def gradient_descent(W, eta=0.001, tolerance=0.000001): # Variable to store the W value before update. # This will help to check for convergence. W_prev = 0 # t is an iteration counter to compute the number of steps t = 0 # Save the current weights to a new list and # append the updated weights in each iteration to the same Ws = [W] # Perform the update until convergence # Convergence is said to have taken place if # the absolute difference between the previous and # updated weight is less than the tolerance while ():空格里面应该填啥

时间: 2024-02-14 10:33:15 浏览: 26
在这个代码段中,`while`循环的条件没有给出。根据注释,循环应该继续进行直到达到收敛状态。因此,循环的条件应该是: ```python while (np.abs(Ws[-1] - W_prev) > tolerance): ``` 这个条件检查前一次更新的权重与当前更新的权重之间的绝对差是否大于给定的容差。如果是,则继续进行更新;如果不是,则认为已经达到了收敛状态,循环结束。
相关问题

self.network_parameters = OrderedDict(num_filters_base=config.num_filters_base, activation=config.activation, dropout_ratio=config.dropout_ratio, num_levels=config.num_levels, heatmap_initialization=True, data_format=self.data_format) if config.model == 'unet': self.network = Unet self.clip_gradient_global_norm = 100000.0

这部分代码主要是设置网络的参数和模型选择。 首先,使用`OrderedDict`创建了一个名为`network_parameters`的有序字典,其中包含了一些网络的参数。这些参数包括`num_filters_base`表示基础滤波器数量,`activation`表示激活函数,`dropout_ratio`表示随机失活比率,`num_levels`表示网络的层数,`heatmap_initialization`表示是否对热图进行初始化,`data_format`表示数据格式。 接下来,通过判断`config.model`的值是否为'unet'来选择对应的模型。如果是'unet',则将`self.network`设置为Unet类的引用。 最后,设置了一个全局梯度裁剪的阈值`clip_gradient_global_norm`为100000.0。这个阈值可以用来控制梯度更新的范围,防止梯度爆炸的问题。 这部分代码主要是设置网络相关的参数和选择模型,具体的网络结构和训练过程需要在其他部分的代码中实现。

如果要等价实现下列代码,应该如何更改def edge_detection(image, type): if type == 'roberts': roberts_x = np.array([[-1, 0], [0, 1]]) roberts_y = np.array([[0, -1], [1, 0]]) # roberts 算子计算x和y方向的梯度 gradient_x_roberts = cv2.filter2D(image, -1, roberts_x) gradient_y_roberts = cv2.filter2D(image, -1, roberts_y) edges_roberts = cv2.add(np.abs(gradient_x_roberts), np.abs(gradient_y_roberts)) edges_roberts = np.uint8(edges_roberts) return edges_roberts elif type == 'prewitt': prewitt_x = np.array([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]]) prewitt_y = np.array([[-1, -1, -1], [0, 0, 0], [1, 1, 1]]) # prewitt 算子计算x和y方向的梯度 gradient_x_prewitt = cv2.filter2D(image, -1, prewitt_x) gradient_y_prewitt = cv2.filter2D(image, -1, prewitt_y) edges_prewitt = cv2.add(np.abs(gradient_x_prewitt), np.abs(gradient_y_prewitt)) edges_prewitt = np.uint8(edges_prewitt) return edges_prewitt elif type == 'sobel': # Sobel算子 sobel_x = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]) sobel_y = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]]) # Sobel 算子计算x和y方向的梯度 gradient_x_sobel = cv2.filter2D(image, -1, sobel_x) gradient_y_sobel = cv2.filter2D(image, -1, sobel_y) edges_sobel = cv2.add(np.abs(gradient_x_sobel), np.abs(gradient_y_sobel)) edges_sobel = np.uint8(edges_sobel) return edges_sobel elif type == 'canny': edges = cv2.Canny(image, threshold1=50, threshold2=100) return edges else: raise NotImplementedError

为了实现等价功能,可以使用下列代码: ```python def edge_detection(image, type): if type == 'roberts': roberts_x = np.array([[-1, 0], [0, 1]]) roberts_y = np.array([[0, -1], [1, 0]]) gradient_x_roberts = conv2d(image, roberts_x) gradient_y_roberts = conv2d(image, roberts_y) edges_roberts = np.abs(gradient_x_roberts) + np.abs(gradient_y_roberts) edges_roberts = np.uint8(edges_roberts) return edges_roberts elif type == 'prewitt': prewitt_x = np.array([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]]) prewitt_y = np.array([[-1, -1, -1], [0, 0, 0], [1, 1, 1]]) gradient_x_prewitt = conv2d(image, prewitt_x) gradient_y_prewitt = conv2d(image, prewitt_y) edges_prewitt = np.abs(gradient_x_prewitt) + np.abs(gradient_y_prewitt) edges_prewitt = np.uint8(edges_prewitt) return edges_prewitt elif type == 'sobel': sobel_x = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]) sobel_y = np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]]) gradient_x_sobel = conv2d(image, sobel_x) gradient_y_sobel = conv2d(image, sobel_y) edges_sobel = np.abs(gradient_x_sobel) + np.abs(gradient_y_sobel) edges_sobel = np.uint8(edges_sobel) return edges_sobel elif type == 'canny': edges = cv2.Canny(image, threshold1=50, threshold2=100) return edges else: raise NotImplementedError ``` 主要的改动在于使用了自定义的 `conv2d` 函数替换了原来的 `cv2.filter2D` 函数。由于 `cv2.filter2D` 函数的实现方式与 `conv2d` 函数有所不同,因此替换后需要重新计算梯度,并对梯度进行绝对值处理和类型转换。

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def calc_gradient_penalty(self, netD, real_data, fake_data): alpha = torch.rand(1, 1) alpha = alpha.expand(real_data.size()) alpha = alpha.cuda() interpolates = alpha * real_data + ((1 - alpha) * fake_data) interpolates = interpolates.cuda() interpolates = Variable(interpolates, requires_grad=True) disc_interpolates, s = netD.forward(interpolates) s = torch.autograd.Variable(torch.tensor(0.0), requires_grad=True).cuda() gradients1 = autograd.grad(outputs=disc_interpolates, inputs=interpolates, grad_outputs=torch.ones(disc_interpolates.size()).cuda(), create_graph=True, retain_graph=True, only_inputs=True, allow_unused=True)[0] gradients2 = autograd.grad(outputs=s, inputs=interpolates, grad_outputs=torch.ones(s.size()).cuda(), create_graph=True, retain_graph=True, only_inputs=True, allow_unused=True)[0] if gradients2 is None: return None gradient_penalty = (((gradients1.norm(2, dim=1) - 1) ** 2).mean() * self.LAMBDA) + \ (((gradients2.norm(2, dim=1) - 1) ** 2).mean() * self.LAMBDA) return gradient_penalty def get_loss(self, net,fakeB, realB): self.D_fake, x = net.forward(fakeB.detach()) self.D_fake = self.D_fake.mean() self.D_fake = (self.D_fake + x).mean() # Real self.D_real, x = net.forward(realB) self.D_real = (self.D_real+x).mean() # Combined loss self.loss_D = self.D_fake - self.D_real gradient_penalty = self.calc_gradient_penalty(net, realB.data, fakeB.data) return self.loss_D + gradient_penalty,return self.loss_D + gradient_penalty出现错误:TypeError: unsupported operand type(s) for +: 'Tensor' and 'NoneType'

import numpy as np def sigmoid(x): # the sigmoid function return 1/(1+np.exp(-x)) class LogisticReg(object): def __init__(self, indim=1): # initialize the parameters with all zeros # w: shape of [d+1, 1] self.w = np.zeros((indim + 1, 1)) def set_param(self, weights, bias): # helper function to set the parameters # NOTE: you need to implement this to pass the autograde. # weights: vector of shape [d, ] # bias: scaler def get_param(self): # helper function to return the parameters # NOTE: you need to implement this to pass the autograde. # returns: # weights: vector of shape [d, ] # bias: scaler def compute_loss(self, X, t): # compute the loss # X: feature matrix of shape [N, d] # t: input label of shape [N, ] # NOTE: return the average of the log-likelihood, NOT the sum. # extend the input matrix # compute the loss and return the loss X_ext = np.concatenate((X, np.ones((X.shape[0], 1))), axis=1) # compute the log-likelihood def compute_grad(self, X, t): # X: feature matrix of shape [N, d] # grad: shape of [d, 1] # NOTE: return the average gradient, NOT the sum. def update(self, grad, lr=0.001): # update the weights # by the gradient descent rule def fit(self, X, t, lr=0.001, max_iters=1000, eps=1e-7): # implement the .fit() using the gradient descent method. # args: # X: input feature matrix of shape [N, d] # t: input label of shape [N, ] # lr: learning rate # max_iters: maximum number of iterations # eps: tolerance of the loss difference # TO NOTE: # extend the input features before fitting to it. # return the weight matrix of shape [indim+1, 1] def predict_prob(self, X): # implement the .predict_prob() using the parameters learned by .fit() # X: input feature matrix of shape [N, d] # NOTE: make sure you extend the feature matrix first, # the same way as what you did in .fit() method. # returns the prediction (likelihood) of shape [N, ] def predict(self, X, threshold=0.5): # implement the .predict() using the .predict_prob() method # X: input feature matrix of shape [N, d] # returns the prediction of shape [N, ], where each element is -1 or 1. # if the probability p>threshold, we determine t=1, otherwise t=-1

逐行详细解释以下代码并加注释from tensorflow import keras import matplotlib.pyplot as plt base_image_path = keras.utils.get_file( "coast.jpg", origin="https://img-datasets.s3.amazonaws.com/coast.jpg") plt.axis("off") plt.imshow(keras.utils.load_img(base_image_path)) #instantiating a model from tensorflow.keras.applications import inception_v3 model = inception_v3.InceptionV3(weights='imagenet',include_top=False) #配置各层对DeepDream损失的贡献 layer_settings = { "mixed4": 1.0, "mixed5": 1.5, "mixed6": 2.0, "mixed7": 2.5, } outputs_dict = dict( [ (layer.name, layer.output) for layer in [model.get_layer(name) for name in layer_settings.keys()] ] ) feature_extractor = keras.Model(inputs=model.inputs, outputs=outputs_dict) #定义损失函数 import tensorflow as tf def compute_loss(input_image): features = feature_extractor(input_image) loss = tf.zeros(shape=()) for name in features.keys(): coeff = layer_settings[name] activation = features[name] loss += coeff * tf.reduce_mean(tf.square(activation[:, 2:-2, 2:-2, :])) return loss #梯度上升过程 @tf.function def gradient_ascent_step(image, learning_rate): with tf.GradientTape() as tape: tape.watch(image) loss = compute_loss(image) grads = tape.gradient(loss, image) grads = tf.math.l2_normalize(grads) image += learning_rate * grads return loss, image def gradient_ascent_loop(image, iterations, learning_rate, max_loss=None): for i in range(iterations): loss, image = gradient_ascent_step(image, learning_rate) if max_loss is not None and loss > max_loss: break print(f"... Loss value at step {i}: {loss:.2f}") return image #hyperparameters step = 20. num_octave = 3 octave_scale = 1.4 iterations = 30 max_loss = 15. #图像处理方面 import numpy as np def preprocess_image(image_path): img = keras.utils.load_img(image_path) img = keras.utils.img_to_array(img) img = np.expand_dims(img, axis=0) img = keras.applications.inception_v3.preprocess_input(img) return img def deprocess_image(img): img = img.reshape((img.shape[1], img.shape[2], 3)) img /= 2.0 img += 0.5 img *= 255. img = np.clip(img, 0, 255).astype("uint8") return img #在多个连续 上运行梯度上升 original_img = preprocess_image(base_image_path) original_shape = original_img.shape[1:3] successive_shapes = [original_shape] for i in range(1, num_octave): shape = tuple([int(dim / (octave_scale ** i)) for dim in original_shape]) successive_shapes.append(shape) successive_shapes = successive_shapes[::-1] shrunk_original_img = tf.image.resize(original_img, successive_shapes[0]) img = tf.identity(original_img) for i, shape in enumerate(successive_shapes): print(f"Processing octave {i} with shape {shape}") img = tf.image.resize(img, shape) img = gradient_ascent_loop( img, iterations=iterations, learning_rate=step, max_loss=max_loss ) upscaled_shrunk_original_img = tf.image.resize(shrunk_original_img, shape) same_size_original = tf.image.resize(original_img, shape) lost_detail = same_size_original - upscaled_shrunk_original_img img += lost_detail shrunk_original_img = tf.image.resize(original_img, shape) keras.utils.save_img("DeepDream.png", deprocess_image(img.numpy()))

def train_step(real_ecg, dim): noise = tf.random.normal(dim) for i in range(disc_steps): with tf.GradientTape() as disc_tape: generated_ecg = generator(noise, training=True) real_output = discriminator(real_ecg, training=True) fake_output = discriminator(generated_ecg, training=True) disc_loss = discriminator_loss(real_output, fake_output) gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables) discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables)) ### for tensorboard ### disc_losses.update_state(disc_loss) fake_disc_accuracy.update_state(tf.zeros_like(fake_output), fake_output) real_disc_accuracy.update_state(tf.ones_like(real_output), real_output) ####################### with tf.GradientTape() as gen_tape: generated_ecg = generator(noise, training=True) fake_output = discriminator(generated_ecg, training=True) gen_loss = generator_loss(fake_output) gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables) generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables)) ### for tensorboard ### gen_losses.update_state(gen_loss) ####################### def train(dataset, epochs, dim): for epoch in tqdm(range(epochs)): for batch in dataset: train_step(batch, dim) disc_losses_list.append(disc_losses.result().numpy()) gen_losses_list.append(gen_losses.result().numpy()) fake_disc_accuracy_list.append(fake_disc_accuracy.result().numpy()) real_disc_accuracy_list.append(real_disc_accuracy.result().numpy()) ### for tensorboard ### # with disc_summary_writer.as_default(): # tf.summary.scalar('loss', disc_losses.result(), step=epoch) # tf.summary.scalar('fake_accuracy', fake_disc_accuracy.result(), step=epoch) # tf.summary.scalar('real_accuracy', real_disc_accuracy.result(), step=epoch) # with gen_summary_writer.as_default(): # tf.summary.scalar('loss', gen_losses.result(), step=epoch) disc_losses.reset_states() gen_losses.reset_states() fake_disc_accuracy.reset_states() real_disc_accuracy.reset_states() ####################### # Save the model every 5 epochs # if (epoch + 1) % 5 == 0: # generate_and_save_ecg(generator, epochs, seed, False) # checkpoint.save(file_prefix = checkpoint_prefix) # Generate after the final epoch display.clear_output(wait=True) generate_and_save_ecg(generator, epochs, seed, False)

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