这是tflearn中XOR的代码。我希望获得倒数第二个隐藏层节点的值（而不是权重）。我该怎么办？更具体地说，我希望为下面给出的四个预测中的每一个获取第2层节点的值（在代码中给定）。
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
您可以重复使用共享相同会话（使用相同权重）的新模型：。请注意，您还可以保存“ m”模型，然后将其加载为“ m2”，从而得出相似的结果。
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.]]))
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我来说两句