f7715f63c17832f66e54164cc0ae3cb0e4afc4a3,cleverhans/attacks.py,Attack,generate_np,#Attack#Any#,64

Before Change

            if isinstance(value, np.ndarray):
                new_shape = [None]+list(value.shape[1:])
                new_kwargs[name] = tf.placeholder(value.dtype, new_shape)
            if any(isinstance(value, num) for num in num_types):
                if isinstance(value, float):
                    new_kwargs[name] = tf.placeholder(tf.float32, shape=[])
                elif isinstance(value, int):

After Change

            // create a unique key for this set of fixed paramaters
            hash_key = tuple(sorted(fixed.items()))

        if hash_key not in self.graphs:
            // try our very best to create a TF placeholder for each of the
            // feedable keyword arguments by inferring the type
            num_types = (int, float, np.float16, np.float32, np.float64,
                         np.int8, np.int16, np.int32, np.int32, np.int64, 
                         np.uint8, np.uint16, np.uint32, np.uint64)

            new_kwargs = dict(x for x in fixed.items())
            for name, value in feedable.items():
                if isinstance(value, np.ndarray):
                    new_shape = [None]+list(value.shape[1:])
                    new_kwargs[name] = tf.placeholder(value.dtype, new_shape)
                if isinstance(value, num_types):
                    if isinstance(value, float):
                        // can"t instantiate placeholder with python float
                        // cast it to tf.float32 as that"s most likely
                        new_kwargs[name] = tf.placeholder(tf.float32, shape=[])
                    elif isinstance(value, int):
                        // can"t instantiate placeholder with python int
                        // cast it to tf.int32 as that"s most likely
                        new_kwargs[name] = tf.placeholder(tf.int32, shape=[])
                    else:
                        new_kwargs[name] = tf.placeholder(type(value), shape=[])

            // x is a special placeholder we always want to have
            x = tf.placeholder(tf.float32, shape=[None]+list(x_val.shape)[1:])

            // now we generate the graph that we want
            x_adv = self.generate(x, **new_kwargs)

            if hash_key is not None:
                // only save the graph if every fixed element is hashable
                self.graphs[hash_key] = (x, new_kwargs, x_adv)

            if len(self.graphs) == 10:
                warnings.warn("Calling generate_np() with multiple different "
                              "structural paramaters is inefficient and should "
                              "be avoided. Calling generate() is preferred.")

        if hash_key is not None:
            // if it is None, we must have constructed it already
            x, new_kwargs, x_adv = self.graphs[hash_key]
            

Instances