Question

我正在使用预训练的单词向量（fasttext），然后运行CNN模型。我似乎遇到embedding input和output layer的形状不匹配的情况。我检查了this个类似的问题，但仍然不知道如何解决。

以下是我的CNN体系结构：

#CNN architecture
max_seq_len =150
print("training CNN ...")
model = Sequential()
model.add(Embedding(nb_words, embed_dim,
      weights=[embedding_matrix], input_length=max_seq_len, trainable=False))
model.add(Conv1D(num_filters, 7, activation='relu', padding='same'))
model.add(MaxPooling1D(2))
model.add(Conv1D(num_filters, 7, activation='relu', padding='same'))
model.add(GlobalMaxPooling1D())
model.add(Dropout(0.5))
model.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Dense(num_classes, activation='sigmoid'))  #multi-label (k-hot encoding)

adam = optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])
model.summary()

输出：

training CNN ...
Model: "sequential_3"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_3 (Embedding)      (None, 150, 300)          2695800   
_________________________________________________________________
conv1d_5 (Conv1D)            (None, 150, 64)           134464    
_________________________________________________________________
max_pooling1d_3 (MaxPooling1 (None, 75, 64)            0         
_________________________________________________________________
conv1d_6 (Conv1D)            (None, 75, 64)            28736     
_________________________________________________________________
global_max_pooling1d_3 (Glob (None, 64)                0         
_________________________________________________________________
dropout_3 (Dropout)          (None, 64)                0         
_________________________________________________________________
dense_5 (Dense)              (None, 32)                2080      
_________________________________________________________________
dense_6 (Dense)              (None, 8)                 264       
=================================================================
Total params: 2,861,344
Trainable params: 165,544
Non-trainable params: 2,695,800

由于model.fit在输入和输出嵌入层不匹配上出现错误

#define callbacks
early_stopping = EarlyStopping(monitor='val_loss', min_delta=0.01, patience=4, verbose=1)
callbacks_list = [early_stopping]

#model training
hist = model.fit(word_seq_train, y_train, batch_size=batch_size, epochs=num_epochs, callbacks=callbacks_list, validation_split=0.1, shuffle=True, verbose=2)

错误：

---------------------------------------------------------------------------
    ValueError                                Traceback (most recent call last)
    <ipython-input-41-1a4b3093afeb> in <module>()
          3 callbacks_list = [early_stopping]
          4 #model training
    ----> 5 hist = model.fit(word_seq_train, y_train, batch_size=batch_size, epochs=num_epochs, callbacks=callbacks_list, validation_split=0.1, shuffle=True, verbose=2)

    2 frames
    /usr/local/lib/python3.6/dist-packages/keras/engine/training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
        143                             ': expected ' + names[i] + ' to have shape ' +
        144                             str(shape) + ' but got array with shape ' +
    --> 145                             str(data_shape))
        146     return data
        147 

    ValueError: Error when checking input: expected embedding_3_input to have shape (150,) but got array with shape (74,)

一些其他信息：

print(word_seq_train.shape)
print(y_train.shape)
print(embedding_matrix.shape)
>>(1446, 74)
>>(1446,)
>>(8986, 300)

Answer 1

来自@marco的评论：使用pad_sequences是有效的

max_seq_len =150
word_seq_train = sequence.pad_sequences(word_seq_train, maxlen=max_seq_len)

ValueError：检查输入时出错：预期embedding_1_input具有形状（150，）但具有形状（74，）的数组

1 个答案: