python - 网格搜索用于keras深度学习模型分割GridSearchCV

model = KerasClassifier(build_fn = unet, verbose=0) epochs = [10, 50, 100] param_grid = dict(epochs=epochs) grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=3,scoring='neg_mean_squared_error') dataset = tf.data.Dataset.from_generator(lambda: map(tuple, genaug), (tf.int32, tf.int32), (tf.TensorShape([]), tf.TensorShape([None]))) X,Y = dataset.next() grid_result = grid.fit(X,Y)

class DataGenerator(Sequence): """Generates data for Keras Sequence based data generator. Suitable for building data generator for training and prediction. """ def __init__(self, list_IDs, image_path, mask_path, to_fit=True, batch_size=32, dim=(512, 512), dimy=(512, 512), n_channels=1, n_classes=10, shuffle=True): """Initialization :param list_IDs: list of all 'label' ids to use in the generator :param image_path: path to images location :param mask_path: path to masks location :param to_fit: True to return X and y, False to return X only :param batch_size: batch size at each iteration :param dim: tuple indicating image dimension :param n_channels: number of image channels :param n_classes: number of output masks :param shuffle: True to shuffle label indexes after every epoch """ self.list_IDs = list_IDs self.image_path = image_path self.mask_path = mask_path self.to_fit = to_fit self.batch_size = batch_size self.dim = dim self.dimy = dimy self.n_channels = n_channels self.n_classes = n_classes self.shuffle = shuffle self.on_epoch_end() self.n = 0 self.max = self.__len__() def __next__(self): if self.n >= self.max: self.n = 0 result = self.__getitem__(self.n) self.n += 1 return result def __len__(self): """Denotes the number of batches per epoch :return: number of batches per epoch """ return int(np.floor(len(self.list_IDs) / self.batch_size)) def __getitem__(self, index): """Generate one batch of data :param index: index of the batch :return: X and y when fitting. X only when predicting """ # Generate indexes of the batch indexes = self.indexes[index * self.batch_size:(index + 1) * self.batch_size] # Find list of IDs list_IDs_temp = [self.list_IDs[k] for k in indexes] # Generate data X = self._generate_X(list_IDs_temp) if self.to_fit: y = self._generate_y(list_IDs_temp) return X, y else: return X def on_epoch_end(self): """Updates indexes after each epoch """ self.indexes = np.arange(len(self.list_IDs)) if self.shuffle == True: np.random.shuffle(self.indexes) def _generate_X(self, list_IDs_temp): """Generates data containing batch_size images :param list_IDs_temp: list of label ids to load :return: batch of images """ # Initialization X = np.empty((self.batch_size, *self.dim,self.n_channels)) # Generate data for i, ID in enumerate(list_IDs_temp): # Store sample X[i,] = self._load_dicom_image(self.image_path + '/'+ ID) #X=np.expand_dims(X, 4) return X def _generate_y(self, list_IDs_temp): """Generates data containing batch_size masks :param list_IDs_temp: list of label ids to load :return: batch if masks """ y = np.empty((self.batch_size, *self.dimy,self.n_channels)) # Generate data for i, ID in enumerate(list_IDs_temp): # Store sample #y[i,] = self._load_grayscale_image_VTK(self.mask_path + '/'+'label_'+ID[6:15]+'.png') y[i,] = self._load_grayscale_image_VTK(self.mask_path + '/' + 'label_' + ID[6:15] + '.png') return y def _load_grayscale_image(self, image_path): """Load grayscale image :param image_path: path to image to load :return: loaded image """ img = cv2.imread(image_path) img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) img = cv2.rotate(img, cv2.ROTATE_90_COUNTERCLOCKWISE) img = img / 255 ''' img2 = img.astype(np.float32) # --- the following holds the square root of the sum of squares of the image dimensions --- # --- this is done so that the entire width/height of the original image is used to express the complete circular range of the resulting polar image --- value = np.sqrt(((img2.shape[0] / 2.0) ** 2.0) + ((img2.shape[1] / 2.0) ** 2.0)) polar_image = cv2.linearPolar(img2, (img2.shape[0] / 2, img2.shape[1] / 2), value, cv2.WARP_FILL_OUTLIERS) polar_image = polar_image.astype(np.uint8) img=polar_image ''' img = np.expand_dims(img, axis=2) img=img.astype(np.float32) return img def _load_dicom_image(self, image_path): """Load grayscale image :param image_path: path to image to load :return: loaded image """ img = load_dicom(image_path) img = img / np.max(img) #self.polar(img) return img def _load_grayscale_image_VTK(self, image_path): """Load grayscale image :param image_path: path to image to load :return: loaded image """ img = vtk.vtkPNGReader() img.SetFileName(os.path.normpath(image_path)) img.Update() _extent = img.GetDataExtent() ConstPixelDims = [_extent[1]-_extent[0]+1, _extent[3]-_extent[2]+1, _extent[5]-_extent[4]+1] img_data = img.GetOutput() datapointer = img_data.GetPointData() assert (datapointer.GetNumberOfArrays()==1) vtkarray = datapointer.GetArray(0) img = vtk.util.numpy_support.vtk_to_numpy(vtkarray) img = img.reshape(ConstPixelDims, order='F') img = img / np.max(img) img = img.astype('float32') return img