ResNet101V2、ResNet152V2模型复现

经典网络/ResNetV2/ResNet50.py

@@ -3,7 +3,8 @@ import tensorflow.keras.layers as layers
 from tensorflow.keras.models import Model
 from plot_model import plot_model
 
-"""A residual block.
+"""
+A residual block.
   Arguments:
       x: input tensor.
       filters: integer, filters of the bottleneck layer.
@@ -14,9 +15,7 @@ from plot_model import plot_model
       name: string, block label.
   Returns:
     Output tensor for the residual block.
-  """
-
-
+"""
 def block2(x, filters, kernel_size=3, stride=1, conv_shortcut=False, name=None):
     preact = layers.BatchNormalization(name=name + '_preact_bn')(x)
     preact = layers.Activation('relu', name=name + '_preact_relu')(preact)
@@ -183,4 +182,6 @@ if __name__ == '__main__':
 
     model152=ResNet152V2(input_shape=(224,224,3))
     model152.summary()
-    # plot_model(model, to_file='../img/resnet50v2.png')
+    # plot_model(model, to_file='img/resnet50v2.png')
+    # plot_model(model101, to_file='img/resnet101v2.png')
+    # plot_model(model152, to_file='img/resnet152v2.png')

经典网络/ResNetV2/test/resnetv2——test.ipynb

@@ -36,7 +36,7 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
-    "scrolled": false
+    "scrolled": true
    },
    "outputs": [
     {