dtypeってなに・・・？

Question

dtypeが分からずエラーが取り除けません。協力お願いします！

コード

import sys
import numpy
import csv


numpy.seterr(all='ignore')

def sigmoid(x):
    return 1. / (1 + numpy.exp(-x))


class dA(object):
    # パラメータの初期化
    def __init__(self, input=None, n_visible=2, n_hidden=3, \
        W=None, hbias=None, vbias=None, numpy_rng=None):

        self.n_visible = n_visible  # 入力層のユニット数
        self.n_hidden = n_hidden    # 中間層のユニット数

        if numpy_rng is None:
            numpy_rng = numpy.random.RandomState(1234)
            
        if W is None:
            a = 1. / n_visible
            initial_W = numpy.array(numpy_rng.uniform(  # 均一にW(重み)を初期化する
                low=-a,
                high=a,
                size=(n_visible, n_hidden)))

            W = initial_W

        if hbias is None:
            hbias = numpy.zeros(n_hidden)  # 中間層のバイアスを0で初期化する

        if vbias is None:
            vbias = numpy.zeros(n_visible)  # 中間層のバイアスを0で初期化する

        self.numpy_rng = numpy_rng
        self.x = input
        self.W = W
        self.W_prime = self.W.T
        self.hbias = hbias
        self.vbias = vbias

        # self.params = [self.W, self.hbias, self.vbias]


    def get_corrupted_input(self, input, corruption_level):
        assert corruption_level < 1

#        print (self.numpy_rng.binomial(size=input.shape,n=1,p=1-corruption_level))
        return self.numpy_rng.binomial(size=input.shape,
                                       n=1,
                                       p=1-corruption_level) * input

    # Encode
    def get_hidden_values(self, input):
        print (sigmoid(numpy.dot(input, self.W) + self.hbias))
        return sigmoid(numpy.dot(input, self.W) + self.hbias)  #シグモイド関数(入力*重みの総和)

    # Decode
    def get_reconstructed_input(self, hidden):
        return sigmoid(numpy.dot(hidden, self.W_prime) + self.vbias)  #シグモイド関数(中間*重みの総和)


    def train(self, lr=0.1, corruption_level=0.3, input=None):
        if input is not None:
            self.x = input

        x = self.x
        tilde_x = self.get_corrupted_input(x, corruption_level)
        y = self.get_hidden_values(tilde_x)
        z = self.get_reconstructed_input(y)

        L_h2 = x - z
        L_h1 = numpy.dot(L_h2, self.W) * y * (1 - y)

        L_vbias = L_h2
        L_hbias = L_h1
        L_W =  numpy.dot(tilde_x.T, L_h1) + numpy.dot(L_h2.T, y)


        self.W += lr * L_W
        self.hbias += lr * numpy.mean(L_hbias, axis=0)
        self.vbias += lr * numpy.mean(L_vbias, axis=0)



    def negative_log_likelihood(self, corruption_level=0.3):
        tilde_x = self.get_corrupted_input(self.x, corruption_level)
        y = self.get_hidden_values(tilde_x)
        z = self.get_reconstructed_input(y)

        cross_entropy = - numpy.mean(
            numpy.sum(self.x * numpy.log(z) +
            (1 - self.x) * numpy.log(1 - z),
                      axis=1))

        return cross_entropy


    def reconstruct(self, x):
        y = self.get_hidden_values(x)
        z = self.get_reconstructed_input(y)
        return z



def test_dA(learning_rate=0.1, corruption_level=0.3, training_epochs=50):
    data = numpy.array([])
    with open('train.csv','r') as f:
        reader = csv.reader(f)
#        header = next(x)
        for row in reader:
            data = numpy.append(data, row)
    
    rng = numpy.random.RandomState(123)

    # construct dA
    da = dA(input=data, n_visible=5, n_hidden=1, numpy_rng=rng)

    # train
    for epoch in range(training_epochs):
        da.train(lr=learning_rate, corruption_level=corruption_level)
        # cost = da.negative_log_likelihood(corruption_level=corruption_level)
        # print >> sys.stderr, 'Training epoch %d, cost is ' % epoch, cost
        # learning_rate *= 0.95


    # test
    x = numpy.array([])
    with open('test.csv','r') as f:
        reader = csv.reader(f)
#        header = next(x)
        for row in reader:
            x = numpy.append(x, row)

    print (da.reconstruct(x))



if __name__ == "__main__":
    test_dA()

エラー文

Traceback (most recent call last):
  File "C:\Users\yossi\Documents\sotuken\Autoencoder.py", line 159, in <module>
  File "C:\Users\yossi\Documents\sotuken\Autoencoder.py", line 140, in test_dA
  File "C:\Users\yossi\Documents\sotuken\Autoencoder.py", line 87, in train
  File "C:\Users\yossi\Documents\sotuken\Autoencoder.py", line 70, in get_corrupted_input
TypeError: ufunc 'multiply' did not contain a loop with signature matching types dtype('<U32') dtype('<U32') dtype('<U32')

Answer 1

変数の型に掛け算が定義されていないというエラーです。

        return self.numpy_rng.binomial(size=input.shape,
                                       n=1,
                                       p=1-corruption_level) * input

のself.numpy_rng.binomial(size=input.shape,n=1,p=1-corruption_level)が数字のnp.arrayであることを考えると、input->xが文字列であると考えれます。

Comments

[atena]

解決できました！！ありがとうございます！！