def init_toy_data(): np.random.seed(1) X = 10 * np.random.randn(num_inputs, input_size) y = np.array([0, 1, 2, 2, 1]) return X, y

np.random.seed() 的使用详解

在学习人工智能时，大量的使用了np.random.seed()，利用随机数种子，使得每次生成的随机数相同。我们带着2个问题来进行下列实验 np.random.seed()是否一直有效 np.random.seed(Argument)的参数作用? 例子1 import numpy as np if __name__ == '__main__': i = 0 while (i < 6): if (i < 3): np.random.seed(0) print(np.random.randn(1, 5)) else: print(np.random.ran

def init(self, input_dim=(3, 32, 32), num_filters=32, filter_size=7, hidden_dim=100, num_classes=10, weight_scale=1e-3, reg=0.0, dtype=np.float32): self.params = {} self.reg = reg self.dtype = dtype # Initialize weights and biases C, H, W = input_dim self.params['W1'] = weight_scale * np.random.randn(num_filters, C, filter_size, filter_size) self.params['b1'] = np.zeros(num_filters) self.params['W2'] = weight_scale * np.random.randn(num_filters * H * W // 4, hidden_dim) self.params['b2'] = np.zeros(hidden_dim) self.params['W3'] = weight_scale * np.random.randn(hidden_dim, num_classes) self.params['b3'] = np.zeros(num_classes) for k, v in self.params.items(): self.params[k] = v.astype(dtype)定义了W吗

其中，第一层卷积层的权重参数W1的形状为(num_filters, C, filter_size, filter_size)，即(num_filters, 输入数据通道数C, 卷积核大小filter_size, 卷积核大小filter_size)，第二层全连接层的权重参数W2的形状为(num...

class NeuralNetwork: def init(self, input_dim, hidden_dim, output_dim): self.input_dim = input_dim self.hidden_dim = hidden_dim self.output_dim = output_dim self.weights1 = np.random.randn(input_dim, hidden_dim) self.bias1 = np.zeros((1, hidden_dim)) self.weights2 = np.random.randn(hidden_dim, output_dim) self.bias2 = np.zeros((1, output_dim)) def relu(self, x): return np.maximum(0, x) def relu_derivative(self, x): return np.where(x >= 0, 1, 0) def forward(self, x): self.z1 = np.dot(x, self.weights1) + self.bias1 self.a1 = self.relu(self.z1) self.z2 = np.dot(self.a1, self.weights2) + self.bias2 self.y_hat = self.z2 return self.y_hat def backward(self, x, y, learning_rate): error = self.y_hat - y delta2 = error delta1 = np.dot(delta2, self.weights2.T) * self.relu_derivative(self.a1) grad_weights2 = np.dot(self.a1.T, delta2) grad_bias2 = np.sum(delta2, axis=0, keepdims=True) grad_weights1 = np.dot(x.T, delta1) grad_bias1 = np.sum(delta1, axis=0) self.weights2 -= learning_rate * grad_weights2 self.bias2 -= learning_rate * grad_bias2 self.weights1 -= learning_rate * grad_weights1 根据代码加上损失函数和优化

self.weights1 = np.random.randn(input_dim, hidden_dim) self.bias1 = np.zeros((1, hidden_dim)) self.weights2 = np.random.randn(hidden_dim, output_dim) self.bias2 = np.zeros((1, output_dim)) def ...

class ThreeLayerConvNet(object): def init(self, input_dim=(3, 32, 32), num_filters=32, filter_size=7, hidden_dim=100, num_classes=10, weight_scale=1e-3, reg=0.0, dtype=np.float32): self.params = {} self.reg = reg self.dtype = dtype # Initialize weights and biases C, H, W = input_dim self.params['W1'] = weight_scale * np.random.randn(num_filters, C, filter_size, filter_size) self.params['b1'] = np.zeros(num_filters) self.params['W2'] = weight_scale * np.random.randn(num_filters * H * W // 4, hidden_dim) self.params['b2'] = np.zeros(hidden_dim) self.params['W3'] = weight_scale * np.random.randn(hidden_dim, num_classes) self.params['b3'] = np.zeros(num_classes) for k, v in self.params.items(): self.params[k] = v.astype(dtype)用于mnist识别中，应该怎么修改

def __init__(self, input_dim=(1, 28, 28), num_filters=32, filter_size=7, hidden_dim=100, num_classes=10, weight_scale=1e-4, reg=0.0, dtype=np.float32): self.params = {} self.reg = reg self.dtype...

pre_node_nums = np.array([133, 1633, 1633, 3233, 3233, 6433, 6433, hidden_size]) wight_init_scales = np.sqrt(2.0 / pre_node_nums) # 使用ReLU的情况下推荐的初始值 self.params = {} pre_channel_num = input_dim[0] for idx, conv_param in enumerate([conv_param_1, conv_param_2, conv_param_3, conv_param_4, conv_param_5, conv_param_6]): self.params['W' + str(idx+1)] = wight_init_scales[idx] * np.random.randn(conv_param['filter_num'], pre_channel_num, conv_param['filter_size'], conv_param['filter_size']) self.params['b' + str(idx+1)] = np.zeros(conv_param['filter_num']) pre_channel_num = conv_param['filter_num'] self.params['W7'] = wight_init_scales[6] * np.random.randn(6444, hidden_size) self.params['b7'] = np.zeros(hidden_size) self.params['W8'] = wight_init_scales[7] * np.random.randn(hidden_size, output_size) self.params['b8'] = np.zeros(output_size)

这段代码是神经网络模型的参数初始化函数。根据输入数据的维度和卷积层的参数，初始化卷积层和全连接层的权重和偏置。 pre_node_nums是一个包含了所有层的输入节点数量的数组，根据这个数组和ReLU激活函数的特性，...

代码解释：class Network(object): def init(self, sizes): self.num_layers = len(sizes) self.sizes = sizes self.biases = [np.random.randn(y, 1) for y in sizes[1:]] self.weights = [np.random.randn(y, x) for x, y in zip(sizes[:-1], sizes[1:])]

这个代码段是一个定义了网络结构的类，并在初始化方法中，根据给定的sizes参数设置网络的层数、每一层的神经元个数、偏置项和权重矩阵，并利用np.random.randn函数生成随机的偏置向量和权重矩阵。其中，sizes参数是...

import numpy as np class BPNeuralNetwork: def init(self, input_size, hidden_size, output_size): self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.weights1 = np.random.randn(self.input_size, self.hidden_size) self.bias1 = np.zeros((1, self.hidden_size)) self.weights2 = np.random.randn(self.hidden_size, self.output_size) self.bias2 = np.zeros((1, self.output_size)) def forward(self, X): self.hidden_layer = np.dot(X, self.weights1) + self.bias1 self.activated_hidden_layer = self.sigmoid(self.hidden_layer) self.output_layer = np.dot(self.activated_hidden_layer, self.weights2) + self.bias2 self.activated_output_layer = self.sigmoid(self.output_layer) return self.activated_output_layer def sigmoid(self, s): return 1 / (1 + np.exp(-s)) def sigmoid_derivative(self, s): return s * (1 - s) def backward(self, X, y, o, learning_rate): self.error = y - o self.delta_output = self.error * self.sigmoid_derivative(o) self.error_hidden = self.delta_output.dot(self.weights2.T) self.delta_hidden = self.error_hidden * self.sigmoid_derivative(self.activated_hidden_layer) self.weights1 += X.T.dot(self.delta_hidden) * learning_rate self.bias1 += np.sum(self.delta_hidden, axis=0, keepdims=True) * learning_rate self.weights2 += self.activated_hidden_layer.T.dot(self.delta_output) * learning_rate self.bias2 += np.sum(self.delta_output, axis=0, keepdims=True) * learning_rate def train(self, X, y, learning_rate, epochs): for epoch in range(epochs): output = self.forward(X) self.backward(X, y, output, learning_rate) def predict(self, X): return self.forward(X) X = np.array([[0, 0, 1], [0, 1, 1], [1, 0, 1], [1, 1, 1]]) y = np.array([[0], [1], [1], [0]]) nn = BPNeuralNetwork(3, 4, 1) nn.train(X, y, 0.1, 10000) new_data = np.array([[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 1, 0]]) print(nn.predict(new_data))

它的训练数据X是一个4x3的矩阵，y是一个4x1的矩阵。训练过程中，使用随机初始化的权重和偏置，对训练数据进行前向传播，计算输出值，在计算误差，并使用反向传播算法更新权重和偏置，最终得到一个可以进行二分类的...

class Network(object): def init(self, num_of_weights): # 随机产生w的初始值 # 为了保持程序每次运行结果的一致性， # 此处设置固定的随机数种子 np.random.seed(0) self.w = np.random.randn(num_of_weights, 1) self.b = 0. def forward(self, x): z = np.dot(x, self.w) + self.b return z

这是一个类，命名为...它有一个初始化函数__init__，传入一个参数num_of_weights。该函数是初始化类的方法，用于设置类的属性或者完成其他必要的初始化工作。在这个类中，初始化函数需要接收一个权重数目的参数。

class NeuralNetwork: def init(self, n_inputs, n_hidden, n_outputs): self.n_inputs = n_inputs self.n_hidden = n_hidden self.n_outputs = n_outputs # 初始化权重和偏差 self.weights1 = np.random.randn(self.n_inputs, self.n_hidden) self.bias1 = np.zeros((1, self.n_hidden)) self.weights2 = np.random.randn(self.n_hidden, self.n_outputs) self.bias2 = np.zeros((1, self.n_outputs)) def sigmoid(self, z): return 1 / (1 + np.exp(-z)) def sigmoid_derivative(self, z): return self.sigmoid(z) * (1 - self.sigmoid(z)) def feedforward(self, X): # 计算隐藏层输出 self.z1 = np.dot(X, self.weights1) + self.bias1 self.a1 = self.sigmoid(self.z1) # 计算输出层输出 self.z2 = np.dot(self.a1, self.weights2) + self.bias2 self.a2 = self.sigmoid(self.z2) return self.a2 def backpropagation(self, X, y, output): # 计算输出层误差 error = output - y d_output = error * self.sigmoid_derivative(self.z2) # 计算隐藏层误差 error_hidden = d_output.dot(self.weights2.T) d_hidden = error_hidden * self.sigmoid_derivative(self.z1) # 更新权重和偏差 self.weights2 -= self.a1.T.dot(d_output) self.bias2 -= np.sum(d_output, axis=0, keepdims=True) self.weights1 -= X.T.dot(d_hidden) self.bias1 -= np.sum(d_hidden, axis=0) def train(self, X, y, n_epochs, learning_rate): for epoch in range(n_epochs): output = self.feedforward(X) self.backpropagation(X, y, output) def predict(self, X): output = self.feedforward(X) predictions = np.argmax(output, axis=1) return predictions

这是一个简单的神经网络实现，包括初始化权重和偏差、前向传播、反向传播、训练和预测。神经网络的训练过程中，通常需要一些超参数的设置，如隐藏层的神经元数量、迭代次数、学习率等。在这个实现中，隐藏层神经...

翻译这段程序并自行赋值调用：import matplotlib.pyplot as plt import numpy as np import sklearn import sklearn.datasets import sklearn.linear_model def plot_decision_boundary(model, X, y): # Set min and max values and give it some padding x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1 y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1 h = 0.01 # Generate a grid of points with distance h between them xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) # Predict the function value for the whole grid Z = model(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) # Plot the contour and training examples plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral) plt.ylabel('x2') plt.xlabel('x1') plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral) def sigmoid(x): s = 1/(1+np.exp(-x)) return s def load_planar_dataset(): np.random.seed(1) m = 400 # number of examples N = int(m/2) # number of points per class print(np.random.randn(N)) D = 2 # dimensionality X = np.zeros((m,D)) # data matrix where each row is a single example Y = np.zeros((m,1), dtype='uint8') # labels vector (0 for red, 1 for blue) a = 4 # maximum ray of the flower for j in range(2): ix = range(Nj,N(j+1)) t = np.linspace(j3.12,(j+1)3.12,N) + np.random.randn(N)0.2 # theta r = anp.sin(4t) + np.random.randn(N)0.2 # radius X[ix] = np.c_[rnp.sin(t), rnp.cos(t)] Y[ix] = j X = X.T Y = Y.T return X, Y def load_extra_datasets(): N = 200 noisy_circles = sklearn.datasets.make_circles(n_samples=N, factor=.5, noise=.3) noisy_moons = sklearn.datasets.make_moons(n_samples=N, noise=.2) blobs = sklearn.datasets.make_blobs(n_samples=N, random_state=5, n_features=2, centers=6) gaussian_quantiles = sklearn.datasets.make_gaussian_quantiles(mean=None, cov=0.5, n_samples=N, n_features=2, n_classes=2, shuffle=True, random_state=None) no_structure = np.random.rand(N, 2), np.random.rand(N, 2) return noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure

np.random.seed(1) m = 400 # 样本数量 N = int(m / 2) # 每个类的样本数量 # 生成数据集 D = 2 # 特征维度 X = np.zeros((m, D)) # 特征矩阵 Y = np.zeros((m, 1), dtype='uint8') # 标签向量 a = 4 # 花...

import numpy as np class BPNeuralNetwork: def init(self, input_size, hidden_size, output_size): self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size # 初始化权重和偏置 self.weights_ih = np.random.randn(hidden_size, input_size) self.bias_ih = np.random.randn(hidden_size, 1) self.weights_ho = np.random.randn(output_size, hidden_size) self.bias_ho = np.random.randn(output_size, 1) # 定义激活函数 self.activation = lambda x: 1 / (1 + np.exp(-x)) self.derivative = lambda x: x * (1 - x) def forward(self, inputs): # 计算隐藏层的输出 hidden = self.activation(np.dot(self.weights_ih, inputs) + self.bias_ih) # 计算输出层的输出 output = self.activation(np.dot(self.weights_ho, hidden) + self.bias_ho) return output def backward(self, inputs, targets, output): # 计算输出层的误差 output_error = targets - output output_delta = output_error * self.derivative(output) # 计算隐藏层的误差 hidden_error = np.dot(self.weights_ho.T, output_delta) hidden_delta = hidden_error * self.derivative(hidden) # 更新权重和偏置 self.weights_ho += np.dot(output_delta, hidden.T) self.bias_ho += output_delta self.weights_ih += np.dot(hidden_delta, inputs.T) self.bias_ih += hidden_delta def train(self, inputs, targets, epochs): for i in range(epochs): for j in range(len(inputs)): # 前向传播 output = self.forward(inputs[j].reshape(-1, 1)) # 反向传播 self.backward(inputs[j].reshape(-1, 1), targets[j].reshape(-1, 1), output)

1. 初始化函数__init__中，定义了输入层、隐藏层和输出层的节点数，以及它们之间的权重和偏置。其中，权重和偏置都是随机初始化的。 2. 定义了激活函数activation和导数函数derivative，这里采用的是Sigmoid函数。 ...

x = np.random.randn(100) ax = sns.displot(x) x = np.random.randn(100) ax = sns.displot(x) x = np.random.randn(100) ax = sns.displot(x) x = np.random.randn(100) ax = sns.displot(x) x = np.random.randn(100)ax = sns.displot(x) 写到pycharm里，pycharm为什么不出现图

x = np.random.randn(100) ax = sns.displot(x) plt.show() x = np.random.randn(100) ax = sns.displot(x) plt.show() x = np.random.randn(100) ax = sns.displot(x) plt.show() x = np.random.randn(100) ax =...

class DBN(object): def init(self, sizes): self.sizes = sizes self.num_layers = len(sizes) self.biases = [np.random.randn(y, 1) for y in sizes[1:]] self.weights = [np.random.randn(y, x) / np.sqrt(x) for x, y in zip(sizes[:-1], sizes[1:])] def sigmoid(self, z): return 1.0 / (1.0 + np.exp(-z)) def feedforward(self, a): for b, w in zip(self.biases, self.weights): a = self.sigmoid(np.dot(w, a) + b) return a def predict(self, X): y_pred = np.zeros_like(y_test) for i in range(len(X)): y_pred[i] = np.argmax(self.feedforward(X[i].reshape(-1, 1))) return y_pred def train(self, dbn_training_data, dbn_epochs): pass

这是一个深度置信网络（Deep Belief Network）的类定义，包含了初始化、sigmoid函数、前向传播、预测和训练等方法。其中，sizes参数表示每层神经元的数量，biases和weights分别表示偏置和权重。...

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代码解释：class Network(object): def init(self, sizes): self.num_layers = len(sizes) self.sizes = sizes self.biases = [np.random.randn(y, 1) for y in sizes[1:]] self.weights = [np.random.randn(y, x) for x, y in zip(sizes[:-1], sizes[1:])]