CNN on tfidf作为输入

Question

我正在使用CNN进行假新闻检测，我对在keras和tensorflow中编码CNN是个新手。我需要关于创建CNN的帮助，该CNN将输入作为语句的形式，每个向量的长度为100，输出0或1取决于其预测值为false或true。

X_train.shape
# 10229, 100

X_train = np.expand_dims(X_train, axis=2)
X_train.shape
# 10229,100,1

# actual cnn model here
import tensorflow as tf
from tensorflow.keras import layers

# Conv1D + global max pooling

from keras.layers import Conv1D, MaxPooling1D, Embedding, Dropout, Flatten, Dense
from keras.layers import Input
text_len=100
from keras.models import Model

inp = Input(batch_shape=(None, text_len, 1))
conv2 = Conv1D(filters=128, kernel_size=5, activation='relu')(inp)
drop21 = Dropout(0.5)(conv2)
conv22 = Conv1D(filters=64, kernel_size=5, activation='relu')(drop21)
drop22 = Dropout(0.5)(conv22)
pool2 = MaxPooling1D(pool_size=2)(drop22)
flat2 = Flatten()(pool2)
out = Dense(1, activation='softmax')(flat2)

model = Model(inp, out)
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
model.fit(X_train, Y_train)

如果有人能给我一点解释的工作代码，我将不胜感激

Answer 1

在这个虚拟示例中，我使用了一个具有2D特性的Conv1D。Conv1D接受3D格式(n_samples、time_steps、features)的输入序列。如果您正在使用2D功能，则必须将其调整为3D功能。通常的选择是考虑你的特征，简单地扩展时间维度(轴1上的expand_dims)，没有理由假设tfidf/one-hot特征的位置/时间模式。

当你建立你的神经网络时，你从3D维度开始，你必须通过2D。从3D过渡到2D有很多可能性，简单的帖子是扁平化的，1个时间暗淡的池化层是无用的。如果您使用softmax作为最后一个激活层，请记住将一个维度传递给您的密集层，该维度等于您的类数

import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.models import *

## define variable
n_sample = 10229
text_len = 100

## create dummy data
X_train = np.random.uniform(0,1, (n_sample,text_len))
y_train = np.random.randint(0,2, n_sample)

## expand train dimnesion: pass from 2d to 3d
X_train = np.expand_dims(X_train, axis=1)
print(X_train.shape, y_train.shape)

## create model
inp = Input(shape=(1,text_len))
conv2 = Conv1D(filters=128, kernel_size=5, activation='relu', padding='same')(inp)
drop21 = Dropout(0.5)(conv2)
conv22 = Conv1D(filters=64, kernel_size=5, activation='relu', padding='same')(drop21)
drop22 = Dropout(0.5)(conv22)
pool2 = Flatten()(drop22) # this is an option to pass from 3d to 2d
out = Dense(2, activation='softmax')(pool2) # the output dim must be equal to the num of class if u use softmax

model = Model(inp, out)
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
model.fit(X_train, y_train, epochs=5)