因此，正如标题所述，我尝试训练我的模块，但获得的精度保持不变。 我只能使用TensorFlow库 我的数据集标签是“男性”和“女性” （试图区分男声和女声）
代码：
import numpy as np
import pandas as pd
import tensorflow as tf
def prep_data(target_class=1, train_siz=120, test_siz=30):
cols = ['meanfreq', 'sd', 'median', 'Q25', 'Q75', 'IQR', 'skew', 'kurt', 'sp.ent', 'sfm', 'mode', 'centroid',
'meanfun', 'minfun', 'maxfun', 'meandom', 'mindom', 'maxdom', 'dfrange', 'modindx', 'label']
iris_df = pd.read_csv('C:\\Users\\Owner\\PycharmProjects\\TesnsorFlowU\\voice.csv', header=None, names=cols)
iris_df = iris_df.drop(iris_df.index[0])
# Encode class
class_name = ['male', 'female']
iris_df['iclass'] = [class_name.index(class_str)
for class_str in iris_df['label'].values]
# Random Shuffle before split to train/test
orig = np.arange(len(iris_df))
perm = np.copy(orig)
np.random.shuffle(perm)
iris = iris_df[
['meanfreq', 'sd', 'median', 'Q25', 'Q75', 'IQR', 'skew', 'kurt', 'sp.ent', 'sfm', 'mode', 'centroid',
'meanfun', 'minfun', 'maxfun', 'meandom', 'mindom', 'maxdom', 'dfrange', 'modindx', 'iclass']].values
iris[orig, :] = iris[perm, :]
# Arrange Label value to consider one vs. all classification
# ex. class 0 --> label 1.0, class 1 or 2 --> label 0.0
if target_class in [1, 2]:
target_class = target_class - 1 # python indexing rule
for i in range(len(iris)):
iclass = int(iris[i, -1])
iris[i, -1] = float(iclass == target_class)
else:
print('Value Error (target_class)')
# Split dataset
trX = iris[:train_siz, :-1]
teX = iris[train_siz:, :-1]
trY = iris[:train_siz, -1]
teY = iris[train_siz:, -1]
return trX, trY, teX, teY
def linear_model(X, w, b):
output = tf.matmul(X, w) + b
return output
if __name__ == '__main__':
tr_x, tr_y, te_x, te_y = prep_data(target_class=3, train_siz=2218, test_siz=950)
# Variables
x = tf.placeholder(tf.float32, [None, 20])
y_ = tf.placeholder(tf.float32, [None, 1])
p5 = tf.constant(0.5) # threshold of Logistic Regression
w = tf.Variable(tf.random_normal([20, 1], mean=0.0, stddev=0.05))
b = tf.Variable([0.])
y_pred = linear_model(x, w, b)
y_pred_sigmoid = tf.sigmoid(y_pred) # for prediction
#y_pred_sigmoid = tf.nn.relu(y_pred)
#cross_entropy = -tf.reduce_sum(y_ * tf.log(y_pred_sigmoid))
x_entropy = tf.nn.sigmoid_cross_entropy_with_logits(labels=y_pred, logits=y_)
loss = tf.reduce_mean(x_entropy)
train_step = tf.train.GradientDescentOptimizer(0.001).minimize(loss)
delta = tf.abs((y_ - y_pred_sigmoid))
correct_prediction = tf.cast(tf.less(delta, p5), tf.int32)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# Train
init = tf.initialize_all_variables()
with tf.Session() as sess:
sess.run(init)
print('Training...')
for i in range(3001):
batch_xs, batch_ys = tr_x, tr_y
fd_train = {x: batch_xs, y_: batch_ys.reshape((-1, 1))}
train_step.run(fd_train)
if i % 500 == 0:
loss_step = loss.eval(fd_train)
train_accuracy = accuracy.eval(fd_train)
print(' step, loss, accurary = %6d: %8.3f,%8.3f' % (i, loss_step, train_accuracy))
# Test trained model
fd_test = {x: te_x, y_: te_y.reshape((-1, 1))}
print('accuracy = %10.4f' % accuracy.eval(fd_test))
conf_mat = tf.math.confusion_matrix(labels=tf.argmax(y_, 1), predictions=tf.argmax(y_pred_sigmoid, 1), num_classes=2)
te_y = te_y.reshape([950, 1])
conf_mat_to_print = sess.run(conf_mat, feed_dict={x: te_x, y_: te_y})
print(conf_mat_to_print)
输出：
Training...
step, loss, accurary = 0: 1.156, 0.483
step, loss, accurary = 500: -94.210, 0.505
step, loss, accurary = 1000: -189.574, 0.505
step, loss, accurary = 1500: -284.936, 0.505
step, loss, accurary = 2000: -380.298, 0.505
step, loss, accurary = 2500: -475.661, 0.505
step, loss, accurary = 3000: -571.024, 0.505
accuracy = 0.4874
conf_mat:
[[950 0]
[ 0 0]]
正如您在混淆矩阵中看到的那样，该模块错误地认为所有训练数据均来自男性...
我尝试了很多东西，但我不明白哪里错了。