python - 如何在 Keras 中训练（拟合）连接模型？

Question

您好，这是经过一些编辑后我为分类 IMDB 电影创建的模型。它由两个连接模型（LSTM 和 CNN）组成

import numpy as np
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence
np.random.seed(7)

top_words = 5000
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)
max_review_length = 5
X_train = sequence.pad_sequences(X_train, maxlen=max_review_length)
X_test = sequence.pad_sequences(X_test, maxlen=max_review_length)   


def cnn_lstm_merged():
        embedding_vecor_length = 32
        cnn_model = Sequential()
        cnn_model.add(Embedding(top_words, embedding_vecor_length, input_length=max_review_length))
        cnn_model.add(Conv1D(filters=32, kernel_size=3, padding='same', activation='relu'))
        cnn_model.add(MaxPooling1D(pool_size=2))
        cnn_model.add(Flatten())

        lstm_model = Sequential()
        lstm_model.add(Embedding(top_words, embedding_vecor_length, input_length=max_review_length))
        lstm_model.add(LSTM(64, activation = 'relu', return_sequences=True))
        lstm_model.add(Flatten())

        merge = Concatenate([lstm_model, cnn_model])
        hidden = Dense(1, activation = 'sigmoid')

        conc_model = Sequential()
        conc_model.add(merge)
        conc_model.add(hidden)

        conc_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
        history = conc_model.fit([X_train, X_train], [y_train, y_train], np.reshape([y_train, y_train],(25000,2)),epochs=3, batch_size=(64,64))
        return history

x_train shape is (25000, 5) y_train shape is (25000,) 我不知道如何训练这个模型，因为有问题。错误是：

File "/home/pythonist/Desktop/EnsemblingLSTM_CONV/train.py", line 79, in <module>
    cnn_lstm_model = cnn_lstm_merged()
  File "/home/pythonist/Desktop/EnsemblingLSTM_CONV/train.py", line 69, in cnn_lstm_merged
    history = conc_model.fit([X_train, X_train], [y_train, y_train], np.reshape([y_train, y_train],(25000,2)),epochs=3, batch_size=(64,64))
TypeError: fit() got multiple values for argument 'batch_size'

即使我将 batch_size = (64, 64) 更改为 batch_size = 64 仍然给我一个错误。而且我不确定我是否正确地重塑了 y_train。如何在这个级联模型中训练和预测输出数据？谢谢

标签： pythonkerasconv-neural-networklstm