我有一个带有单个隐藏层的 keras MLP。我正在使用多层感知器，在单个隐藏层中具有特定数量的节点。我想在一批通过时提取该隐藏层所有神经元的激活值，并且我想为每个时期执行此操作并将其存储在列表中以进行探索。我的表述如下。
class myNetwork:
# Architecture of our neural network.
def multilayerPerceptron(self, Num_Nodes_hidden,input_features,output_dims,activation_function = 'relu', learning_rate=0.001,
momentum_val=0.00):
model = Sequential()
model.add(Dense(Num_Nodes_hidden, input_dim =input_features, activation=activation_function))
model.add(Dense(output_dims,activation='softmax'))
model.compile(loss = "categorical_crossentropy",
optimizer=SGD(lr = learning_rate, momentum = momentum_val),
metrics=['accuracy'])
return model
下面是我对另一部分的调用，我使用 lambdacallbacks 来保存权重。我想要类似的东西，但这次保存隐藏层的实际激活值。
from keras.callbacks import LambdaCallback
import pickle
from keras.callbacks import ModelCheckpoint
from keras.callbacks import CSVLogger
# setting_parameters and calling inputs.
val = myNetwork()
vals = val.multilayerPerceptron(8,4,3,'relu',0.01)
batch_size_val = 20
number_iters = 200
weights_ih = []
weights_ho = []
activation_vals = []
get_activtaion = LambdaCallback(on_epoch_end=lambda batch, logs: activation_vals.append("What should I put Here"))
print_weights = LambdaCallback(on_epoch_end=lambda batch, logs: weights_ih.append(vals.layers[0].get_weights()))
print_weights_1 = LambdaCallback(on_epoch_end=lambda batch, logs: weights_ho.append(vals.layers[1].get_weights()))
history_callback = vals.fit(X_train, Y_train,
batch_size=batch_size_val,
epochs=number_iters,
verbose=0,
validation_data=(X_test, Y_test),
callbacks = [csv_logger,print_weights,print_weights_1,get_activtaion])
我非常困惑，不知道应该在 GetActivtion 中放入什么。请让我知道我应该做什么才能获得该批处理所有样本的权重迭代值的激活值。
最佳答案
weights_callback获取每层的权重:
weights_list = [] #[epoch][layer][unit(l-1)][unit(l)]
def save_weights(model):
inner_list = []
for layer in model.layers:
inner_list.append(layer.get_weights()[0])
weights_list.append(inner_list)
weights_callback = LambdaCallback(on_epoch_end = lambda batch, logs:save_weights(model))
activations_callback 获取每层的输出:
activations_list = [] #[epoch][layer][0][X][unit]
def save_activations(model):
outputs = [layer.output for layer in model.layers]
functors = [K.function([model.input],[out]) for out in outputs]
layer_activations = [f([X_input_vectors]) for f in functors]
activations_list.append(layer_activations)
activations_callback = LambdaCallback(on_epoch_end = lambda batch, logs:save_activations(model))
应用回调:
result = model.fit(... , callbacks = [weights_callback, activations_callback], ...)
引用资料:
关于python - 每个时期隐藏层的输出并将其存储在 keras 的列表中？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/58265156/