您好,登錄后才能下訂單哦!
Numpy實(shí)現(xiàn)卷積神經(jīng)網(wǎng)絡(luò)的方法?針對(duì)這個(gè)問題,這篇文章詳細(xì)介紹了相對(duì)應(yīng)的分析和解答,希望可以幫助更多想解決這個(gè)問題的小伙伴找到更簡單易行的方法。
import numpy as np import sys def conv_(img, conv_filter): filter_size = conv_filter.shape[1] result = np.zeros((img.shape)) # 循環(huán)遍歷圖像以應(yīng)用卷積運(yùn)算 for r in np.uint16(np.arange(filter_size/2.0, img.shape[0]-filter_size/2.0+1)): for c in np.uint16(np.arange(filter_size/2.0, img.shape[1]-filter_size/2.0+1)): # 卷積的區(qū)域 curr_region = img[r-np.uint16(np.floor(filter_size/2.0)):r+np.uint16(np.ceil(filter_size/2.0)), c-np.uint16(np.floor(filter_size/2.0)):c+np.uint16(np.ceil(filter_size/2.0))] # 卷積操作 curr_result = curr_region * conv_filter conv_sum = np.sum(curr_result) # 將求和保存到特征圖中 result[r, c] = conv_sum # 裁剪結(jié)果矩陣的異常值 final_result = result[np.uint16(filter_size/2.0):result.shape[0]-np.uint16(filter_size/2.0), np.uint16(filter_size/2.0):result.shape[1]-np.uint16(filter_size/2.0)] return final_result def conv(img, conv_filter): # 檢查圖像通道的數(shù)量是否與過濾器深度匹配 if len(img.shape) > 2 or len(conv_filter.shape) > 3: if img.shape[-1] != conv_filter.shape[-1]: print("錯(cuò)誤:圖像和過濾器中的通道數(shù)必須匹配") sys.exit() # 檢查過濾器是否是方陣 if conv_filter.shape[1] != conv_filter.shape[2]: print('錯(cuò)誤:過濾器必須是方陣') sys.exit() # 檢查過濾器大小是否是奇數(shù) if conv_filter.shape[1] % 2 == 0: print('錯(cuò)誤:過濾器大小必須是奇數(shù)') sys.exit() # 定義一個(gè)空的特征圖,用于保存過濾器與圖像的卷積輸出 feature_maps = np.zeros((img.shape[0] - conv_filter.shape[1] + 1, img.shape[1] - conv_filter.shape[1] + 1, conv_filter.shape[0])) # 卷積操作 for filter_num in range(conv_filter.shape[0]): print("Filter ", filter_num + 1) curr_filter = conv_filter[filter_num, :] # 檢查單個(gè)過濾器是否有多個(gè)通道。如果有,那么每個(gè)通道將對(duì)圖像進(jìn)行卷積。所有卷積的結(jié)果加起來得到一個(gè)特征圖。 if len(curr_filter.shape) > 2: conv_map = conv_(img[:, :, 0], curr_filter[:, :, 0]) for ch_num in range(1, curr_filter.shape[-1]): conv_map = conv_map + conv_(img[:, :, ch_num], curr_filter[:, :, ch_num]) else: conv_map = conv_(img, curr_filter) feature_maps[:, :, filter_num] = conv_map return feature_maps def pooling(feature_map, size=2, stride=2): # 定義池化操作的輸出 pool_out = np.zeros((np.uint16((feature_map.shape[0] - size + 1) / stride + 1), np.uint16((feature_map.shape[1] - size + 1) / stride + 1), feature_map.shape[-1])) for map_num in range(feature_map.shape[-1]): r2 = 0 for r in np.arange(0, feature_map.shape[0] - size + 1, stride): c2 = 0 for c in np.arange(0, feature_map.shape[1] - size + 1, stride): pool_out[r2, c2, map_num] = np.max([feature_map[r: r+size, c: c+size, map_num]]) c2 = c2 + 1 r2 = r2 + 1 return pool_out
import skimage.data import numpy import matplotlib import matplotlib.pyplot as plt import NumPyCNN as numpycnn # 讀取圖像 img = skimage.data.chelsea() # 轉(zhuǎn)成灰度圖像 img = skimage.color.rgb2gray(img) # 初始化卷積核 l1_filter = numpy.zeros((2, 3, 3)) # 檢測垂直邊緣 l1_filter[0, :, :] = numpy.array([[[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]]]) # 檢測水平邊緣 l1_filter[1, :, :] = numpy.array([[[1, 1, 1], [0, 0, 0], [-1, -1, -1]]]) """ 第一個(gè)卷積層 """ # 卷積操作 l1_feature_map = numpycnn.conv(img, l1_filter) # ReLU l1_feature_map_relu = numpycnn.relu(l1_feature_map) # Pooling l1_feature_map_relu_pool = numpycnn.pooling(l1_feature_map_relu, 2, 2) """ 第二個(gè)卷積層 """ # 初始化卷積核 l2_filter = numpy.random.rand(3, 5, 5, l1_feature_map_relu_pool.shape[-1]) # 卷積操作 l2_feature_map = numpycnn.conv(l1_feature_map_relu_pool, l2_filter) # ReLU l2_feature_map_relu = numpycnn.relu(l2_feature_map) # Pooling l2_feature_map_relu_pool = numpycnn.pooling(l2_feature_map_relu, 2, 2) """ 第三個(gè)卷積層 """ # 初始化卷積核 l3_filter = numpy.random.rand(1, 7, 7, l2_feature_map_relu_pool.shape[-1]) # 卷積操作 l3_feature_map = numpycnn.conv(l2_feature_map_relu_pool, l3_filter) # ReLU l3_feature_map_relu = numpycnn.relu(l3_feature_map) # Pooling l3_feature_map_relu_pool = numpycnn.pooling(l3_feature_map_relu, 2, 2) """ 結(jié)果可視化 """ fig0, ax0 = plt.subplots(nrows=1, ncols=1) ax0.imshow(img).set_cmap("gray") ax0.set_title("Input Image") ax0.get_xaxis().set_ticks([]) ax0.get_yaxis().set_ticks([]) plt.savefig("in_img1.png", bbox_inches="tight") plt.close(fig0) # 第一層 fig1, ax1 = plt.subplots(nrows=3, ncols=2) ax1[0, 0].imshow(l1_feature_map[:, :, 0]).set_cmap("gray") ax1[0, 0].get_xaxis().set_ticks([]) ax1[0, 0].get_yaxis().set_ticks([]) ax1[0, 0].set_title("L1-Map1") ax1[0, 1].imshow(l1_feature_map[:, :, 1]).set_cmap("gray") ax1[0, 1].get_xaxis().set_ticks([]) ax1[0, 1].get_yaxis().set_ticks([]) ax1[0, 1].set_title("L1-Map2") ax1[1, 0].imshow(l1_feature_map_relu[:, :, 0]).set_cmap("gray") ax1[1, 0].get_xaxis().set_ticks([]) ax1[1, 0].get_yaxis().set_ticks([]) ax1[1, 0].set_title("L1-Map1ReLU") ax1[1, 1].imshow(l1_feature_map_relu[:, :, 1]).set_cmap("gray") ax1[1, 1].get_xaxis().set_ticks([]) ax1[1, 1].get_yaxis().set_ticks([]) ax1[1, 1].set_title("L1-Map2ReLU") ax1[2, 0].imshow(l1_feature_map_relu_pool[:, :, 0]).set_cmap("gray") ax1[2, 0].get_xaxis().set_ticks([]) ax1[2, 0].get_yaxis().set_ticks([]) ax1[2, 0].set_title("L1-Map1ReLUPool") ax1[2, 1].imshow(l1_feature_map_relu_pool[:, :, 1]).set_cmap("gray") ax1[2, 0].get_xaxis().set_ticks([]) ax1[2, 0].get_yaxis().set_ticks([]) ax1[2, 1].set_title("L1-Map2ReLUPool") plt.savefig("L1.png", bbox_inches="tight") plt.close(fig1) # 第二層 fig2, ax2 = plt.subplots(nrows=3, ncols=3) ax2[0, 0].imshow(l2_feature_map[:, :, 0]).set_cmap("gray") ax2[0, 0].get_xaxis().set_ticks([]) ax2[0, 0].get_yaxis().set_ticks([]) ax2[0, 0].set_title("L2-Map1") ax2[0, 1].imshow(l2_feature_map[:, :, 1]).set_cmap("gray") ax2[0, 1].get_xaxis().set_ticks([]) ax2[0, 1].get_yaxis().set_ticks([]) ax2[0, 1].set_title("L2-Map2") ax2[0, 2].imshow(l2_feature_map[:, :, 2]).set_cmap("gray") ax2[0, 2].get_xaxis().set_ticks([]) ax2[0, 2].get_yaxis().set_ticks([]) ax2[0, 2].set_title("L2-Map3") ax2[1, 0].imshow(l2_feature_map_relu[:, :, 0]).set_cmap("gray") ax2[1, 0].get_xaxis().set_ticks([]) ax2[1, 0].get_yaxis().set_ticks([]) ax2[1, 0].set_title("L2-Map1ReLU") ax2[1, 1].imshow(l2_feature_map_relu[:, :, 1]).set_cmap("gray") ax2[1, 1].get_xaxis().set_ticks([]) ax2[1, 1].get_yaxis().set_ticks([]) ax2[1, 1].set_title("L2-Map2ReLU") ax2[1, 2].imshow(l2_feature_map_relu[:, :, 2]).set_cmap("gray") ax2[1, 2].get_xaxis().set_ticks([]) ax2[1, 2].get_yaxis().set_ticks([]) ax2[1, 2].set_title("L2-Map3ReLU") ax2[2, 0].imshow(l2_feature_map_relu_pool[:, :, 0]).set_cmap("gray") ax2[2, 0].get_xaxis().set_ticks([]) ax2[2, 0].get_yaxis().set_ticks([]) ax2[2, 0].set_title("L2-Map1ReLUPool") ax2[2, 1].imshow(l2_feature_map_relu_pool[:, :, 1]).set_cmap("gray") ax2[2, 1].get_xaxis().set_ticks([]) ax2[2, 1].get_yaxis().set_ticks([]) ax2[2, 1].set_title("L2-Map2ReLUPool") ax2[2, 2].imshow(l2_feature_map_relu_pool[:, :, 2]).set_cmap("gray") ax2[2, 2].get_xaxis().set_ticks([]) ax2[2, 2].get_yaxis().set_ticks([]) ax2[2, 2].set_title("L2-Map3ReLUPool") plt.savefig("L2.png", bbox_inches="tight") plt.close(fig2) # 第三層 fig3, ax3 = plt.subplots(nrows=1, ncols=3) ax3[0].imshow(l3_feature_map[:, :, 0]).set_cmap("gray") ax3[0].get_xaxis().set_ticks([]) ax3[0].get_yaxis().set_ticks([]) ax3[0].set_title("L3-Map1") ax3[1].imshow(l3_feature_map_relu[:, :, 0]).set_cmap("gray") ax3[1].get_xaxis().set_ticks([]) ax3[1].get_yaxis().set_ticks([]) ax3[1].set_title("L3-Map1ReLU") ax3[2].imshow(l3_feature_map_relu_pool[:, :, 0]).set_cmap("gray") ax3[2].get_xaxis().set_ticks([]) ax3[2].get_yaxis().set_ticks([]) ax3[2].set_title("L3-Map1ReLUPool") plt.savefig("L3.png", bbox_inches="tight") plt.close(fig3)
關(guān)于Numpy實(shí)現(xiàn)卷積神經(jīng)網(wǎng)絡(luò)的方法問題的解答就分享到這里了,希望以上內(nèi)容可以對(duì)大家有一定的幫助,如果你還有很多疑惑沒有解開,可以關(guān)注億速云行業(yè)資訊頻道了解更多相關(guān)知識(shí)。
免責(zé)聲明:本站發(fā)布的內(nèi)容(圖片、視頻和文字)以原創(chuàng)、轉(zhuǎn)載和分享為主,文章觀點(diǎn)不代表本網(wǎng)站立場,如果涉及侵權(quán)請(qǐng)聯(lián)系站長郵箱:is@yisu.com進(jìn)行舉報(bào),并提供相關(guān)證據(jù),一經(jīng)查實(shí),將立刻刪除涉嫌侵權(quán)內(nèi)容。