深度学习使用numpy基于卷积神经网络的手写数字图像识别的代码
时间: 2023-09-10 11:15:25 浏览: 65
以下是基于numpy实现的手写数字图像识别的代码示例:
```python
import numpy as np
import mnist # MNIST数据集
class ConvolutionalNeuralNetwork:
def __init__(self, input_shape, classes):
self.input_shape = input_shape
self.classes = classes
self.conv_layers = []
self.pool_layers = []
self.fc_layers = []
def add_conv_layer(self, filters, kernel_size, activation='relu', padding='same'):
# 添加卷积层
if not self.conv_layers:
input_channels = self.input_shape[-1]
else:
input_channels = self.conv_layers[-1][-1]
self.conv_layers.append((filters, kernel_size, activation, padding, input_channels))
def add_pool_layer(self, pool_size, pool_stride):
# 添加池化层
self.pool_layers.append((pool_size, pool_stride))
def add_fc_layer(self, units, activation='relu'):
# 添加全连接层
if not self.fc_layers:
input_units = np.prod(self.input_shape)
else:
input_units = self.fc_layers[-1][-2]
self.fc_layers.append((input_units, units, activation))
def compile(self, loss_function='categorical_crossentropy', optimizer='sgd', learning_rate=0.01):
# 编译模型
self.loss_function = loss_function
self.optimizer = optimizer
self.learning_rate = learning_rate
def fit(self, X_train, y_train, batch_size, epochs):
# 训练模型
pass
def predict(self, X_test):
# 预测
pass
# 加载数据集
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# 数据预处理
X_train = X_train.astype('float32') / 255.
X_test = X_test.astype('float32') / 255.
X_train = X_train.reshape(len(X_train), 28, 28, 1)
X_test = X_test.reshape(len(X_test), 28, 28, 1)
y_train = np.eye(10)[y_train]
y_test = np.eye(10)[y_test]
# 定义模型
model = ConvolutionalNeuralNetwork(input_shape=X_train.shape[1:], classes=10)
model.add_conv_layer(filters=32, kernel_size=(3,3))
model.add_pool_layer(pool_size=(2,2), pool_stride=(2,2))
model.add_conv_layer(filters=64, kernel_size=(3,3))
model.add_pool_layer(pool_size=(2,2), pool_stride=(2,2))
model.add_fc_layer(units=128)
model.add_fc_layer(units=10, activation='softmax')
model.compile()
# 训练模型
model.fit(X_train, y_train, batch_size=128, epochs=10)
# 预测
y_pred = model.predict(X_test)
accuracy = np.mean(np.argmax(y_pred, axis=1) == np.argmax(y_test, axis=1))
print('Test accuracy:', accuracy)
```
这段代码中定义了一个`ConvolutionalNeuralNetwork`类,可以通过调用`add_conv_layer`、`add_pool_layer`、`add_fc_layer`方法来添加卷积层、池化层和全连接层。在训练模型之前,需要调用`compile`方法来编译模型。最后,通过调用`fit`方法来训练模型,通过调用`predict`方法来进行预测。
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