时间戳

首页 > 解决方案 > 我的 RNN 是否仅在 1 个或 2 个样本上进行训练？

python - 我的 RNN 是否仅在 1 个或 2 个样本上进行训练？

问题描述

我创建了一个包含 7 个单元的 LSTM-RNN。它减少了损失，但准确度保持为零。在我看到 keras 训练控制台输出之前，我一直无法找出原因。以下是最新训练运行的示例。

Epoch 500/500
2/2 [==============================] - 0s 13ms/step - loss: 0.1505 - accuracy: 0.0000e+00

2/2 是否意味着只在两个样本上进行训练？我有 7168 个数据点，我的批量大小明确表示为 7168，那么为什么会发生这种情况？下面是我的代码

import pandas
import scipy.io as loader
import tensorflow as tf
import keras
import numpy
import time
import math
from tensorflow.keras.datasets import imdb
from tensorflow.keras.layers import Embedding, Dense, LSTM
from tensorflow.keras.losses import BinaryCrossentropy
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.preprocessing.sequence import pad_sequences

additional_metrics = ['accuracy']

loss_function = BinaryCrossentropy()

number_of_epochs = 500
optimizer = SGD()
validation_split = 0.20
verbosity_mode = 1
mini = 0
maxi  = 0
mean = 0
"""

"""
def myfunc(arg):
    global mini, maxi, mean
    return (arg - mean) / (maxi - mini)

# k = 0
cgm = numpy.load('cgm_train_new.npy')
labels = numpy.load('labels_train_new.npy')
labs = list()
cgm_flat = cgm.flatten()
mini = min(cgm_flat)
maxi = max(cgm_flat)
mean = sum(cgm_flat) / len(cgm_flat)
cgm = numpy.apply_along_axis(myfunc, 0, cgm)

for each in labels:
    # suma = suma + sum(each)
    if each[-1] == 1: labs.append(.99)
    else: labs.append(.01)

RNNmodel = Sequential()

RNNmodel.add(LSTM(7, activation='tanh'))
RNNmodel.add(Dense(1, activation='sigmoid'))
RNNmodel.compile(optimizer=optimizer, loss=loss_function, metrics=additional_metrics)
cgm_rs = numpy.reshape(cgm, [len(cgm), 7, 1])
ans = numpy.reshape(labs, [len(labs), 1, 1])

history = RNNmodel.fit(
    cgm_rs,
    ans,
    batch_size=7168,
    epochs=number_of_epochs)#,
    # verbose=verbosity_mode)#,
                  #  validation_split=validation_split)



tf.keras.utils.plot_model(
    RNNmodel,
    to_file="RNNmodel.png")
answers = RNNmodel.predict(cgm_rs)
# for each in answers:
    # print(each)

标签： pythontensorflowkeras

解决方案

我已经理解了我的错误。不需要任何答案。谢谢你。

推荐阅读