Remove ">>> _ =" These are unnecessary.
The doctest system no longer checks outputs if the author doesn't supply one. PiperOrigin-RevId: 305055550 Change-Id: I424110500a15a8c79bf0082318c0a7d9af7fe608
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@ -620,14 +620,14 @@ class Tensor(tensor_like.TensorLike):
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The shape inference functions propagate shapes to the extent possible:
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>>> _ = my_matmul.get_concrete_function(
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>>> f = my_matmul.get_concrete_function(
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... tf.TensorSpec([None,3]),
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... tf.TensorSpec([3,5]))
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Result shape: (None, 5)
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Tracing may fail if a shape missmatch can be detected:
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>>> _ = my_matmul.get_concrete_function(
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>>> cf = my_matmul.get_concrete_function(
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... tf.TensorSpec([None,3]),
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... tf.TensorSpec([4,5]))
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Traceback (most recent call last):
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@ -647,7 +647,7 @@ class Tensor(tensor_like.TensorLike):
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... print("Result shape: ", a.shape)
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... return a
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>>> _ = my_fun.get_concrete_function(
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>>> cf = my_fun.get_concrete_function(
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... tf.TensorSpec([None, None]))
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Result shape: (5, 5)
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@ -712,7 +712,7 @@ class Tensor(tensor_like.TensorLike):
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Trace the function, see the [Concrete Functions
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Guide](https://www.tensorflow.org/guide/concrete_function) for details.
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>>> _ = load_image.get_concrete_function(
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>>> cf = load_image.get_concrete_function(
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... tf.TensorSpec([], dtype=tf.string))
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Initial shape: (None, None, 3)
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Final shape: (28, 28, 3)
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@ -3296,11 +3296,11 @@ _VALUE_SET_CODE_STRING = """
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>>> v = tf.Variable(1.)
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>>> _ = v.assign(2.)
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>>> v.assign(2.)
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>>> print(v.numpy())
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2.0
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>>> _ = v.assign_add(1.)
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>>> v.assign_add(1.)
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>>> print(v.numpy())
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3.0"""[3:] # Prune first newline and indent to match the docstring template.
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@ -416,7 +416,7 @@ class Model(network.Network, version_utils.ModelVersionSelector):
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>>> x = np.random.random((2, 3))
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>>> y = np.random.randint(0, 2, (2, 2))
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>>> _ = model.fit(x, y, verbose=0)
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>>> model.fit(x, y, verbose=0)
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>>> [m.name for m in model.metrics]
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['loss', 'mae']
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@ -429,7 +429,7 @@ class Model(network.Network, version_utils.ModelVersionSelector):
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>>> model.add_metric(
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... tf.reduce_sum(output_2), name='mean', aggregation='mean')
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>>> model.compile(optimizer="Adam", loss="mse", metrics=["mae", "acc"])
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>>> _ = model.fit(x, (y, y), verbose=0)
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>>> model.fit(x, (y, y), verbose=0)
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>>> [m.name for m in model.metrics]
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['loss', 'out_loss', 'out_1_loss', 'out_mae', 'out_acc', 'out_1_mae',
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'out_1_acc', 'mean']
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