Tensor Operation Properties

Neural Network Tutorials - Herong's Tutorial Examples

∟Tensor Operation Properties

This section provides a tutorial example on how to view tensor operation properties by calling type(x), str(x), x.__dict__ and x.get_shape().

Once a tensor operation, x, is created as object instance, we can explore its properties in different ways:

1. type(x) - Global static method returning the exact class name that the instance was created from:

>>> import tensorflow as tf
>>> a = tf.constant([10])
>>> b = tf.Variable([20])
>>> c = tf.placeholder(tf.int32,1)
>>> o = tf.add(a,b)

>>> type(a)
<class 'tensorflow.python.framework.ops.Tensor'>

>>> type(b)
<class 'tensorflow.python.ops.variables.RefVariable'>

>>> type(c)
<class 'tensorflow.python.framework.ops.Tensor'>

>>> type(o)
<class 'tensorflow.python.framework.ops.Tensor'>

As you can see, the "Variable" operation, b, was instantiated from a different flavor of "ops" class. Its behavior will probably be different too. We will see it later.

2. str(x) - Global static method returning the string that summaries the instance:

>>> str(a)
'Tensor("Const:0", shape=(1,), dtype=int32)'

>>> str(b)
"<tf.Variable 'Variable:0' shape=(1,) dtype=int32_ref>"

>>> str(c)
'Tensor("Placeholder:0", shape=(1,), dtype=int32)'

>>> str(x)
'Tensor("Add:0", shape=(1,), dtype=int32)'

Note that the string summary format of the "Variable" operation, b, is slightly different. But it has the same information of instance name, output tensor shape, and tensor element type.

3. x.__dict__ - Instance dictionary property returning other instance properties;

>>> a.__dict__
{'_op': <tf.Operation 'Const' type=Const>,
 '_value_index': 0,
 '_dtype': tf.int32,
 '_tf_output': <tensorflow.python.pywrap_tensorflow_internal...,
 '_shape_val': TensorShape([Dimension(1)]),
 '_consumers': [],
 '_id': 9,
 '_name': 'Const:0'
}

>>> b.__dict__
{'_in_graph_mode': True,
 '_graph_key': 'grap-key-0/',
 '_synchronization': <VariableSynchronization.AUTO: 0>,
 '_aggregation': <VariableAggregation.NONE: 0>,
 '_trainable': True,
 '_initial_value': <tf.Tensor 'Variable/initial_value:0' ...,
 '_variable': <tf.Tensor 'Variable:0' shape=(1,) dtype=int32_ref>,
 '_initializer_op': <tf.Operation 'Variable/Assign' type=Assign>,
 '_snapshot': <tf.Tensor 'Variable/read:0' shape=(1,) dtype=int32>,
 '_caching_device': None,
 '_save_slice_info': None,
 '_constraint': None
}

>>> c.__dict__
{'_op': <tf.Operation 'Placeholder' type=Placeholder>,
 '_value_index': 0,
 '_dtype': tf.int32,
 '_tf_output': <tensorflow.python.pywrap_tensorflow_internal...,
 '_shape_val': TensorShape([Dimension(1)]),
 '_consumers': [],
 '_id': 17,
 '_name': 'Placeholder_2:0'
}

>>> o.__dict__
{'_op': <tf.Operation 'Add' type=Add>,
 '_value_index': 0,
 '_dtype': tf.int32,
 '_tf_output': None,
 '_shape_val': None,
 '_consumers': [],
 '_id': 18,
 '_name': None
}

4. x.get_shap() - Instance method returning the output tensor shape of the instance. They are all 1 dimension of size 1.

>>> a.get_shape()
TensorShape([Dimension(1)])

>>> b.get_shape()
TensorShape([Dimension(1)])

>>> c.get_shape()
TensorShape([Dimension(1)])

>>> o.get_shape()
TensorShape([Dimension(1)])