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Chapter 23. Placeholders: Placeholders are used to feed values into a TensorFlow graph.They are used along with feed_dict to feed data. They are normally usedto feed new training examples while training a neural network. We assignvalues to a placeholder while running the graph in session. They allow us tocreate our operations and build the computational graph without requiringany data. An important detail to note is that placeholders do not contain anydata and thus there is no need to initialize them.Examples of operationsLet's see some examples of different operations available in TensorFlow 1.x.ConstantsFirst, let's look at some constants that we will encounter:• We can declare a scalar constant:t_1 = tf.constant(4)Example: A constant vector of shape [1,3]:t_2 = tf.constant([4, 3, 2])• To create a tensor with all elements as zero, we use tf.zeros(). Thisstatement creates a matrix of zeros of shape [M,N] with dtype (int32,float32, and so on):tf.zeros([M,N],tf.dtype)Example: zero_t = tf.zeros([2,3],tf.int32) ==>[[0 0 0], [0 0 0]]• Get the shape of a tensor:Example: print(tf.zeros([2,3],tf.int32).shape) ==> (2, 3)• We can also create tensor variables of the same shape as an existing NumPyarray or tensor constant using:tf.zeros_like(t_2) # Create a zero matrix of same shape as t_2tf.ones_like(t_2) # Creates a ones matrix of same shape as t_2• We can create a tensor with all elements set to one; next, we create a onesmatrix of shape [M,N]:tf.ones([M,N],tf.dtype)Example: ones_t = tf.ones([2,3],tf.int32) ==>[[0 0 0], [0 0 0]][ 55 ]
TensorFlow 1.x and 2.x• We can broadcast in a similar way to how it's done with NumPy:Example: t = tf.Variable([[0., 1., 2.], [3., 4., 5.], [6., 7.,8]])print (t*2) ==>tf.Tensor([[ 0. 2. 4.][ 6. 8. 10.][12. 14. 16.]], shape=(3, 3), dtype=float32)Sequences• We can generate a sequence of evenly spaced vectors, starting from start toend, with a total num values:tf.linspace(start, stop, num) // The corresponding values differby (stop-start)/(num-1)Example: range_t = tf.linspace(2.0,5.0,5) ==> [ 2. 2.75 3.54.25 5. ]• Generate a sequence of numbers starting from start (default=0),incremented by delta (default=1) up to but not including the limit:tf.range(start,limit,delta)Example: range_t = tf.range(10) ==> [0 1 2 3 4 5 6 7 8 9]Random tensorsTensorFlow allows random tensors with different distributions to be created:• To create random values from a normal distribution of shape [M,N], withmean (default =0.0), standard deviation stddev (default=1.0), and using seed,we can use:t_random = tf.random_normal([2,3], mean=2.0, stddev=4, seed=12)==> [[ 0.25347459 5.37990952 1.95276058], [-1.537603141.2588985 2.84780669]]• To create random values from a truncated normal distribution of shape[M,N] with mean (default =0.0), standard deviation stddev (default=1.0),and using seed, we can use:t_random = tf.truncated_normal([1,5], stddev=2, seed=12) ==> [[-0.8732627 1.68995488 -0.02361972 -1.76880157 -3.87749004]][ 56 ]
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- Page 86 and 87: TensorFlow 1.x and 2.xThe intent of
- Page 88 and 89: An example to start withWe'll consi
- Page 92 and 93: • To create random values from a
- Page 94 and 95: To know the value, we need to creat
- Page 96 and 97: Chapter 2Both PyTorch and TensorFlo
- Page 98 and 99: Chapter 2state = [tf.zeros([100, 10
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- Page 102 and 103: Chapter 2Let's see an example of a
- Page 104 and 105: Chapter 2If you want to save a mode
- Page 106 and 107: Chapter 2supervised=True)train_data
- Page 108 and 109: Chapter 2There, tf.feature_column.n
- Page 110 and 111: Chapter 2print (dz_dx)print (dy_dx)
- Page 112 and 113: Chapter 2In our toy example we use
- Page 114 and 115: Chapter 2For multi-machine training
- Page 116 and 117: Chapter 25. Use tf.layers modules t
- Page 118 and 119: Chapter 2Keras or tf.keras?Another
- Page 120: • tf.data can be used to load mod
- Page 123 and 124: RegressionLet us imagine a simpler
- Page 125 and 126: RegressionTake a look at the last t
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TensorFlow 1.x and 2.x
• We can broadcast in a similar way to how it's done with NumPy:
Example: t = tf.Variable([[0., 1., 2.], [3., 4., 5.], [6., 7.,
8]])
print (t*2) ==>
tf.Tensor(
[[ 0. 2. 4.]
[ 6. 8. 10.]
[12. 14. 16.]], shape=(3, 3), dtype=float32)
Sequences
• We can generate a sequence of evenly spaced vectors, starting from start to
end, with a total num values:
tf.linspace(start, stop, num) // The corresponding values differ
by (stop-start)/(num-1)
Example: range_t = tf.linspace(2.0,5.0,5) ==> [ 2. 2.75 3.5
4.25 5. ]
• Generate a sequence of numbers starting from start (default=0),
incremented by delta (default=1) up to but not including the limit:
tf.range(start,limit,delta)
Example: range_t = tf.range(10) ==> [0 1 2 3 4 5 6 7 8 9]
Random tensors
TensorFlow allows random tensors with different distributions to be created:
• To create random values from a normal distribution of shape [M,N], with
mean (default =0.0), standard deviation stddev (default=1.0), and using seed,
we can use:
t_random = tf.random_normal([2,3], mean=2.0, stddev=4, seed=12)
==> [[ 0.25347459 5.37990952 1.95276058], [-1.53760314
1.2588985 2.84780669]]
• To create random values from a truncated normal distribution of shape
[M,N] with mean (default =0.0), standard deviation stddev (default=1.0),
and using seed, we can use:
t_random = tf.truncated_normal([1,5], stddev=2, seed=12) ==> [[-
0.8732627 1.68995488 -0.02361972 -1.76880157 -3.87749004]]
[ 56 ]
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