'distributions'列对应于一个独特的方程组，我试图将其压缩成SageMath中的一系列数字。'probs'列对应于是否应该根据它所在的行和列，通过给定的单项式来给定方程式多余。上面仅用于概述，与我的实际问题无关。

无论如何，这是我的代码。我试着用注释尽可能地解释它。

import csv
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow.keras as keras

distribution_train = []
probs_train = []

x_train = []

y_train = []

with open('tftest.csv') as csv_file:

csv_reader = csv.reader(csv_file, delimiter=',')

for row in csv_reader:
    distribution_train.append(row[0])
    probs_train.append(row[1])

'''
Get rid of the titles in the csv file
'''
distribution_train.pop(0)
probs_train.pop(0)

'''
For some reason everything in my csv file is stored as strings.
The below function is to convert it into floats so that TF can work with it.
'''
def num_converter_flatten(csv_list):

f = []
for j in range(len(csv_list)):
    append_this = []
    for i in csv_list[j]:
        if i == '1' or i == '2' or i == '3' or i == '4' or i == '5' or i == '6' or i == '7' or i == '8' or i =='9' or i =='0':
            append_this.append(float(i))
    f.append((append_this))

return f

x_train = num_converter_flatten(distribution_train)
y_train = num_converter_flatten(probs_train)

x_train = tf.keras.utils.normalize(x_train, axis=1)
y_train = tf.keras.utils.normalize(y_train, axis=1)

model = tf.keras.models.Sequential()

model.add(tf.keras.layers.Flatten())

model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))

'''
I'm making the final layer 80 because I want TF to output the size of the
'probs' list in the csv file
'''

model.add(tf.keras.layers.Dense(80, activation=tf.nn.softmax))

model.compile(optimizer='adam',

          loss='sparse_categorical_crossentropy',
          metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5)
但是，当我运行我的代码时，我收到以下错误。

tensorflow.python.framework.errors_impl.InvalidArgumentError: logits and labels must have the same first dimension, got logits shape [32,80] and labels shape [2560]
[[{{node loss/output_1_loss/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits}} = SparseSoftmaxCrossEntropyWithLogits[T=DT_FLOAT, Tlabels=DT_INT64, _class=["loc:@train...s_grad/mul"], _device="/job:localhost/replica:0/task:0/device:CPU:0"](loss/output_1_loss/Log, loss/output_1_loss/Cast)]]