【python实现卷积神经网络】全连接层实现

代码来源：

卷积神经⽹络中卷积层Conv2D（带stride、padding）的具体实现：

激活函数的实现（sigmoid、softmax、tanh、relu、leakyrelu、elu、selu、softplus）：损失函数定义（均⽅误差、交叉熵损失）：

优化器的实现（SGD、Nesterov、Adagrad、Adadelta、RMSprop、Adam）：卷积层反向传播过程：

全连接层实现代码：

class Dense(Layer):

\"\"\"A fully-connected NN layer. Parameters: ----------- n_units: int

The number of neurons in the layer. input_shape: tuple

The expected input shape of the layer. For dense layers a single digit specifying the number of features of the input. Must be specified if it is the first layer in the network. \"\"\"

def __init__(self, n_units, input_shape=None): self.layer_input = None

self.input_shape = input_shape self.n_units = n_units self.trainable = True self.W = None self.w0 = None

def initialize(self, optimizer): # Initialize the weights

limit = 1 / math.sqrt(self.input_shape[0])

self.W = np.random.uniform(-limit, limit, (self.input_shape[0], self.n_units)) self.w0 = np.zeros((1, self.n_units)) # Weight optimizers

self.W_opt = copy.copy(optimizer) self.w0_opt = copy.copy(optimizer)

def parameters(self):

return np.prod(self.W.shape) + np.prod(self.w0.shape) def forward_pass(self, X, training=True): self.layer_input = X

return X.dot(self.W) + self.w0

def backward_pass(self, accum_grad):

# Save weights used during forwards pass W = self.W

if self.trainable:

# Calculate gradient w.r.t layer weights

grad_w = self.layer_input.T.dot(accum_grad)

grad_w0 = np.sum(accum_grad, axis=0, keepdims=True) # Update the layer weights

self.W = self.W_opt.update(self.W, grad_w) self.w0 = self.w0_opt.update(self.w0, grad_w0)

# Return accumulated gradient for next layer

# Calculated based on the weights used during the forward pass accum_grad = accum_grad.dot(W.T) return accum_grad def output_shape(self): return (self.n_units, )