python实现textcnn，改变超参数，如果在python中画出对照的图每一次的准确率的图

好的，我可以为您提供代码和说明来实现这个问题。

首先，我们需要导入必要的库：

import numpy as np
import tensorflow as tf
from tensorflow.keras.datasets import imdb
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.layers import Input, Embedding, Conv1D, GlobalMaxPooling1D, Dense
from tensorflow.keras.models import Model
import matplotlib.pyplot as plt

接下来，我们需要加载 IMDB 数据集，并进行预处理：

# 加载 IMDB 数据集
vocab_size = 10000
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=vocab_size)

# 将文本序列填充到相同的长度
max_len = 128
X_train = pad_sequences(X_train, maxlen=max_len)
X_test = pad_sequences(X_test, maxlen=max_len)

然后，我们可以定义 TextCNN 模型：

def TextCNN(max_len, vocab_size, embedding_size, filter_sizes, num_filters, num_classes):
    inputs = Input(shape=(max_len,))
    x = Embedding(vocab_size, embedding_size)(inputs)
    pooled_outputs = []
    for fsz in filter_sizes:
        conv = Conv1D(num_filters, fsz, activation='relu')(x)
        pooled = GlobalMaxPooling1D()(conv)
        pooled_outputs.append(pooled)
    x = tf.keras.layers.concatenate(pooled_outputs, axis=-1)
    x = Dense(128, activation='relu')(x)
    predictions = Dense(num_classes, activation='sigmoid')(x)
    model = Model(inputs=inputs, outputs=predictions)
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    return model

我们可以定义一些超参数，如下：

embedding_size = 128
filter_sizes = [3, 4, 5]
num_filters = 128
num_classes = 1

然后我们可以训练模型，同时记录每个 epoch 的准确率：

# 记录每个 epoch 的准确率
history = {'accuracy': [], 'val_accuracy': []}

# 训练模型并记录准确率
for i in range(10):
    model = TextCNN(max_len, vocab_size, embedding_size, filter_sizes, num_filters, num_classes)
    hist = model.fit(X_train, y_train, batch_size=128, epochs=1, validation_data=(X_test, y_test))
    history['accuracy'].append(hist.history['accuracy'][0])
    history['val_accuracy'].append(hist.history['val_accuracy'][0])

最后，我们可以绘制准确率的图表：

# 绘制准确率的图表
plt.plot(history['accuracy'], label='accuracy')
plt.plot(history['val_accuracy'], label='val_accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend()
plt.show()

您可以更改超参数并查看准确率图表的变化。