训练LSTM mod

Question

我试着训练我的lstm模型，但是准确度，精确度，召回率和f1分数都是0。我从kaggle下载了心脏病数据集。代码如下：

import tensorflow as tf
import pandas as pd
import numpy as np
from tensorflow.contrib import rnn
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score, accuracy_score, recall_score, precision_score

heartt = pd.read_csv('heart.csv')

cols_to_norm = ['sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal']

heartt[cols_to_norm] = heartt[cols_to_norm].apply(lambda x: (x - x.min()) / (x.max() - x.min()))

sex_people = tf.feature_column.numeric_column('sex')
c_p = tf.feature_column.numeric_column('cp')
trest_bps = tf.feature_column.numeric_column('trestbps')
cholestrol = tf.feature_column.numeric_column('chol')
fb_s= tf.feature_column.numeric_column('fbs')
rest_ecg = tf.feature_column.numeric_column('restecg')
thala_ch = tf.feature_column.numeric_column('thalach')
ex_ang = tf.feature_column.numeric_column('exang')
old_peak = tf.feature_column.numeric_column('oldpeak')
slo_pe = tf.feature_column.numeric_column('slope')
c_a = tf.feature_column.numeric_column('ca')
tha_l = tf.feature_column.numeric_column('thal')
ag_e = tf.feature_column.numeric_column('age')

age_buckets = tf.feature_column.bucketized_column(ag_e, boundaries=[20,30,40,50,60,70,80])

feat_cols = [sex_people ,c_p, trest_bps ,cholestrol ,fb_s,rest_ecg,thala_ch ,ex_ang, old_peak, slo_pe,c_a, tha_l, age_buckets]

x_data = heartt.drop('target',axis=1)

x_data.info()

labels = heartt['target']

X_train,X_test,y_train,y_test = train_test_split(x_data, labels, test_size=0.2, shuffle=False, random_state=42)

epochs = 8
n_classes = 1
n_units = 200
n_features = 13
batch_size = 35

xplaceholder= tf.placeholder('float',[None,n_features])
yplaceholder = tf.placeholder('float')

def recurrent_neural_network_model():
    layer ={ 'weights': tf.Variable(tf.random_normal([n_units, n_classes])),'bias': tf.Variable(tf.random_normal([n_classes]))}

    x = tf.split(xplaceholder, n_features, 1)
    print(x)

    lstm_cell = rnn.BasicLSTMCell(n_units)

    outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)

    output = tf.matmul(outputs[-1], layer['weights']) + layer['bias']

    return output

def train_neural_network():
    logit = recurrent_neural_network_model()
    logit = tf.reshape(logit, [-1])

    cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logit, labels=yplaceholder))
    optimizer = tf.train.AdamOptimizer().minimize(cost)

    with tf.Session() as sess:

        tf.global_variables_initializer().run()
        tf.local_variables_initializer().run()

        for epoch in range(epochs):
            epoch_loss = 0

            i = 0
            for i in range(int(len(X_train) / batch_size)):

                start = i
                end = i + batch_size

                batch_x = np.array(X_train[start:end])
                batch_y = np.array(y_train[start:end])

                _, c = sess.run([optimizer, cost], feed_dict={xplaceholder: batch_x, yplaceholder: batch_y})
                epoch_loss += c
                i += batch_size

            print('Epoch', epoch, 'completed out of', epochs, 'loss:', epoch_loss)

        pred = tf.round(tf.nn.sigmoid(logit)).eval({xplaceholder: np.array(X_test), yplaceholder: np.array(y_test)})
        f1 = f1_score(np.array(y_test), pred, average='macro')
        accuracy=accuracy_score(np.array(y_test), pred)
        recall = recall_score(y_true=np.array(y_test), y_pred= pred)
        precision = precision_score(y_true=np.array(y_test), y_pred=pred)
        print("F1 Score:", f1)
        print("Accuracy Score:",accuracy)
        print("Recall:", recall)
        print("Precision:", precision)


train_neural_network()

这是我得到的输出：

^{pr2}$

我很困惑我在这里可能错在哪里。为什么我得不到正确的准确度，准确度，f1分数和回忆能力？在

Answer 1

我看一下数据集，它看起来不像是LSTM模型的问题。LSTMs（以及所有rnn）都是用来预测连续输出的，它们是时间序列回归的神经网络等价物。我知道有些情况下（例如使用NLP的情绪分析）可以将LSTMs应用于分类问题，但事实似乎并非如此。这些数据是“时间的”，即数据集的每一行代表一个病人，数据序列不携带任何信息。在

当您需要一个具有数据先前状态（如时间序列）的“内存”模型时，使用LSTMs。如果你想应用LSTMs，我建议你改变数据集（你可以看看this huge list of ML datasets）。如果你的神经网络是用来分类的，那就转换成神经网络。为此，您可以检查my personal TensorFlow tutorial如何执行此操作。在