如何獲得Tensorflow/Tflearn中隱藏層節點的值？

Question

這裏是一個用於tflearn中XOR的代碼。我希望得到倒數第二個隱藏層節點的值（而不是權重）。我怎麼弄到的？更具體地說，我希望獲得以下給出的四個預測中的每一個的層2節點的值（在代碼中給出）。

import tensorflow as tf 
import tflearn 

X = [[0., 0.], [0., 1.], [1., 0.], [1., 1.]] #input 
Y_xor = [[0.], [1.], [1.], [0.]] #input_labels 

# Graph definition 
with tf.Graph().as_default(): 
    tnorm = tflearn.initializations.uniform(minval=-1.0, maxval=1.0) 
    net = tflearn.input_data(shape=[None, 2], name='inputLayer') 
    net = tflearn.fully_connected(net, 2, activation='sigmoid', weights_init=tnorm, name='layer1') 
    net = tflearn.fully_connected(net, 1, activation='softmax', weights_init=tnorm, name='layer2') 
    regressor = tflearn.regression(net, optimizer='sgd', learning_rate=2., loss='mean_square', name='layer3') 

    # Training 
    m = tflearn.DNN(regressor) 
    m.fit(X, Y_xor, n_epoch=100, snapshot_epoch=False) 

    # Testing 
    print("Testing XOR operator") 
    print("0 xor 0:", m.predict([[0., 0.]])) 
    print("0 xor 1:", m.predict([[0., 1.]])) 
    print("1 xor 0:", m.predict([[1., 0.]])) 
    print("1 xor 1:", m.predict([[1., 1.]])) 

    layer1_var = tflearn.variables.get_layer_variables_by_name('layer1') 
    layer2_var = tflearn.variables.get_layer_variables_by_name('layer2') 
    inputLayer_var = tflearn.variables.get_layer_variables_by_name('inputLayer') 

    #result = tf.matmul(inputLayer_var, layer1_var[0]) + layer1_var[1] 

    with m.session.as_default(): 
     print(tflearn.variables.get_value(layer1_var[0])) #layer1 weights 
     print(tflearn.variables.get_value(layer1_var[1])) #layer1 bias 
     print(tflearn.variables.get_value(layer2_var[0])) #layer2 weights 
     print(tflearn.variables.get_value(layer2_var[1])) #layer2 bias 

Answer 1

您可以重新使用共享同一個會話的新模式（使用相同的權重）： 。請注意，您還可以保存您的'm'模型並使用'm2'加載它，得到類似的結果。

import tensorflow as tf 
import tflearn 

X = [[0., 0.], [0., 1.], [1., 0.], [1., 1.]] 
Y_xor = [[0.], [1.], [1.], [0.]] 

# Graph definition 
with tf.Graph().as_default(): 
    tnorm = tflearn.initializations.uniform(minval=-1.0, maxval=1.0) 
    net = tflearn.input_data(shape=[None, 2], name='inputLayer') 
    layer1 = tflearn.fully_connected(net, 2, activation='sigmoid', weights_init=tnorm, name='layer1') 
    layer2 = tflearn.fully_connected(layer1, 1, activation='softmax', weights_init=tnorm, name='layer2') 
    regressor = tflearn.regression(layer2, optimizer='sgd', learning_rate=2., loss='mean_square', name='layer3') 

    # Training 
    m = tflearn.DNN(regressor) 
    m.fit(X, Y_xor, n_epoch=100, snapshot_epoch=False) 

    # Testing 
    print("Testing XOR operator") 
    print("0 xor 0:", m.predict([[0., 0.]])) 
    print("0 xor 1:", m.predict([[0., 1.]])) 
    print("1 xor 0:", m.predict([[1., 0.]])) 
    print("1 xor 1:", m.predict([[1., 1.]])) 

    # You can create a new model, that share the same session (to get same weights) 
    # Or you can also simply save and load a model 
    m2 = tflearn.DNN(layer1, session=m.session) 
    print(m2.predict([[0., 0.]]))