柚子快報邀請碼778899分享：Numpy實現(xiàn)Conv2D

http://yzkb.51969.com/

self.stride = stride

self.input_shape = input_shape

self.trainable = True

def initialize(self, optimizer):

Initialize the weights

filter_height, filter_width = self.filter_shape

channels = self.input_shape[0]

limit = 1 / math.sqrt(np.prod(self.filter_shape))

self.W = np.random.uniform(-limit, limit, size=(self.n_filters, channels, filter_height, filter_width))

self.w0 = np.zeros((self.n_filters, 1))

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):

batch_size, channels, height, width = X.shape

self.layer_input = X

Turn image shape into column shape

(enables dot product between input and weights)

self.X_col = image_to_column(X, self.filter_shape, stride=self.stride, output_shape=self.padding)

Turn weights into column shape

self.W_col = self.W.reshape((self.n_filters, -1))

Calculate output

output = self.W_col.dot(self.X_col) + self.w0

Reshape into (n_filters, out_height, out_width, batch_size)

output = output.reshape(self.output_shape() + (batch_size, ))

Redistribute axises so that batch size comes first

return output.transpose(3,0,1,2)

def backward_pass(self, accum_grad):

Reshape accumulated gradient into column shape

accum_grad = accum_grad.transpose(1, 2, 3, 0).reshape(self.n_filters, -1)

if self.trainable:

Take dot product between column shaped accum. gradient and column shape

layer input to determine the gradient at the layer with respect to layer weights

grad_w = accum_grad.dot(self.X_col.T).reshape(self.W.shape)

The gradient with respect to bias terms is the sum similarly to in Dense layer

grad_w0 = np.sum(accum_grad, axis=1, keepdims=True)

Update the layers weights

self.W = self.W_opt.update(self.W, grad_w)

self.w0 = self.w0_opt.update(self.w0, grad_w0)

Recalculate the gradient which will be propogated back to prev. layer

accum_grad = self.W_col.T.dot(accum_grad)

Reshape from column shape to image shape

accum_grad = column_to_image(accum_grad,

self.layer_input.shape,

self.filter_shape,

stride=self.stride,

output_shape=self.padding)

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由于文件比較大，這里只是將部分目錄大綱截圖出來，每個節(jié)點里面都包含大廠面經(jīng)、學習筆記、源碼講義、實戰(zhàn)項目、講解視頻，并且后續(xù)會持續(xù)更新

如果你覺得這些內容對你有幫助，可以掃碼獲取?。。。▊渥ython）

柚子快報邀請碼778899分享：Numpy實現(xiàn)Conv2D

http://yzkb.51969.com/

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