How do I change the symbolic variable (tf.Variable) in tensorflow?

Question

I have written my self a tensorflow class as below, but I met some problem when I am trying to set some weight to zero after training manually in function refine_init_weight. In this function, I tried setting all numbers to zero once it is below some value and see how would the accuracy rate change. The problem is, when I reran self.sess.run(self.accuracy, feed_dict={self.var_X: self.X_test, self.var_Y: self.y_test}), it seems that its value does not changed accordingly. I am just wondering where should I change the symbolic variable (the accuracy depends on the weights I changed) at this case?

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

(I'll just repost my comment as an answer)

You need to run the assign operation you created, otherwise it is just added to the graph and never executed.

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

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If you want to do this in Tensorflow you could create a boolean mask of the weight variable with tf.greater and tf.less, convert this mask to tf.float32 and multiply it with the weight array.