[英]How to get the result of an intermediate layer in keras with Siamese Networks and Functional API?
我對連體網絡有以下網絡定義:
def build_siamese_model(inputShape, embeddingDim=48):
# specify the inputs for the feature extractor network
inputs = Input(inputShape)
## first set of CONV => RELU => RESID=> POOL => DROPOUT layers
first_conv1 = Conv2D(32, (3, 3), padding="same")(inputs)
first_batch_norm1=BatchNormalization()(first_conv1)
first_act1= LeakyReLU()(first_batch_norm1)
second_conv1 = Conv2D(32, (5, 5), padding="same")(inputs)
second_batch_norm1=BatchNormalization()(second_conv1)
second_act1= LeakyReLU()(second_batch_norm1)
third_conv1 = Conv2D(32, (7, 7), padding="same")(inputs)
third_batch_norm1=BatchNormalization()(third_conv1)
third_act1= LeakyReLU()(third_batch_norm1)
residual_block1= Add()([first_act1, second_act1, third_act1])
pool1 = MaxPooling2D(pool_size=(2, 2))(residual_block1)
dropout1 = Dropout(0.3)(pool1)
#receiver Convolutional layer
receiver1_conv = Conv2D(32, (3, 3), padding="same")(dropout1)
receiver1_batch_norm=BatchNormalization()(receiver1_conv)
act_receiver1=LeakyReLU()(receiver1_batch_norm)
## second set of CONV => BN=> RELU => RESID=> POOL => DROPOUT layers
first_conv2 = Conv2D(32, (3, 3), padding="same")(act_receiver1)
first_batch_norm2=BatchNormalization()(first_conv2)
first_act2= LeakyReLU()(first_batch_norm2)
second_conv2 = Conv2D(32, (5, 5), padding="same")(act_receiver1)
second_batch_norm2=BatchNormalization()(second_conv2)
second_act2= LeakyReLU()(second_batch_norm2)
third_conv2 = Conv2D(32, (7, 7), padding="same")(act_receiver1)
third_batch_norm2=BatchNormalization()(third_conv2)
third_act2= LeakyReLU()(third_batch_norm2)
residual_block2= Add()([first_act2, second_act2, third_act2])
pool2 = MaxPooling2D(pool_size=(2, 2))(residual_block2)
dropout2 = Dropout(0.3)(pool2)
#receiver Convolutional layer
receiver2_conv = Conv2D(32, (3, 3), padding="same")(dropout2)
receiver2_batch_norm=BatchNormalization()(receiver2_conv)
act_receiver2=LeakyReLU()(receiver2_batch_norm)
## last set of CONV => BN=> RELU => RESID=> POOL => DROPOUT layers
first_conv3 = Conv2D(32, (3, 3), padding="same")(act_receiver2)
first_batch_norm3=BatchNormalization()(first_conv3)
first_act3= LeakyReLU()(first_batch_norm3)
second_conv3 = Conv2D(32, (5, 5), padding="same")(act_receiver2)
second_batch_norm3=BatchNormalization()(second_conv3)
second_act3= LeakyReLU()(second_batch_norm3)
third_conv3 = Conv2D(32, (7, 7), padding="same")(act_receiver2)
third_batch_norm3=BatchNormalization()(third_conv3)
third_act3= LeakyReLU()(third_batch_norm3)
residual_block3= Add()([first_act3, second_act3, third_act3])
pool3 = MaxPooling2D(pool_size=(2, 2))(residual_block3)
dropout3 = Dropout(0.3)(pool3)
#last receiver Convolutional layer
receiver3_conv = Conv2D(32, (3, 3), padding="same")(dropout3)
receiver3_batch_norm=BatchNormalization()(receiver3_conv)
act_receiver3=LeakyReLU()(receiver3_batch_norm)
# prepare the final outputs
pooledOutput = GlobalAveragePooling2D()(act_receiver3)
outputs = Dense(embeddingDim)(pooledOutput)
# build the model
model = Model(inputs, outputs)
return(model)
但是,這部分連接到我的網絡的輸入和output作為功能API。 以下是我如何鏈接這些部分:
print("[INFO] building siamese network...")
imgA = Input(shape=config.IMG_SHAPE)
imgB = Input(shape=config.IMG_SHAPE)
featureExtractor = build_siamese_model(config.IMG_SHAPE)
featsA = featureExtractor(imgA)
featsB = featureExtractor(imgB)
distance = Lambda(utils.euclidean_distance)([featsA, featsB])
outputs = Dense(1, activation="sigmoid")(distance)
model = Model(inputs=[imgA, imgB], outputs=outputs)
但是,在編譯model時,這里是對model的總結:
因此,我在上面完成的網絡定義似乎只是網絡的一層。
那么,我想要什么?
我想加載 model 並提取特定層的 output。 特別想要功能 object 的最后一層的 output (outputs = Dense(48)(pooledOutput) 在上面的網絡定義中) 。 這將為我在 model 中測試的每對圖像提供 48 個特征向量。
我試圖檢查一些以前的帖子並做了以下事情:
print("Step 1: Loading Model")
model1=load_model("where/the/model/is/located", compile=False)
#I tried the output of the firstlayer, for example
model_with_intermediate_layers = Model(inputs=model1.input, outputs = model1.layers[0].output)
pred = model_with_intermediate_layers.predict([pair_1,pair_2], steps = 1)
print(pred)
問題是什么??
上面代碼的問題是它只能訪問 0、1、3 和 4 層。0 和 1 給出輸入形狀,第 3 層給出分數,第 4 層是空的。 **我想訪問中間層,尤其是特征提取器網絡的最后一層。 ** 我怎樣才能做到這一點?
考慮到(i)我的功能 object 是網絡的第二層; (ii) 我想要它的最后一層 output; (iii) 第二層 output 是第三層的輸入,我用下面的代碼解決了這個問題:
#I am getting layer's 3 input, which is the same as the second layer's output (last layer of my functional model)
model_intermediate = Model(inputs=model1.input, outputs = model1.layers[3].input)
#Here I get 2 48-d vectors.
pred_intermediate = model_intermediate.predict([pair_1,pair_2], steps = 1) # predict_generator is deprecated
pred_intermediate=np.array(pred_intermediate)
print(type(pred_intermediate))
print(pred_intermediate)
print(pred_intermediate.shape)
input()
這給了我我想要的
聲明:本站的技術帖子網頁,遵循CC BY-SA 4.0協議,如果您需要轉載,請注明本站網址或者原文地址。任何問題請咨詢:yoyou2525@163.com.