cifar10/general_child.py shape fix for tf1.5 and higher resolves melo…

…dyguan#4 melodyguan#29 taken from melodyguan@ad9ec6c

src/cifar10/general_child.py

@@ -148,7 +148,7 @@ def _factorized_reduction(self, x, out_filters, stride, is_training):
       w = create_weight("w", [1, 1, inp_c, out_filters // 2])
       path1 = tf.nn.conv2d(path1, w, [1, 1, 1, 1], "SAME",
                            data_format=self.data_format)
-  
+
     # Skip path 2
     # First pad with 0"s on the right and bottom, then shift the filter to
     # include those 0"s that were added.
@@ -160,15 +160,15 @@ def _factorized_reduction(self, x, out_filters, stride, is_training):
       pad_arr = [[0, 0], [0, 0], [0, 1], [0, 1]]
       path2 = tf.pad(x, pad_arr)[:, :, 1:, 1:]
       concat_axis = 1
-  
+
     path2 = tf.nn.avg_pool(
         path2, [1, 1, 1, 1], stride_spec, "VALID", data_format=self.data_format)
     with tf.variable_scope("path2_conv"):
       inp_c = self._get_C(path2)
       w = create_weight("w", [1, 1, inp_c, out_filters // 2])
       path2 = tf.nn.conv2d(path2, w, [1, 1, 1, 1], "SAME",
                            data_format=self.data_format)
-  
+
     # Concat and apply BN
     final_path = tf.concat(values=[path1, path2], axis=concat_axis)
     final_path = batch_norm(final_path, is_training,
@@ -291,13 +291,14 @@ def _enas_layer(self, layer_id, prev_layers, start_idx, out_filters, is_training
           y = self._pool_branch(inputs, is_training, out_filters, "max",
                                 start_idx=0)
         branches[tf.equal(count, 5)] = lambda: y
-      out = tf.case(branches, default=lambda: tf.constant(0, tf.float32),
-                    exclusive=True)
 
       if self.data_format == "NHWC":
-        out.set_shape([None, inp_h, inp_w, out_filters])
+        out_shape = [self.batch_size, inp_h, inp_w, out_filters]
       elif self.data_format == "NCHW":
-        out.set_shape([None, out_filters, inp_h, inp_w])
+        out_shape = [self.batch_size, out_filters, inp_h, inp_w]
+
+      out = tf.case(branches, default=lambda: tf.constant(0, tf.float32, shape=out_shape),
+                    exclusive=True)
     else:
       count = self.sample_arc[start_idx:start_idx + 2 * self.num_branches]
       branches = []