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Numpy實(shí)現(xiàn)卷積神經(jīng)網(wǎng)絡(luò)(CNN)的示例

 更新時間：2020年10月09日 11:54:44   作者：chenxiangzhen  
這篇文章主要介紹了Numpy實(shí)現(xiàn)卷積神經(jīng)網(wǎng)絡(luò)(CNN)的示例，幫助大家更好的理解和使用Numpy，感興趣的朋友可以了解下
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('錯誤：過濾器大小必須是奇數(shù)')
    sys.exit()

  # 定義一個空的特征圖，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, :]

    # 檢查單個過濾器是否有多個通道。如果有，那么每個通道將對圖像進(jìn)行卷積。所有卷積的結(jié)果加起來得到一個特征圖。
    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)

以上就是Numpy實(shí)現(xiàn)卷積神經(jīng)網(wǎng)絡(luò)(CNN)的示例的詳細(xì)內(nèi)容，更多關(guān)于Numpy實(shí)現(xiàn)卷積神經(jīng)網(wǎng)絡(luò)的資料請關(guān)注腳本之家其它相關(guān)文章！

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