用python代码实现CRNN
时间: 2023-11-19 11:57:35 浏览: 47
CRNN是一种深度学习模型,结合了卷积神经网络(CNN)和循环神经网络(RNN),用于图像文本识别。下面是一个使用Keras实现CRNN的示例代码:
```python
from keras.models import Sequential, Model
from keras.layers import Input, Dense, Dropout, Activation, Flatten, Reshape, Permute
from keras.layers.convolutional import Conv2D, MaxPooling2D
from keras.layers.recurrent import GRU
from keras.layers.wrappers import TimeDistributed
from keras.layers.normalization import BatchNormalization
# 定义模型输入形状和参数
input_shape = (32, 280, 1)
num_classes = 10
max_text_len = 32
# 定义CNN层
def cnn_layers(input_shape):
# 定义模型
model = Sequential()
# 第一层卷积
model.add(Conv2D(64, (3, 3), activation='relu', padding='same', name='conv1', input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2), name='pool1'))
# 第二层卷积
model.add(Conv2D(128, (3, 3), activation='relu', padding='same', name='conv2'))
model.add(MaxPooling2D(pool_size=(2, 2), name='pool2'))
# 第三层卷积
model.add(Conv2D(256, (3, 3), activation='relu', padding='same', name='conv3'))
# 第四层卷积
model.add(Conv2D(256, (3, 3), activation='relu', padding='same', name='conv4'))
model.add(MaxPooling2D(pool_size=(1, 2), name='pool3'))
# 第五层卷积
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5'))
model.add(BatchNormalization(name='batchnorm1'))
# 第六层卷积
model.add(Conv2D(512, (3, 3), activation='relu', padding='same', name='conv6'))
model.add(BatchNormalization(name='batchnorm2'))
model.add(MaxPooling2D(pool_size=(1, 2), name='pool4'))
# 输出CNN结果
model.add(Permute((2, 1, 3), name='permute'))
model.add(TimeDistributed(Flatten(), name='timedistrib'))
return model
# 定义CRNN模型
def create_crnn(input_shape, num_classes, max_text_len):
# 定义CNN层
cnn = cnn_layers(input_shape)
# 定义RNN层
rnn = Sequential()
rnn.add(GRU(256, return_sequences=True, name='gru1'))
rnn.add(GRU(256, return_sequences=True, name='gru2'))
rnn.add(Dropout(0.25, name='dropout'))
# 定义最终输出层
input_data = Input(name='the_input', shape=input_shape, dtype='float32')
inner = cnn(input_data)
inner = rnn(inner)
y_pred = Dense(num_classes, activation='softmax', name='dense')(inner)
model = Model(inputs=input_data, outputs=y_pred)
# 定义模型输出形状
model.output_length = lambda x: cnn.output_shape[1]
return model
# 创建CRNN模型
crnn_model = create_crnn(input_shape, num_classes, max_text_len)
```
以上代码中,`cnn_layers`函数定义了CNN层,`create_crnn`函数定义了CRNN模型,包括CNN层、RNN层和最终输出层。`input_shape`参数指定了输入图像的形状,`num_classes`参数指定了输出类别数,`max_text_len`参数指定了输出文本的最大长度。最后,使用`create_crnn`函数创建CRNN模型并保存在`crnn_model`变量中。