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- # coding: utf-8
- import numpy as np
- def smooth_curve(x):
- """用于使损失函数的图形变圆滑
- 参考：http://glowingpython.blogspot.jp/2012/02/convolution-with-numpy.html
- """
- window_len = 11
- s = np.r_[x[window_len-1:0:-1], x, x[-1:-window_len:-1]]
- w = np.kaiser(window_len, 2)
- y = np.convolve(w/w.sum(), s, mode='valid')
- return y[5:len(y)-5]
- def shuffle_dataset(x, t):
- """打乱数据集
- Parameters
- ----------
- x : 训练数据
- t : 监督数据
- Returns
- -------
- x, t : 打乱的训练数据和监督数据
- """
- permutation = np.random.permutation(x.shape[0])
- x = x[permutation,:] if x.ndim == 2 else x[permutation,:,:,:]
- t = t[permutation]
- return x, t
- def conv_output_size(input_size, filter_size, stride=1, pad=0):
- return (input_size + 2*pad - filter_size) / stride + 1
- def im2col(input_data, filter_h, filter_w, stride=1, pad=0):
- """
- Parameters
- ----------
- input_data : 由(数据量, 通道, 高, 长)的4维数组构成的输入数据
- filter_h : 滤波器的高
- filter_w : 滤波器的长
- stride : 步幅
- pad : 填充
- Returns
- -------
- col : 2维数组
- """
- 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
- def col2im(col, input_shape, filter_h, filter_w, stride=1, pad=0):
- """
- Parameters
- ----------
- col :
- input_shape : 输入数据的形状（例：(10, 1, 28, 28)）
- filter_h :
- filter_w
- stride
- pad
- Returns
- -------
- """
- N, C, H, W = input_shape
- out_h = (H + 2*pad - filter_h)//stride + 1
- out_w = (W + 2*pad - filter_w)//stride + 1
- col = col.reshape(N, out_h, out_w, C, filter_h, filter_w).transpose(0, 3, 4, 5, 1, 2)
- img = np.zeros((N, C, H + 2*pad + stride - 1, W + 2*pad + stride - 1))
- for y in range(filter_h):
- y_max = y + stride*out_h
- for x in range(filter_w):
- x_max = x + stride*out_w
- img[:, :, y:y_max:stride, x:x_max:stride] += col[:, :, y, x, :, :]
- return img[:, :, pad:H + pad, pad:W + pad]
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