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import tensorflow as tfData Manipulation - d2l.ai Exercises - Part 1
d2l.ai-exercises
deep-learning
tensorflow
The first notebook in a series to be posted aiming to solve and understand exercises from d2l.ai curriculum on deep learning
“Data Manipulation - d2l.ai Exercises - Part 1”
“The first notebook in a series to be posted aiming to solve and understand exercises from d2l.ai curriculum on deep learning”
This is the first notebook in a series to be posted aiming to solve and understand exercises from d2l.ai curriculum on deep learning, the corresponding lesson reference for this notebook is this link.
This series of practice notebook posts may use the exercises and content provided from d2l.ai, I write these to get a good hands-on practice in deep learning.
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tf.__version__'2.4.1'Setup for exercises
Problem 1
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X = tf.reshape(tf.range(12, dtype=tf.float32), (3, 4))
Y = tf.constant([[2.0, 1, 4, 3], [1, 2, 3, 4], [4, 3, 2, 1]])
X, Y(<tf.Tensor: shape=(3, 4), dtype=float32, numpy=
array([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]], dtype=float32)>,
<tf.Tensor: shape=(3, 4), dtype=float32, numpy=
array([[2., 1., 4., 3.],
[1., 2., 3., 4.],
[4., 3., 2., 1.]], dtype=float32)>)Problem 2
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a = tf.reshape(tf.range(3), (3, 1))
b = tf.reshape(tf.range(2), (1, 2))
a, b(<tf.Tensor: shape=(3, 1), dtype=int32, numpy=
array([[0],
[1],
[2]], dtype=int32)>,
<tf.Tensor: shape=(1, 2), dtype=int32, numpy=array([[0, 1]], dtype=int32)>)Solutions
Problem 1
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X == Y<tf.Tensor: shape=(3, 4), dtype=bool, numpy=
array([[False, True, False, True],
[False, False, False, False],
[False, False, False, False]])>Show code
# Replacing == with comparison operators
X < Y<tf.Tensor: shape=(3, 4), dtype=bool, numpy=
array([[ True, False, True, False],
[False, False, False, False],
[False, False, False, False]])>Show code
# Replacing == with comparison operators
X > Y<tf.Tensor: shape=(3, 4), dtype=bool, numpy=
array([[False, False, False, False],
[ True, True, True, True],
[ True, True, True, True]])>The operations are as expected of an elementwise comparison. Let’s try to check if the operations are opposites of each other by trying to not one of them.
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(X > Y) == tf.math.logical_not(X < Y)<tf.Tensor: shape=(3, 4), dtype=bool, numpy=
array([[ True, False, True, False],
[ True, True, True, True],
[ True, True, True, True]])>We can see that apart from two cases where the numbers were equal(1 and 3), all the other values matched
Problem 2
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# we need to change tensors a and b with 3-d tensors
a = tf.reshape(tf.range(16), (2, 4, -1))
b = tf.reshape(tf.range(16), (4, 2, -1))
a, b(<tf.Tensor: shape=(2, 4, 2), dtype=int32, numpy=
array([[[ 0, 1],
[ 2, 3],
[ 4, 5],
[ 6, 7]],
[[ 8, 9],
[10, 11],
[12, 13],
[14, 15]]], dtype=int32)>,
<tf.Tensor: shape=(4, 2, 2), dtype=int32, numpy=
array([[[ 0, 1],
[ 2, 3]],
[[ 4, 5],
[ 6, 7]],
[[ 8, 9],
[10, 11]],
[[12, 13],
[14, 15]]], dtype=int32)>)Show code
a + b--------------------------------------------------------------------------- InvalidArgumentError Traceback (most recent call last) <ipython-input-12-bd58363a63fc> in <module>() ----> 1 a + b /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py in binary_op_wrapper(x, y) 1162 with ops.name_scope(None, op_name, [x, y]) as name: 1163 try: -> 1164 return func(x, y, name=name) 1165 except (TypeError, ValueError) as e: 1166 # Even if dispatching the op failed, the RHS may be a tensor aware /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py in wrapper(*args, **kwargs) 199 """Call target, and fall back on dispatchers if there is a TypeError.""" 200 try: --> 201 return target(*args, **kwargs) 202 except (TypeError, ValueError): 203 # Note: convert_to_eager_tensor currently raises a ValueError, not a /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py in _add_dispatch(x, y, name) 1484 return gen_math_ops.add(x, y, name=name) 1485 else: -> 1486 return gen_math_ops.add_v2(x, y, name=name) 1487 1488 /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_math_ops.py in add_v2(x, y, name) 470 return _result 471 except _core._NotOkStatusException as e: --> 472 _ops.raise_from_not_ok_status(e, name) 473 except _core._FallbackException: 474 pass /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in raise_from_not_ok_status(e, name) 6860 message = e.message + (" name: " + name if name is not None else "") 6861 # pylint: disable=protected-access -> 6862 six.raise_from(core._status_to_exception(e.code, message), None) 6863 # pylint: enable=protected-access 6864 /usr/local/lib/python3.6/dist-packages/six.py in raise_from(value, from_value) InvalidArgumentError: Incompatible shapes: [2,4,2] vs. [4,2,2] [Op:AddV2]
I tried using tensors of 3d shapes, thinking it might but it did’nt, so I was searching about the rules to determine whether an array can be broadcasted or not and found this documentation, where the conditions are explained, the main points to consider broadcasting are, if the dimensions
are equal, or
one of them is 1
In the above case we hade shapes: [2,4,2] vs. [4,2,2], Let’s try a different shape
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a = tf.reshape(tf.range(12), (6, 2, -1))
b = tf.reshape(tf.range(16), (1, -1))
a, b(<tf.Tensor: shape=(6, 2, 1), dtype=int32, numpy=
array([[[ 0],
[ 1]],
[[ 2],
[ 3]],
[[ 4],
[ 5]],
[[ 6],
[ 7]],
[[ 8],
[ 9]],
[[10],
[11]]], dtype=int32)>, <tf.Tensor: shape=(1, 16), dtype=int32, numpy=
array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]],
dtype=int32)>)Show code
a + b<tf.Tensor: shape=(6, 2, 16), dtype=int32, numpy=
array([[[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
[ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]],
[[ 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17],
[ 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]],
[[ 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19],
[ 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]],
[[ 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21],
[ 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22]],
[[ 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],
[ 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]],
[[10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25],
[11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]]],
dtype=int32)>Similar to the examples in the link, the above example followed the rules and operation(addition) could happen with the help of broadcasting.
a - 6 X 2 X 1
b - 1 X 16
a + b - 6 X 2 X 16