DL之DNN：自定义2层神经网络TwoLayerNet模型(封装为层级结构)利用MNIST数据集进行训练、GC对比
导读 神经网络算法封装为层级结构的作用。在神经网络算法中，通过将神经网络的组成元素实现为层，可以高效地计算梯度（反向传播法）。通过比较数值微分和误差反向传播法的结果，可以确认误差反向传播法的实现是否正确（梯度确认）。
目录
输出结果
设计思路
核心代码
代码实现过程错误记录
输出结果
设计思路
核心代码
class TwoLayerNet:
def __init__(self, input_size, hidden_size, output_size, weight_init_std = 0.01):
self.params = {}
self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size)
self.params['b1'] = np.zeros(hidden_size)
self.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size)
self.params['b2'] = np.zeros(output_size)
self.layers = OrderedDict()
self.layers['Affine1'] = Affine(self.params['W1'], self.params['b1'])
self.layers['Relu1'] = Relu()
self.layers['Affine2'] = Affine(self.params['W2'], self.params['b2'])
self.lastLayer = SoftmaxWithLoss()
def predict(self, x):
for layer in self.layers.values():
x = layer.forward(x)
return x
# x:输入数据, t:监督数据
def loss(self, x, t):
y = self.predict(x)
return self.lastLayer.forward(y, t)
def accuracy(self, x, t):
y = self.predict(x)
y = np.argmax(y, axis=1)
if t.ndim != 1 : t = np.argmax(t, axis=1)
accuracy = np.sum(y == t) / float(x.shape[0])
return accuracy
def gradient(self, x, t):
self.loss(x, t)
dout = 1
dout = self.lastLayer.backward(dout)
layers = list(self.layers.values())
layers.reverse()
for layer in layers:
dout = layer.backward(dout)
grads = {}
grads['W1'], grads['b1'] = self.layers['Affine1'].dW, self.layers['Affine1'].db
grads['W2'], grads['b2'] = self.layers['Affine2'].dW, self.layers['Affine2'].db
return grads
network_batch = TwoLayerNet(input_size=784, hidden_size=50, output_size=10)
grad_numerical = network_batch.numerical_gradient_api(x_batch, t_batch)
grad_backprop = network_batch.gradient(x_batch, t_batch)
代码实现过程错误记录
出现错误，待解决!!!
Traceback (most recent call last): File "F:\File_Python\Python_daydayup\190316.py", line 281, in grad = network.gradient(x_batch, t_batch) File "F:\File_Python\Python_daydayup\190316.py", line 222, in gradient self.loss(x, t) File "F:\File_Python\Python_daydayup\190316.py", line 193, in loss return self.lastLayer.forward(y, t) # ☆★☆★☆★☆★☆★☆★☆★☆★☆★☆★☆★☆★☆★☆ ------部分更改 File "F:\File_Python\Python_daydayup\190316.py", line 132, in forward self.loss = cross_entropy_error(self.y, self.t) File "F:\File_Python\Python_daydayup\190316.py", line 39, in cross_entropy_error return -np.sum(np.log(y[np.arange(batch_size), t.astype('int64')] + 1e-7)) / batch_size #t.astype('int64') IndexError: shape mismatch: indexing arrays could not be broadcast together with shapes (100,) (100,10)
相关文章DL之DNN：自定义2层神经网络TwoLayerNet模型(层级结构更高效)算法对MNIST数据集进行训练、预测
