如何避免在给定的 Convnet 中过度拟合

我正在尝试实现一个用于句子分类的 CNN 网络；我正在尝试遵循论文中提出的架构。为此，我正在使用 Keras（带有张量流）。以下是我的模型的总结：

____________________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to                     
====================================================================================================
input_4 (InputLayer)             (None, 56)            0                                            
____________________________________________________________________________________________________
embedding (Embedding)            (None, 56, 300)       6510000                                      
____________________________________________________________________________________________________
dropout_7 (Dropout)              (None, 56, 300)       0                                            
____________________________________________________________________________________________________
conv1d_10 (Conv1D)               (None, 54, 100)       90100                                        
____________________________________________________________________________________________________
conv1d_11 (Conv1D)               (None, 53, 100)       120100                                       
____________________________________________________________________________________________________
conv1d_12 (Conv1D)               (None, 52, 100)       150100                                       
____________________________________________________________________________________________________
max_pooling1d_10 (MaxPooling1D)  (None, 27, 100)       0                                            
____________________________________________________________________________________________________
max_pooling1d_11 (MaxPooling1D)  (None, 26, 100)       0                                            
____________________________________________________________________________________________________
max_pooling1d_12 (MaxPooling1D)  (None, 26, 100)       0                                            
____________________________________________________________________________________________________
flatten_10 (Flatten)             (None, 2700)          0                                            
____________________________________________________________________________________________________
flatten_11 (Flatten)             (None, 2600)          0                                            
____________________________________________________________________________________________________
flatten_12 (Flatten)             (None, 2600)          0                                            
____________________________________________________________________________________________________
concatenate_4 (Concatenate)      (None, 7900)          0                                            
____________________________________________________________________________________________________
dropout_8 (Dropout)              (None, 7900)          0                                            
____________________________________________________________________________________________________
dense_7 (Dense)                  (None, 50)            395050                                       
____________________________________________________________________________________________________
dense_8 (Dense)                  (None, 5)             255                                          
====================================================================================================
Total params: 7,265,605.0
Trainable params: 7,265,605.0
Non-trainable params: 0.0

对于给定的架构，我遇到了严重的过度拟合。以下是我的结果：

我无法理解过度拟合的原因是什么，请建议我对架构进行一些更改以避免这种情况。如果您需要更多信息，请告诉我。

源代码：

if model_type in ['CNN-non-static', 'CNN-static']:
    embedding_wts = train_word2vec( np.vstack((x_train, x_test, x_valid)), 
                                    ind_to_wrd, num_features = embedding_dim)
    if model_type == 'CNN-static':
        x_train = embedding_wts[0][x_train]
        x_test  = embedding_wts[0][x_test]
        x_valid = embedding_wts[0][x_valid]

elif model_type == 'CNN-rand':
    embedding_wts = None

else:
    raise ValueError("Unknown model type")

batch_size   = 50
filter_sizes = [3,4,5]
num_filters  = 75
dropout_prob = (0.5, 0.8)
hidden_dims  = 50

l2_reg = 0.3

# Deciding dimension of input based on the model
input_shape = (max_sent_len, embedding_dim) if model_type == "CNN-static" else (max_sent_len,)
model_input = Input(shape = input_shape)

# Static model do not have embedding layer
if model_type == "CNN-static":
    z = Dropout(dropout_prob[0])(model_input)
else:
    z = Embedding(vocab_size, embedding_dim, input_length = max_sent_len, name="embedding")(model_input)
    z = Dropout(dropout_prob[0])(z)

# Convolution layers
z1 = Conv1D(    filters=num_filters, kernel_size=3, 
                padding="valid", activation="relu", 
                strides=1)(z)
z1 = MaxPooling1D(pool_size=2)(z1)
z1 = Flatten()(z1)

z2 = Conv1D(    filters=num_filters, kernel_size=4, 
                padding="valid", activation="relu", 
                strides=1)(z)
z2 = MaxPooling1D(pool_size=2)(z2)
z2 = Flatten()(z2)

z3 = Conv1D(    filters=num_filters, kernel_size=5, 
                padding="valid", activation="relu",
                strides=1)(z)
z3 = MaxPooling1D(pool_size=2)(z3)
z3 = Flatten()(z3)

# Concatenate the output of all convolution layers
z = Concatenate()([z1, z2, z3])
z = Dropout(dropout_prob[1])(z)

# Dense(64, input_dim=64, kernel_regularizer=regularizers.l2(0.01), activity_regularizer=regularizers.l1(0.01))

z = Dense(hidden_dims, activation="relu", kernel_regularizer=regularizers.l2(0.01))(z)
model_output = Dense(N_category, activation="sigmoid")(z)

model = Model(model_input, model_output)
model.compile(loss="categorical_crossentropy", optimizer=optimizers.Adadelta(lr=1, decay=0.005), metrics=["accuracy"])
model.summary()