如何使用 tensorflow 将数据拆分为测试和训练

Question

I'm very new to tensorflow. I've attended an online course, but I still have many questions related to data pre-processing. I would really appreciate if someone could help me out!!

My goal is to train a model that classifies Portuguese nouns into two gender categories (feminine and masculine) based on their internal structure. So, for this, I have a file containing about 4300 nouns and their categories (F and M labels).

First question: I have opened the nouns files and I first tokenized the words and after that I have padded them. I have a put the labels in a separated file. The labels file is a txt list containing the labels 'f' and 'm'. I've converted them into 0 and 1 integers and then convert them into a numpy array. I've also converted the padded nouns into a numpy array. Is that correct?

What is strange is that I have set the number of epochs for 100, but the program keeps training…

Second question:

How can I separate my train and labels into test and test_labels?

My code so far is below:

from collections import defaultdict
import nltk
from nltk.tokenize import word_tokenize
from nltk.tokenize import sent_tokenize,wordpunct_tokenize
import re
import os
import sys
from pathlib import Path
import numpy as np
import numpy
import tensorflow as tf

while True:
    try:
        file_to_open =Path(input("Please, insert your file path: "))
        with open(file_to_open,'r', encoding="utf-8") as f:
            words = f.read()
            break         
    except FileNotFoundError:
        print("\nFile not found. Better try again")
    except IsADirectoryError:
        print("\nIncorrect Directory path.Try again")

corpus=words.split('\n')

labels = []
new_labels=[]
nouns = []
for i in corpus:
    if i == '0':
        labels.append(i)
    elif i== '1':
        labels.append(i)
    else:
        nouns.append(i)

for x in labels:
    new_labels.append(int(x))


training_labels= numpy.array(new_labels)

training_nouns=[]

for w in nouns:
    a=list(w)
    b=' '.join([str(elem) for elem in a]) + ',' + ' '
    training_nouns.append(b)

vocab_size = 10000
embedding_dim = 16
max_length = 120
trunc_type='post'
oov_tok = "<OOV>"


from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences

tokenizer = Tokenizer(num_words = vocab_size, oov_token=oov_tok)
tokenizer.fit_on_texts(training_nouns)
word_index = tokenizer.word_index
nouns_sequences = tokenizer.texts_to_sequences(training_nouns)
padded = pad_sequences(nouns_sequences,maxlen=max_length)

model = tf.keras.Sequential([
    tf.keras.layers.Embedding(vocab_size, embedding_dim, 
input_length=max_length),
    tf.keras.layers.GlobalAveragePooling1D(),
    tf.keras.layers.Dense(36, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=. 
 ['accuracy'])
model.summary()


training_padded = np.array(padded)

num_epochs = 150
model.fit(training_padded, training_labels, epochs=num_epochs)

Answer 1

If you shouldn't use Tensorflow. you can use train_test_split scikit-learn function like this(you can continue with tensorflow):

from sklearn.model_selection import train_test_split
train_data,train_labels,test_data,test_labels=train_test_split(YOUR DATA,YOUR LABELS)

see here for more information.