Question

我正在使用这个tensorflow代码来制作一个pos标签：

import tensorflow as tf
import pickle
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

RANDOM_SEED = 42
tf.set_random_seed(RANDOM_SEED)

Data = myData
Target = myTarget

def init_weights(shape):
    """ Weight initialization """
    weights = tf.random_uniform(shape, minval=0.5, maxval=-0.5)
    return tf.Variable(weights)

def forwardprop(X, w_1, w_2):
    """
    Forward-propagation.
    IMPORTANT: yhat is not softmax since TensorFlow's softmax_cross_entropy_with_logits() does that internally.
    """
    h    = tf.nn.tanh(tf.matmul(X, w_1))  # The \sigma function
    yhat = tf.matmul(h, w_2)  # The \varphi function
    return yhat

def get_data():
    data   = np.array(Data)
    target = np.array(Target)

    # Prepend the column of 1s for bias
    N, M  = data.shape
    all_X = np.ones((N, M + 1))
    all_X[:, 1:] = data
    all_Y=target

    return train_test_split(all_X, all_Y, test_size=0.33, random_state=RANDOM_SEED)

def main():
    train_X, test_X, train_y, test_y = get_data()

    # Layer's sizes
    x_size = train_X.shape[1]   # Number of input nodes
    h_size = 40                # Number of hidden nodes
    y_size = train_y.shape[1]   # Number of outcomes

    # Symbols
    X = tf.placeholder("float", shape=[None, x_size])
    y = tf.placeholder("float", shape=[None, y_size])

    # Weight initializations
    w_1 = init_weights((x_size, h_size))
    w_2 = init_weights((h_size, y_size))

    # Forward propagation
    yhat    = forwardprop(X, w_1, w_2)
    predict = tf.argmax(yhat, axis=1)

    # Backward propagation
    cost    = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=yhat))
    updates = tf.train.GradientDescentOptimizer(0.1).minimize(cost)

    # Run SGD
    sess = tf.Session()
    init = tf.global_variables_initializer()
    sess.run(init)


    for epoch in range(100):
        # Train with each example
        for i in range(len(train_X)):
            sess.run(updates, feed_dict={X: train_X[i: i + 1], y: train_y[i: i + 1]})

        train_accuracy = np.mean(np.argmax(train_y, axis=1) ==
                                 sess.run(predict, feed_dict={X: train_X, y: train_y}))
        test_accuracy  = np.mean(np.argmax(test_y, axis=1) ==
                                 sess.run(predict, feed_dict={X: test_X, y: test_y}))

        print("Epoch = %d, train accuracy = %.2f%%, test accuracy = %.2f%%"
              % (epoch + 1, 100. * train_accuracy, 100. * test_accuracy))

    sess.close()

if __name__ == '__main__':
    main()

我必须将另一个值（标签转换概率）添加到网络的最后一层的输出，这是softmax函数产生的概率。但是，正如代码中提到的那样，'yhat'不是softmax，因为TensorFlow的softmax_cross_entropy_with_logits（）在内部执行。现在，我不知道如何更改代码以便将标签转换概率添加到softmax函数的输出。

使用tensorflow进行POS标记

0 个答案: