機器學習工具代碼
阿新 • • 發佈：2019-03-24
input per where 位置 n) enter pri http dense
(持續整理)
數組閾值處理
"""
img 為圖像數組，同時也是numpy數組
將img數據小於min的都設為min，同時將大於max的都設為max
"""
img[np.where(img < min)] = min
img[np.where(img > 250)] = max 歸一化和中心化
min = np.min(img)
max = np.max(img)
center = (min + max) / 2
img = (img - center) /(max - min) * 2最大聯通域
from skimage import measure def max_connected_domain_3D(arr): # 取相同數字的最大連通域 labels = measure.label(arr) # <1.2s t = np.bincount(labels.flatten())[1:] # <1.5s max_pixel = np.argmax(t) + 1 # 位置變了,去除了0 labels[labels != max_pixel] = 0 labels[labels == max_pixel] = 1 return labels.astype(np.uint8) # 測試 arr = [[1, 1, 0, 3], [1, 0, 3, 3], [0, 1, 3, 3], [0, 0, 0, 0]] arr = np.asarray(arr) print(arr) print(max_connected_domain_3D(arr))
\[
1 1 0 3\1 0 3 3\0 1 3 3\0 0 0 0\\]
\[
\Downarrow
\]
\[
0 0 0 1\0 0 1 1\0 0 1 1\0 0 0 0
\]
arr = np.squeeze(arr) # 從數組的形狀中刪除單維度條目，即把shape中為1的維度去掉
y = np.transpose(y,(1,2,0)) # 將數組的軸交換 (0, 1, 2) => (1, 2, 0)
"""
出處為寫nrrd文件的時候，可以考慮nrrd的數組存儲形式與正常數組維度不一致
"""繪制模型
from keras.utils import plot_model plot_model(model, "RUnet.png", True)
demo
from keras import models from keras import layers from keras import regularizers from keras.utils import plot_model def get_model(x, y, z): model = models.Sequential() model.add(layers.Conv3D(16, (3, 3, 2), activation='relu', input_shape=(x, y, z, 1))) model.add(layers.BatchNormalization()) model.add(layers.Conv3D(8, (3, 3, 2), activation='relu', kernel_regularizer=regularizers.l2(0.1))) model.add(layers.BatchNormalization()) model.add(layers.Conv3D(8, (3, 3, 2), activation='relu', kernel_regularizer=regularizers.l2(0.1))) model.add(layers.BatchNormalization()) model.add(layers.Conv3D(8, (3, 3, 1), activation='relu', kernel_regularizer=regularizers.l2(0.1))) model.add(layers.Dropout(rate=0.1)) model.add(layers.BatchNormalization()) model.add(layers.Flatten()) model.add(layers.BatchNormalization()) model.add(layers.Dense(13, activation='relu')) model.add(layers.BatchNormalization()) model.add(layers.Dense(8, activation='relu')) model.add(layers.BatchNormalization()) model.add(layers.Dense(8, activation='relu')) model.add(layers.Dense(2, activation='sigmoid')) model.summary() return model if __name__ == '__main__': model = get_model(125, 125, 10) plot_model(model, r"C:\Users\fan\Desktop\model.png", True)
效果圖
註：需要安裝graphviz
數據混淆
def data_confusion(data, label):
# 進行數據混淆
permutation = np.random.permutation(label.shape[0])
shuffled_data = data[permutation, :, :]
shuffled_label = label[permutation]
return shuffled_data, shuffled_label機器學習工具代碼