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ゼロから作るDeepLearningのCNNのコードです https://github.com/oreilly-japan/deep-learning-from-scratch/blob/471ff64c25d27eaad58d8b5a9e787249db974d44/ch07/apply_filter.py
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# coding: utf-8 | |
import numpy as np | |
from im2col import im2col | |
class Convolution: | |
def __init__(self, W, b, stride=1, pad=0): | |
self.W = W | |
self.b = b | |
self.stride = stride | |
self.pad = pad | |
def forward(self, x): | |
FN, C, FH, FW = self.W.shape | |
N, C, H, W = x.shape | |
out_h = int(1 + (H + 2*self.pad - FH) / self.stride) | |
out_w = int(1 + (W + 2*self.pad - FW) / self.stride) | |
col = im2col(x, FH, FW, self.stride, self.pad) | |
col_W = self.W.reshape(FN, -1).T | |
out = np.dot(col, col_W) + self.b | |
out = out.reshape(N, out_h, out_w, -1).transpose(0, 3, 1, 2) | |
return out |
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# coding: utf-8 | |
import numpy as np | |
def im2col(input_data, filter_h, filter_w, stride=1, pad=0): | |
""" | |
Parameters | |
---------- | |
input_data : (データ数, チャンネル, 高さ, 幅)の4次元配列からなる入力データ | |
filter_h : フィルターの高さ | |
filter_w : フィルターの幅 | |
stride : ストライド | |
pad : パディング | |
Returns | |
------- | |
col : 2次元配列 | |
""" | |
N, C, H, W = input_data.shape | |
out_h = (H + 2*pad - filter_h)//stride + 1 | |
out_w = (W + 2*pad - filter_w)//stride + 1 | |
img = np.pad(input_data, [(0,0), (0,0), (pad, pad), (pad, pad)], 'constant') | |
col = np.zeros((N, C, filter_h, filter_w, out_h, out_w)) | |
for y in range(filter_h): | |
y_max = y + stride*out_h | |
for x in range(filter_w): | |
x_max = x + stride*out_w | |
col[:, :, y, x, :, :] = img[:, :, y:y_max:stride, x:x_max:stride] | |
col = col.transpose(0, 4, 5, 1, 2, 3).reshape(N*out_h*out_w, -1) | |
return col |
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# coding: utf-8 | |
import numpy as np | |
from im2col import im2col | |
class Pooling: | |
def __init__(self, pool_h, pool_w, stride=1, pad=0): | |
self.pool_h = pool_h | |
self.pool_w = pool_w | |
self.stride = stride | |
self.pad = pad | |
def forward(self, x): | |
N, C, H, W = x.shape | |
out_h = int(1 + (H - self.pool_h) / self.stride) | |
out_w = int(1 + (W - self.pool_w) / self.stride) | |
col = im2col(x, self.pool_h, self.pool_w, self.stride, self.pad) | |
col = reshape(-1, self.pool_h*self.pool_w) | |
out = np.max(col, axis=1) | |
out = out.reshape(N, out_h, out_w, C).transpose(0, 3, 1, 2) | |
return out |
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