如何更改tensorflow中的符號變量（tf.Variable）？

Question

我已經爲自己寫了一個張量流類，如下所示，但是當我嘗試在函數refine_init_weight中手動進行訓練後將一些重量設置爲零時遇到了一些問題。在這個函數中，我試着將所有數字設置爲零，一旦它低於某個值，並看看準確率如何變化。問題是，當我reran self.sess.run(self.accuracy, feed_dict={self.var_X: self.X_test, self.var_Y: self.y_test})，它的價值似乎並沒有相應改變。我只是想知道在這種情況下我應該在哪裏更改符號變量（精確度取決於我更改的權重）？

import tensorflow as tf 
from nncomponents import * 
from helpers import * 
from sda import StackedDenoisingAutoencoder 


class DeepFeatureSelection: 
    def __init__(self, X_train, X_test, y_train, y_test, weight_init='sda', hidden_dims=[100, 100, 100], epochs=1000, 
       lambda1=0.001, lambda2=1.0, alpha1=0.001, alpha2=0.0, learning_rate=0.1, optimizer='FTRL'): 
     # Initiate the input layer 

     # Get the dimension of the input X 
     n_sample, n_feat = X_train.shape 
     n_classes = len(np.unique(y_train)) 

     self.epochs = epochs 

     # Store up original value 
     self.X_train = X_train 
     self.y_train = one_hot(y_train) 
     self.X_test = X_test 
     self.y_test = one_hot(y_test) 

     # Two variables with undetermined length is created 
     self.var_X = tf.placeholder(dtype=tf.float32, shape=[None, n_feat], name='x') 
     self.var_Y = tf.placeholder(dtype=tf.float32, shape=[None, n_classes], name='y') 

     self.input_layer = One2OneInputLayer(self.var_X) 

     self.hidden_layers = [] 
     layer_input = self.input_layer.output 

     # Initialize the network weights 
     weights, biases = init_layer_weight(hidden_dims, X_train, weight_init) 

     print(type(weights[0])) 

     # Create hidden layers 
     for init_w,init_b in zip(weights, biases): 
      self.hidden_layers.append(DenseLayer(layer_input, init_w, init_b)) 
      layer_input = self.hidden_layers[-1].output 

     # Final classification layer, variable Y is passed 
     self.softmax_layer = SoftmaxLayer(self.hidden_layers[-1].output, n_classes, self.var_Y) 

     n_hidden = len(hidden_dims) 

     # regularization terms on coefficients of input layer 
     self.L1_input = tf.reduce_sum(tf.abs(self.input_layer.w)) 
     self.L2_input = tf.nn.l2_loss(self.input_layer.w) 

     # regularization terms on weights of hidden layers   
     L1s = [] 
     L2_sqrs = [] 
     for i in xrange(n_hidden): 
      L1s.append(tf.reduce_sum(tf.abs(self.hidden_layers[i].w))) 
      L2_sqrs.append(tf.nn.l2_loss(self.hidden_layers[i].w)) 

     L1s.append(tf.reduce_sum(tf.abs(self.softmax_layer.w))) 
     L2_sqrs.append(tf.nn.l2_loss(self.softmax_layer.w)) 

     self.L1 = tf.add_n(L1s) 
     self.L2_sqr = tf.add_n(L2_sqrs) 

     # Cost with two regularization terms 
     self.cost = self.softmax_layer.cost \ 
        + lambda1*(1.0-lambda2)*0.5*self.L2_input + lambda1*lambda2*self.L1_input \ 
        + alpha1*(1.0-alpha2)*0.5 * self.L2_sqr + alpha1*alpha2*self.L1 

     # FTRL optimizer is used to produce more zeros 
#   self.optimizer = tf.train.FtrlOptimizer(learning_rate=learning_rate).minimize(self.cost) 

     self.optimizer = optimize(self.cost, learning_rate, optimizer) 

     self.accuracy = self.softmax_layer.accuracy 

     self.y = self.softmax_layer.y 

    def train(self, batch_size=100): 
     sess = tf.Session() 
     self.sess = sess 
     sess.run(tf.initialize_all_variables()) 

     for i in xrange(self.epochs): 
      x_batch, y_batch = get_batch(self.X_train, self.y_train, batch_size) 
      sess.run(self.optimizer, feed_dict={self.var_X: x_batch, self.var_Y: y_batch}) 
      if i % 2 == 0: 
       l = sess.run(self.cost, feed_dict={self.var_X: x_batch, self.var_Y: y_batch}) 
       print('epoch {0}: global loss = {1}'.format(i, l)) 
       self.selected_w = sess.run(self.input_layer.w) 
       print("Train accuracy:",sess.run(self.accuracy, feed_dict={self.var_X: self.X_train, self.var_Y: self.y_train})) 
       print("Test accuracy:",sess.run(self.accuracy, feed_dict={self.var_X: self.X_test, self.var_Y: self.y_test})) 
       print(self.selected_w) 
       print(len(self.selected_w[self.selected_w==0])) 
     print("Final test accuracy:",sess.run(self.accuracy, feed_dict={self.var_X: self.X_test, self.var_Y: self.y_test})) 

    def refine_init_weight(self, threshold=0.001): 
     refined_w = np.copy(self.selected_w) 
     refined_w[refined_w < threshold] = 0 
     self.input_layer.w.assign(refined_w) 
     print("Test accuracy refined:",self.sess.run(self.accuracy, feed_dict={self.var_X: self.X_test, self.var_Y: self.y_test})) 

Answer 1

（我就重新發佈一個答案我的評論）

你需要運行你創建的分配運算，否則它只是加入圖形和永遠不會執行。

assign_op = self.input_layer.w.assign(refined_w) 
self.sess.run(assign_op) 

---

如果你想這樣做，在Tensorflow您可以創建權重變量的布爾面具與tf.greater和tf.less，這個面具轉換爲tf.float32並用重陣列相乘。