make everything approxhpvm compatible

llvm/projects/keras/src/mobilenetv2_cifar10.py

@@ -55,24 +55,22 @@ def _inverted_res_block(inputs, expansion, stride, alpha, filters, block_id):
 
     # Expand
     if block_id:
-        x = Conv2D(expansion * in_channels, kernel_size=1, strides=1, padding='same', use_bias=False, activation=None, kernel_initializer="he_normal", kernel_regularizer=regularizers.l2(4e-5), name=prefix + 'expand')(x)
-        x = BatchNormalization(epsilon=1e-3, momentum=0.999, name=prefix + 'expand_BN')(x)
-        x = ReLU(6., name=prefix + 'expand_relu')(x)
-    else:
-        prefix = 'expanded_conv_'
+        x = Conv2D(expansion * in_channels, kernel_size=1, strides=1, padding='same', use_bias=False, activation=None, kernel_initializer="he_normal", kernel_regularizer=regularizers.l2(4e-5))(x)
+        x = BatchNormalization(epsilon=1e-3, momentum=0.999)(x)
+        x = Activation('relu')(x)
 
     # Depthwise
-    x = DepthwiseConv2D(kernel_size=3, strides=stride, activation=None, use_bias=False, padding='same', kernel_initializer="he_normal", depthwise_regularizer=regularizers.l2(4e-5), name=prefix + 'depthwise')(x)
-    x = BatchNormalization(epsilon=1e-3, momentum=0.999, name=prefix + 'depthwise_BN')(x)
-    x = ReLU(6., name=prefix + 'depthwise_relu')(x)
+    x = DepthwiseConv2D(kernel_size=3, strides=stride, activation=None, use_bias=False, padding='same', kernel_initializer="he_normal", depthwise_regularizer=regularizers.l2(4e-5))(x)
+    x = BatchNormalization(epsilon=1e-3, momentum=0.999)(x)
+    x = Activation('relu')(x)
 
     # Project
-    x = Conv2D(pointwise_filters, kernel_size=1, strides=1, padding='same', use_bias=False, activation=None, kernel_initializer="he_normal", kernel_regularizer=regularizers.l2(4e-5), name=prefix + 'project')(x)
-    x = BatchNormalization(epsilon=1e-3, momentum=0.999, name=prefix + 'project_BN')(x)
+    x = Conv2D(pointwise_filters, kernel_size=1, strides=1, padding='same', use_bias=False, activation=None, kernel_initializer="he_normal", kernel_regularizer=regularizers.l2(4e-5))(x)
+    x = BatchNormalization(epsilon=1e-3, momentum=0.999)(x)
 
 
     if in_channels == pointwise_filters and stride == 1:
-        return Add(name=prefix + 'add')([inputs, x])
+        return Add()([inputs, x])
     return x
 
 # build MobileNetV2 models
@@ -84,9 +82,9 @@ def get_mobilenetv2(alpha=1.0, depth_multiplier=1):
     img_input = Input(shape=input_shape)
 
     # model architechture
-    x = Conv2D(first_block_filters, kernel_size=3, strides=1, padding='same', use_bias=False, kernel_initializer="he_normal", kernel_regularizer=regularizers.l2(4e-5), name='Conv1')(img_input)
-    #x = BatchNormalization(epsilon=1e-3, momentum=0.999, name='bn_Conv1')(x)
-    #x = ReLU(6., name='Conv1_relu')(x)
+    x = Conv2D(first_block_filters, kernel_size=3, strides=1, padding='same', use_bias=False, kernel_initializer="he_normal", kernel_regularizer=regularizers.l2(4e-5))(img_input)
+    #x = BatchNormalization(epsilon=1e-3, momentum=0.999)(x)
+    #x = Activation('relu')(x)
 
     x = _inverted_res_block(x, filters=16,  alpha=alpha, stride=1, expansion=1, block_id=0 )
 
@@ -123,9 +121,9 @@ def get_mobilenetv2(alpha=1.0, depth_multiplier=1):
         last_block_filters = 1280
 
 
-    x = Conv2D(last_block_filters, kernel_size=1, use_bias=False, kernel_initializer="he_normal", kernel_regularizer=regularizers.l2(4e-5), name='Conv_1')(x)
-    x = BatchNormalization(epsilon=1e-3, momentum=0.999, name='Conv_1_bn')(x)
-    x = ReLU(6., name='out_relu')(x)
+    x = Conv2D(last_block_filters, kernel_size=1, use_bias=False, kernel_initializer="he_normal", kernel_regularizer=regularizers.l2(4e-5))(x)
+    x = BatchNormalization(epsilon=1e-3, momentum=0.999)(x)
+    x = Activation('relu')(x)
     
 
     x = AveragePooling2D(pool_size=2)(x)