python - 在 Keras 中建立功能模型

Question

我只是为了好玩(继续教育)而摆弄 Keras，并且在指定 CNN 中的数据结构时遇到了一些问题。

Tutorial: https://github.com/bhaveshoswal/CNN-text-classification-keras
Data: https://catalog.data.gov/dataset/consumer-complaint-database/resource/2f297213-7198-4be1-af1e-2d2623e7f6e9

所以我已经设置了数据处理并且看起来是正确的。

from keras.layers import Input, Dense, Embedding, Conv2D, MaxPool2D
from keras.layers import Reshape, Flatten, Dropout, Concatenate
from keras.callbacks import ModelCheckpoint
from keras.optimizers import Adam
from keras.models import Model
from sklearn.model_selection import train_test_split
from helper_functions import load_data

print('Loading data')
x, y, vocabulary, vocabulary_inv = load_data()

# x.shape -> (50000, 5371)
# y.shape -> (50000, 10)
# len(vocabulary) -> 50111
# len(vocabulary_inv) -> 50111

X_train, X_test, y_train, y_test = train_test_split( x, y, test_size=0.2, random_state=42)

# X_train.shape -> (40000, 5371)
# y_train.shape -> (40000, 18)
# X_test.shape -> (10000, 5371)
# y_test.shape -> (10000, 18)

当我尝试拟合模型时，最终出现与数据集维度相关的错误。

sequence_length = x.shape[1] # 56
vocabulary_size = len(vocabulary_inv) # 18765
embedding_dim = 256
filter_sizes = [3,4,5]
num_filters = 512
drop = 0.5

epochs = 100
batch_size = 30

# this returns a tensor
print("Creating Model...")

inputs = Input(shape=(sequence_length,), dtype='int32')
embedding = Embedding(input_dim=vocabulary_size, output_dim=embedding_dim, input_length=sequence_length)(inputs)
reshape = Reshape((sequence_length,embedding_dim,1))(embedding)

conv_0 = Conv2D(num_filters, kernel_size=(filter_sizes[0], embedding_dim), padding='valid', kernel_initializer='normal', activation='relu')(reshape)
conv_1 = Conv2D(num_filters, kernel_size=(filter_sizes[1], embedding_dim), padding='valid', kernel_initializer='normal', activation='relu')(reshape)
conv_2 = Conv2D(num_filters, kernel_size=(filter_sizes[2], embedding_dim), padding='valid', kernel_initializer='normal', activation='relu')(reshape)

maxpool_0 = MaxPool2D(pool_size=(sequence_length - filter_sizes[0] + 1, 1), strides=(1,1), padding='valid')(conv_0)
maxpool_1 = MaxPool2D(pool_size=(sequence_length - filter_sizes[1] + 1, 1), strides=(1,1), padding='valid')(conv_1)
maxpool_2 = MaxPool2D(pool_size=(sequence_length - filter_sizes[2] + 1, 1), strides=(1,1), padding='valid')(conv_2)

concatenated_tensor = Concatenate(axis=1)([maxpool_0, maxpool_1, maxpool_2])
flatten = Flatten()(concatenated_tensor)
dropout = Dropout(drop)(flatten)
output = Dense(units=18, activation='softmax')(dropout)

# this creates a model that includes
model = Model(inputs=inputs, outputs=output)

checkpoint = ModelCheckpoint('weights.{epoch:03d}-{val_acc:.4f}.hdf5', monitor='val_acc', verbose=1, save_best_only=True, mode='auto')
adam = Adam(lr=1e-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)

#model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

print("Traning Model...")
model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, callbacks=[checkpoint], validation_data=(X_test, y_test))  # starts training

这是错误消息:

Traning Model...
Traceback (most recent call last):

  File "<ipython-input-294-835f1e289b39>", line 41, in <module>
    model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, callbacks=[checkpoint], validation_data=(X_test, y_test))  # starts training

  File "/Users/abrahammathew/anaconda3/lib/python3.6/site-packages/keras/engine/training.py", line 1630, in fit
    batch_size=batch_size)

  File "/Users/abrahammathew/anaconda3/lib/python3.6/site-packages/keras/engine/training.py", line 1480, in _standardize_user_data
    exception_prefix='target')

  File "/Users/abrahammathew/anaconda3/lib/python3.6/site-packages/keras/engine/training.py", line 123, in _standardize_input_data
    str(data_shape))

ValueError: Error when checking target: expected dense_11 to have shape (1,) but got array with shape (18,)

鉴于此错误，使用功能 API 创建 Keras 模型时数据维度应该是什么样子。

Answer 1

你有sparse_categorical_crossentropy，它只需要类的整数标签，而你已经给出了编码版本(18，)。因此，您需要更改 loss='categorical_crossentropy' 来解决问题。