import tensorflow as tf
-input = tf.placeholder(tf.float32, [1, 3, 3, 5])
-filter = tf.constant(1.0, shape=[2, 2, 5, 1])
-conv = tf.nn.conv2d(input, filter=filter, strides=[1, 1, 1, 1], padding='SAME')
+input0 = tf.placeholder(tf.float32, [1, 3, 3, 5])
+filter0 = tf.constant(1.0, shape=[2, 2, 5, 1])
+conv = tf.nn.conv2d(input0, filter=filter0, strides=[1, 1, 1, 1], padding='SAME')
fbn = tf.nn.fused_batch_norm(
conv, scale=[1.0], offset=[0.0], mean=[0.0], variance=[1.0], is_training=False)
import tensorflow as tf
-input = tf.placeholder(tf.float32, [1, 3, 3, 5])
-filter = tf.constant(1.0, shape=[2, 2, 5, 2])
-dconv = tf.nn.depthwise_conv2d(input, filter, [1, 1, 1, 1], 'SAME')
+input0 = tf.placeholder(tf.float32, [1, 3, 3, 5])
+filter0 = tf.constant(1.0, shape=[2, 2, 5, 2])
+dconv = tf.nn.depthwise_conv2d(input0, filter0, [1, 1, 1, 1], 'SAME')
const = tf.constant(2.0, shape=[10])
fbn = tf.nn.fused_batch_norm(
x=dconv, scale=const, offset=const, mean=const, variance=const, is_training=False)