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Tensors may have more axes; here is a tensor with three axes: # There can be an arbitrary number of

All tensors are immutable like Python numbers and strings: you can never update the contents of a tensor, only create a new one. Basics Unicode chars: tf.Tensor([b'\xe3\x82\xa2' b'\xe3\x83\x92' b'\xe3\x83\xab' b'' b'\xf0\x9f\xa6\x86'], shape=(5,), dtype=string) You can reshape a tensor into a new shape. The tf.reshape operation is fast and cheap as the underlying data does not need to be duplicated. # You can reshape a tensor to a new shape. In the above printout the b prefix indicates that tf.string dtype is not a unicode string, but a byte-string. See the Unicode Tutorial for more about working with unicode text in TensorFlow.There are many ways you might visualize a tensor with more than two axes. A 3-axis tensor, shape: [3, 2, 5] The tf.keras.layers.Layer and tf.keras.Model classes build on tf.Module providing additional functionality and convenience methods for building, training, and saving models. Some of these are demonstrated in the next section.

The strings are atomic and cannot be indexed the way Python strings are. The length of the string is not one of the axes of the tensor. See tf.strings for functions to manipulate them. The chart below shows the last few trades taken (white is entry 1 and orange is entry 2) the large numbers are the results since March 1 2018. Unicode bytes: tf.Tensor(b'\xe3\x82\xa2\xe3\x83\x92\xe3\x83\xab \xf0\x9f\xa6\x86', shape=(), dtype=string) I also want to thank "MikeJody". He probably doesn't know this but he is the reason for my curiosity to take what he originally posted and analyze this indicator to the next level. You can do basic math on tensors, including addition, element-wise multiplication, and matrix multiplication. a = tf.constant([[1, 2],The derivative of y is y' = f'(x) = (2*x + 2) = 4. TensorFlow can calculate this automatically: with tf.GradientTape() as tape: You can export these graphs, using tf.saved_model, to run on other systems like a server or a mobile device, no Python installation required. I also want to thank "Matsu" for his help with the 4TF HAS indicator that is the heart of this strategy. tf.Module is a class for managing your tf.Variable objects, and the tf.function objects that operate on them. The tf.Module class is necessary to support two significant features: