Add 'util.py'

util.py

@@ -0,0 +1,99 @@
+# 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]