The first example where I just initiate a constant Tensor of random numbers run approximately the same time irrespectibly of dim ( 0:00:00.003261) startTime = datetime.now() Take a look at this example: import tensorflow as tf I have not found this in documentation, but I believe that the values of the variables (and some of the constants are not calculated at the time of assignment). type of elements in the tensor ( float32).type of the operation that will be used to generate the tensor ( transpose_1:0, random_uniform:0).the dimensionality of the tensor (but I assume it is not hard to calculate it for the list of the operations that TF has).No, you can not see the content of the tensor without running the graph (doing n()). : You might be able to use the tf.get_static_value() function to get the constant value of the given tensor if its value is efficiently calculable. It will not write to the notebook kernel's console logs. In Jupyter notebooks and colabs, tf.print prints to the notebook cell outputs. To make sure the operator runs, users need to pass the produced op to tf.compat.v1.Session's run method, or to use the op as a control dependency for executed ops by specifying with tf.compat.v1.control_dependencies(), which is printed to standard output. : To print the value of a tensor without returning it to your Python program, you can use the tf.print() operator, as Andrzej suggests in another answer. executing independent parts in parallel and using GPUs). This might seem silly for such a small expression, but one of the key ideas in Tensorflow 1.x is deferred execution: it's very cheap to build a large and complex expression, and when you want to evaluate it, the back-end (to which you connect with a Session) is able to schedule its execution more efficiently (e.g. For example, the following works in a Jupyter notebook: with tf.Session() as sess: print(product.eval()) This can be easier in an interactive setting, such as the shell or an IPython notebook, when it's tedious to pass around a Session object everywhere. If you are experimenting with the programming model, and want an easy way to evaluate tensors, the tf.InteractiveSession lets you open a session at the start of your program, and then use that session for all Tensor.eval() (and n()) calls.
#TENSORFLOW BOARD VIEW VALUES CODE#
In general, you cannot print the value of a tensor without running some code in a session. in a with tf.Session(): block, or see below). The easiest way to evaluate the actual value of a Tensor object is to pass it to the n() method, or call Tensor.eval() when you have a default session (i.e.
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