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