如何獲得最後一層的輸出（A和B張量的值）在這個連體實現中的輸出？

Question

這是我爲keras實現模型而編寫的代碼（參見The model architecture）。我想獲得共享網絡中最後一層的輸出張量（圖像中的A & B）。我在其他stackoverflow答案中看到很多例子。但是我的模型是一個暹羅網絡，我想獲得輸入到連體相似度函數的圖層的輸出。

from __future__ import absolute_import 
from __future__ import print_function 
import numpy as np 
import os 

from keras.preprocessing import sequence 
from keras.models import Sequential, Model 
from keras.layers import Dense, Dropout, Embedding, LSTM, Bidirectional, Input, Lambda 
from keras.datasets import imdb 
from keras.layers.embeddings import Embedding 
from keras.optimizers import RMSprop 
from keras import backend as K 

# custom module to read activations of layers 
from read_activations import get_activations 

# ignore TensorFlow messages and warnings 
os.environ['TF_CPP_MIN_LOG_LEVEL']='2' 
# os.system('clear') 

# fix random seed for reproducibility 
np.random.seed(7) 

# importing custom module for data preprocessing 
import preprocess_data 

def euclidean_distance(vects): 
    x, y = vects 
    return K.sqrt(K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), K.epsilon())) 
    # return K.abs(x-y) 

def eucl_dist_output_shape(shapes): 
    shape1, shape2 = shapes 
    return (shape1[0], 1) 

def contrastive_loss(y_true, y_pred): 
    '''Contrastive loss from Hadsell-et-al.'06 
    http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf 
    ''' 
    margin = 1 
    return K.mean(y_true * K.square(y_pred) + 
        (1 - y_true) * K.square(K.maximum(margin - y_pred, 0))) 

def compute_accuracy(predictions, labels): 
    '''Compute classification accuracy with a fixed threshold on distances. 
    ''' 
    return labels[predictions.ravel() < 0.5].mean() 

def create_base_network(input_dim): 
    '''Base network to be shared. 
    ''' 
    seq = Sequential() 
    seq.add(Dense(128, input_shape=(input_dim,), activation='relu')) 
    seq.add(Dropout(0.1)) 
    seq.add(Dense(128, activation='relu')) 
    seq.add(Dropout(0.1)) 
    seq.add(Dense(128, activation='linear')) 
    return seq 

x_data, y_data = preprocess_data.dataset() 
input_dim = 1000 
epochs = 25 

tr_pairs = x_data[:263]  # 263000 
tr_y = y_data[:263]   
te_pairs = x_data[263:]  # 113000 
te_y = y_data[263:] 
# print(tr_pairs[:, 1]) 

base_network = create_base_network(input_dim) 
print(base_network.summary()) 

input_a = Input(shape=(input_dim,)) 
input_b = Input(shape=(input_dim,)) 

# because we re-use the same instance `base_network`, 
# the weights of the network 
# will be shared across the two branches 
processed_a = base_network(input_a) 
processed_b = base_network(input_b) 

distance = Lambda(euclidean_distance, 
        output_shape=eucl_dist_output_shape)([processed_a, processed_b]) 

model = Model([input_a, input_b], distance) 

# train 
rms = RMSprop() 
model.compile(loss=contrastive_loss, optimizer=rms) 
model.fit([tr_pairs[:, 0], tr_pairs[:, 1]], tr_y, 
      batch_size=128, 
      epochs=epochs, 
      validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y)) 

# compute final accuracy on training and test sets 
pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]]) 
tr_acc = compute_accuracy(pred, tr_y) 
pred = model.predict([te_pairs[:, 0], te_pairs[:, 1]]) 
te_acc = compute_accuracy(pred, te_y) 

print(model.summary()) 

print('* Accuracy on training set: %0.2f%%' % (100 * tr_acc)) 
print('* Accuracy on test set: %0.2f%%' % (100 * te_acc)) 

Answer 1

processed_a和processed_b是投入到連體相似度函數。

正如here所述，您可以使用創建張量函數來獲取輸出。

functor = K.function([input_a, input_b]+ [K.learning_phase()], [processed_a, processed_b])

並使用新的輸入相同。