Let's say I have following code:
x = tf.placeholder("float32", shape=[None, ins_size**2*3], name = "x_input") condition = tf.placeholder("int32", shape=[1, 1], name = "condition") W = tf.Variable(tf.zeros([ins_size**2*3,label_option]), name = "weights") b = tf.Variable(tf.zeros([label_option]), name = "bias") if condition > 0: y = tf.nn.softmax(tf.matmul(x, W) + b) else: y = tf.nn.softmax(tf.matmul(x, W) - b)
Would the if
statement work in the calculation (I do not think so)? If not, how can I add an if
statement into the TensorFlow calculation graph?
You're correct that the if
statement doesn't work here, because the condition is evaluated at graph construction time, whereas presumably you want the condition to depend on the value fed to the placeholder at runtime. (In fact, it will always take the first branch, because condition > 0
evaluates to a Tensor
, which is "truthy" in Python.)
To support conditional control flow, TensorFlow provides the tf.cond()
operator, which evaluates one of two branches, depending on a boolean condition. To show you how to use it, I'll rewrite your program so that condition
is a scalar tf.int32
value for simplicity:
x = tf.placeholder(tf.float32, shape=[None, ins_size**2*3], name="x_input") condition = tf.placeholder(tf.int32, shape=[], name="condition") W = tf.Variable(tf.zeros([ins_size**2 * 3, label_option]), name="weights") b = tf.Variable(tf.zeros([label_option]), name="bias") y = tf.cond(condition > 0, lambda: tf.matmul(x, W) + b, lambda: tf.matmul(x, W) - b)
TF 2.0 introduces a feature called AutoGraph which lets you JIT compile python code into Graph executions. This means you can use python control flow statements (yes, this includes if
statements). From the docs,
AutoGraph supports common Python statements like
while
,for
,if
,break
,continue
andreturn
, with support for nesting. That means you can use Tensor expressions in the condition ofwhile
andif
statements, or iterate over a Tensor in afor
loop.
You will need to define a function implementing your logic and annotate it with tf.function
. Here is a modified example from the documentation:
import tensorflow as tf @tf.function def sum_even(items): s = 0 for c in items: if tf.equal(c % 2, 0): s += c return s sum_even(tf.constant([10, 12, 15, 20])) # <tf.Tensor: id=1146, shape=(), dtype=int32, numpy=42>
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