uses soft constraints for device placement, and log the resulting placement decisions, create a session as follows:. The execution engine to connect to. Defaults to using an in-process engine.
See Distributed TensorFlow for more examples.
The Graph to be launched described above. A ConfigProto protocol buffer with configuration options for the session. View source.
Use with the with keyword to specify that calls to tf.
To get the current default session, use tf. Alternatively, you can use with tf. Session : to create a session that is automatically closed on exiting the context, including when an uncaught exception is raised.
Returns A context manager using this session as the default session.
OpError Or one of its subclasses if an error occurs while closing the TensorFlow session. OpError If it encounters an error e. Returns A list of devices in the session. Tensorthe i th argument to the returned callable must be a numpy ndarray or something convertible to an ndarray with matching element type and shape.A Session object encapsulates the environment in which Operation objects are executed, and Tensor objects are evaluated.
For example:. A session may own resources, such as tf. Variabletf. QueueBaseand tf. It is important to release these resources when they are no longer required. To do this, either invoke the tf. The following two examples are equivalent:. The ConfigProto protocol buffer exposes various configuration options for a session. For example, to create a session that uses soft constraints for device placement, and log the resulting placement decisions, create a session as follows:.
GraphDef proto containing nodes for all of the Operations in the underlying TensorFlow graph. If no graph argument is specified when constructing the session, the default graph will be launched in the session. If you are using more than one graph created with tf. Graph in the same process, you will have to use different sessions for each graph, but each graph can be used in multiple sessions.
In this case, it is often clearer to pass the graph to be launched explicitly to the session constructor. Use with the with keyword to specify that calls to tf. To get the current default session, use tf.
Alternatively, you can use with tf. Session : to create a session that is automatically closed on exiting the context, including when an uncaught exception is raised. The default session is a property of the current thread. If you create a new thread, and wish to use the default session in that thread, you must explicitly add a with sess. Entering a with sess. If you are using multiple graphs, and sess. Each element in the list has the following properties: - name : A string with the full name of the device.
Note: depending on the device, it is possible the usable memory could be substantially less. Tensorthe i th argument to the returned callable must be a numpy ndarray or something convertible to an ndarray with matching element type and shape.
See tf. The returned callable will have the same return type as tf. For example, if fetches is a tf. Tensorthe callable will return a numpy ndarray; if fetches is a tf. Operationit will return None. See run for more information. Either a single value if fetches is a single graph element, or a list of values if fetches is a list, or a dictionary with the same keys as fetches if that is a dictionary see documentation for run.Graph and tf.
Session in TensorFlow. Update Sessions are gone in TensorFlow 2. There is one global runtime in the background that executes all computation, whether run eagerly or as a compiled tf. We define a graph with a variable and three operations: variable returns the current value of our variable. The default graph is also what the sessions in the next section use when not manually specifying a graph. To run any of the three defined operations, we need to create a session for that graph.
The session will also allocate memory to store the current value of the variable. As you can see, the value of our variable is only valid within one session. If we try to query the value afterwards in a second session, TensorFlow will raise an error because the variable is not initialized there.
Of course, we can use the graph in more than one session, we just have to initialize the variables again. The values in the new session will be completely independent from the first one:.
Hopefully this short workthrough helped you to better understand tf. Feel free to ask questions in the comments. Updated The operation to initialize variables changed in TensorFlow 1. A session allows to execute graphs or part of graphs. It allocates resources on one or more machines for that and holds the actual values of intermediate results and variables. Defining the Graph We define a graph with a variable and three operations: variable returns the current value of our variable. Graph with graph.TensorFlow is an end-to-end open source platform for machine learning.
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Easily train and deploy models in the cloud, on-prem, in the browser, or on-device no matter what language you use. A simple and flexible architecture to take new ideas from concept to code, to state-of-the-art models, and to publication faster. Train a neural network to classify images of clothing, like sneakers and shirts, in this fast-paced overview of a complete TensorFlow program. Train a generative adversarial network to generate images of handwritten digits, using the Keras Subclassing API.
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We post regularly to the TensorFlow Blog, with content from the TensorFlow team and the best articles from the community. For up-to-date news and updates from the community and the TensorFlow team, follow tensorflow on Twitter.Driver for Graph execution. A Session instance encapsulates the environment in which Operation s in a Graph are executed to compute Tensors.
Instances of a Session are thread-safe. Construct a new session with the associated Graph. Construct a new session with the associated Graph and configuration options. Release resources associated with the Session. Blocks until there are no active executions run calls. A Session is not usable after close returns. Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4. For details, see the Google Developers Site Policies. Some content is licensed under the numpy license.
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