我的RNN是否只针对1个或2个样本进行训练

Question

我创建了一个由7个细胞组成的LSTM-RNN。它减少了损失，但精度保持为零。我一直无法找出原因，直到我看到keras训练控制台的输出。以下是最近一次训练的样本。

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)

Answer 1

我已经理解了我的错误。不需要任何答案。非常感谢。