如何在轉移學習中使用初始層

Question

我想進行轉移學習，我正在加載這些權重文件，但是現在我迷失了如何使用其圖層來訓練我的自定義模型。 任何幫助將不勝感激下面是我嘗試的示例代碼：

local_weights_file= '/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'

pre_trained_model = InceptionV3(input_shape = (150, 150, 3),
include_top = False,
weights = None)
​
pre_trained_model.load_weights(local_weights_file)
​
for layer in pre_trained_model.layers:
layer.trainable = False

Answer 1

您需要將最后一層的輸出輸入到最終模型中。 這樣的事情應該工作

last_layer = pre_trained_model.get_layer('mixed7')
last_output = last_layer.output



# Flatten the output layer to 1 dimension
x = layers.Flatten()(last_output)
# Add a fully connected layer with 1,024 hidden units and ReLU activation
x = layers.Dense(1024, activation='relu')(x)
# Add a dropout rate of 0.2
x = layers.Dropout(0.2)(x)                  
# Add a final sigmoid layer for classification
x = layers.Dense  (1, activation='sigmoid')(x)           

model = Model( pre_trained_model.input, x)