import numpy as np def dense(a_in, W, b, g): units = W.shape[0] a_out = np.zeros(units) for j in range(units): w = W[j] z = np.dot(w, a_in) + b[j] a_out[j] = g(z) return a_out def sequential(x): W1 = np.array([[1,-3,5], [2,4,-6]]) b1 = np.array([-1,1,2]) W2 = np.array([[-1,2], [3,-4], [-5,6]]) b2 = np.array([2,-2]) W3 = np.array([[-2,1], [3,-4]]) b3 = np.array([1,-2]) W4 = np.array([[3,-1]]) b4 = np.array([-2]) a1 = dense(x, W1, b1, np.tanh) a2 = dense(a1, W2, b2, np.tanh) a3 = dense(a2, W3, b3, np.tanh) a4 = dense(a3, W4, b4, np.tanh) f_x = a4 return f_x a_in = np.array([-2, 4]) print(sequential(a_in))

python 3.74 运行import numpy as np 报错lib\site-packages\numpy\init.py

import numpy as np import os,sys #获取当前文件夹，并根据文件名 def path(fileName): p=sys.path[0]+'\\'+fileName return p #读文件 def readFile(fileName): f=open(path(fileName)) str=f.read() ...

import numpy as np import matplotlib.pyplot as plt import math

import numpy as np import matplotlib.pyplot as plt import math # 解决图标题中文乱码问题 import matplotlib as mpl mpl.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体 mpl.rcParams['axes....

import numpy as np_SPI干旱识别_spi_SPI6_matlabimportnumpy_测试_源码.zip

将下列代码补充成完整的程序：def dense(a_in,W,b,g): units=W.shape[1] a_out=np.zeros(units) for j in range(units): w=W[:j] z=np.dot(w,a_in)+b[j] a_out[j]=g(z) return a_out def sequential(x): a1=dense(x,W1,b1) a2=dense(a1,W2,b2) a3=dense(a2,W3,b3) a4=dense(a3,W4,b4) f_x=a4 return f_x W=np.array([[1,-3,5], [2,4,-6]]) b=np.array([-1,1,2]) a_in=np.array([-2,4])

import numpy as np def dense(a_in, W, b, g): units = W.shape[1] a_out = np.zeros(units) for j in range(units): w = W[:, j] z = np.dot(w, a_in) + b[j] a_out[j] = g(z) return a_out def ...

import numpy as np def dense(a_in, W, b, g): units = W.shape[1] a_out = np.zeros(units) for j in range(units): w = W[:,j] z = np.dot(w, a_in) + b[j] a_out[j] = g(z) return a_out def sequential(x): W1 = np.array([[1, 2], [3, 4]]) b1 = np.array([-1, -1]) W2 = np.array([[-3, 4], [5, -6]]) b2 = np.array([1, 1]) W3 = np.array([[7, -8], [-9, 10]]) b3 = np.array([2, 2]) a1 = dense(x, W1, b1, np.tanh) a2 = dense(a1, W2, b2, np.tanh) a3 = dense(a2, W3, b3, np.tanh) f_x = a3 return f_x a_in = np.array([-2, 4]) print(sequential(a_in))

具体来说，dense函数实现了一个全连接层，其输入为a_in，权重矩阵为W，偏置向量为b，激活函数为g。而sequential函数则将三个全连接层组成了一个三层神经网络，并对输入x进行前向传播，最终输出f_x。在这个例子中...

import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from keras.models import Sequential from keras.layers import Dense, LSTM from sklearn.metrics import r2_score,median_absolute_error,mean_absolute_error # 读取数据 data = pd.read_csv(r'C:/Users/Ljimmy/Desktop/yyqc/peijian/销量数据rnn.csv') # 取出特征参数 X = data.iloc[:,2:].values # 数据归一化 scaler = MinMaxScaler(feature_range=(0, 1)) X[:, 0] = scaler.fit_transform(X[:, 0].reshape(-1, 1)).flatten() #X = scaler.fit_transform(X) #scaler.fit(X) #X = scaler.transform(X) # 划分训练集和测试集 train_size = int(len(X) * 0.8) test_size = len(X) - train_size train, test = X[0:train_size, :], X[train_size:len(X), :] # 转换为监督学习问题 def create_dataset(dataset, look_back=1): X, Y = [], [] for i in range(len(dataset) - look_back - 1): a = dataset[i:(i + look_back), :] X.append(a) Y.append(dataset[i + look_back, 0]) return np.array(X), np.array(Y) look_back = 12 X_train, Y_train = create_dataset(train, look_back) #Y_train = train[:, 2:] # 取第三列及以后的数据 X_test, Y_test = create_dataset(test, look_back) #Y_test = test[:, 2:] # 取第三列及以后的数据 # 转换为3D张量 X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) # 构建LSTM模型 model = Sequential() model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1))) model.add(LSTM(units=50)) model.add(Dense(units=1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(X_train, Y_train, epochs=5, batch_size=32) #model.fit(X_train, Y_train.reshape(Y_train.shape[0], 1), epochs=10, batch_size=32) # 预测下一个月的销量 last_month_sales = data.tail(12).iloc[:,2:].values #last_month_sales = data.tail(1)[:,2:].values last_month_sales = scaler.transform(last_month_sales) last_month_sales = np.reshape(last_month_sales, (1, look_back, 1)) next_month_sales = model.predict(last_month_sales) next_month_sales = scaler.inverse_transform(next_month_sales) print('Next month sales: %.0f' % next_month_sales[0][0]) # 计算RMSE误差 rmse = np.sqrt(np.mean((next_month_sales - last_month_sales) ** 2)) print('Test RMSE: %.3f' % rmse)IndexError Traceback (most recent call last) Cell In[1], line 36 33 X_test, Y_test = create_dataset(test, look_back) 34 #Y_test = test[:, 2:] # 取第三列及以后的数据 35 # 转换为3D张量 ---> 36 X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) 37 X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) 38 # 构建LSTM模型 IndexError: tuple index out of range代码修改

import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from keras.models import Sequential from keras.layers import Dense, LSTM ...

import pandas as pd import numpy as np from sklearn.preprocessing import MinMaxScaler from keras.models import Sequential from keras.layers import Dense, LSTM import matplotlib.pyplot as plt # 读取CSV文件 data = pd.read_csv('77.csv', header=None) # 将数据集划分为训练集和测试集 train_size = int(len(data) * 0.7) train_data = data.iloc[:train_size, 1:2].values.reshape(-1,1) test_data = data.iloc[train_size:, 1:2].values.reshape(-1,1) # 对数据进行归一化处理 scaler = MinMaxScaler(feature_range=(0, 1)) train_data = scaler.fit_transform(train_data) test_data = scaler.transform(test_data) # 构建训练集和测试集 def create_dataset(dataset, look_back=1): X, Y = [], [] for i in range(len(dataset) - look_back): X.append(dataset[i:(i+look_back), 0]) Y.append(dataset[i+look_back, 0]) return np.array(X), np.array(Y) look_back = 3 X_train, Y_train = create_dataset(train_data, look_back) X_test, Y_test = create_dataset(test_data, look_back) # 转换为LSTM所需的输入格式 X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) # 构建LSTM模型 model = Sequential() model.add(LSTM(units=50, return_sequences=True, input_shape=(look_back, 1))) model.add(LSTM(units=50)) model.add(Dense(units=1)) model.compile(optimizer='adam', loss='mean_squared_error') model.fit(X_train, Y_train, epochs=100, batch_size=32) # 预测测试集并进行反归一化处理 Y_pred = model.predict(X_test) Y_pred = scaler.inverse_transform(Y_pred) Y_test = scaler.inverse_transform(Y_test) # 输出RMSE指标 rmse = np.sqrt(np.mean((Y_pred - Y_test)**2)) print('RMSE:', rmse) # 绘制训练集真实值和预测值图表 train_predict = model.predict(X_train) train_predict = scaler.inverse_transform(train_predict) train_actual = scaler.inverse_transform(Y_train.reshape(-1, 1)) plt.plot(train_actual, label='Actual') plt.plot(train_predict, label='Predicted') plt.title('Training Set') plt.xlabel('Time (h)') plt.ylabel('kWh') plt.legend() plt.show() # 绘制测试集真实值和预测值图表 plt.plot(Y_test, label='Actual') plt.plot(Y_pred, label='Predicted') plt.title('Testing Set') plt.xlabel('Time (h)') plt.ylabel('kWh') plt.legend() plt.show()以上代码运行时报错，错误为ValueError: Expected 2D array, got 1D array instead: array=[-0.04967795 0.09031832 0.07590125]. Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.如何进行修改

X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) 修改为： X_train, Y_train = create_dataset(train_...

import numpy as np from tensorflow import keras # 加载手写数字图像和标签 def load_data(): train_data = np.loadtxt('train_images.csv', delimiter=',') train_labels = np.loadtxt('train_labels.csv', delimiter=',') test_data = np.loadtxt('test_image.csv', delimiter=',') return train_data, train_labels, test_data # 数据预处理 def preprocess_data(train_data, test_data): # 归一化到 [0, 1] 范围 train_data = train_data / 255.0 test_data = test_data / 255.0 # 将数据 reshape 成适合 CNN 的输入形状 (样本数, 高度, 宽度, 通道数) train_data = train_data.reshape(-1, 28, 28, 1) test_data = test_data.reshape(-1, 28, 28, 1) return train_data, test_data # 构建 CNN 模型 def build_model(): model = keras.Sequential([ keras.layers.Conv2D(filters=32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)), keras.layers.MaxPooling2D(pool_size=(2, 2)), keras.layers.Flatten(), keras.layers.Dense(units=128, activation='relu'), keras.layers.Dense(units=10, activation='softmax') ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) return model # 进行数字识别 def recognize_digit(image, model): probabilities = model.predict(image) digit = np.argmax(probabilities) return digit # 主函数 def main(): # 加载数据 train_data, train_labels, test_data = load_data() # 数据预处理 train_data, test_data = preprocess_data(train_data, test_data) # 构建并训练模型 model = build_model() model.fit(train_data, train_labels, epochs=10, batch_size=32) # 进行数字识别 recognized_digit = recognize_digit(test_data, model) print("识别结果：", recognized_digit) if name == 'main': main()

1. 导入必要的库：numpy和tensorflow的keras模块。 2. 定义一个函数load_data()，用于加载手写数字图像和标签数据。 3. 定义一个函数preprocess_data()，用于对数据进行预处理，包括归一化和reshape操作。 4. 定义一...

import numpy as np import pandas as pd import tensorflow as tf from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import r2_score,median_absolute_error,mean_absolute_error # 读取数据 data = pd.read_csv(r'C:/Users/Ljimmy/Desktop/yyqc/peijian/销量数据rnn.csv') dataset = data.values # 数据归一化 scaler = MinMaxScaler(feature_range=(0, 1)) dataset = scaler.fit_transform(dataset) # 分割训练集和测试集 train_size = int(len(dataset) * 0.67) test_size = len(dataset) - train_size train, test = dataset[0:train_size, :], dataset[train_size:len(dataset), :] # 将数据集转化为适合GRU的数据格式 def create_dataset(dataset): X, Y = [], [] for i in range(len(dataset)-1): a = dataset[i:(i+1), :] X.append(a) Y.append(dataset[i+1, :]) return np.array(X), np.array(Y) train_X, train_Y = create_dataset(train) train_Y = train_Y[:, 2:] # 取第三列及以后的数据 test_X, test_Y = create_dataset(test) test_Y = test_Y[:, 2:] # 取第三列及以后的数据 # 定义GRU模型 model = tf.keras.Sequential([ tf.keras.layers.GRU(units=64, return_sequences=True, input_shape=(1, 3)), tf.keras.layers.GRU(units=32), tf.keras.layers.Dense(3)]) # 编译模型 model.compile(optimizer='adam', loss='mse') # 训练模型 model.fit(train_X, train_Y, epochs=100, batch_size=16, verbose=2) # 预测测试集 test_predict = model.predict(test_X) test_predict = scaler.inverse_transform(test_predict) #test_Y = scaler.inverse_transform(test_Y.reshape(-1, 1)) # 计算RMSE误差 rmse = np.sqrt(np.mean((test_predict - test_Y) ** 2)) print('Test RMSE:',rmse) # 预测下一个月的销量 last_month_sales = data.tail(1).values last_month_sales = scaler.transform(last_month_sales) next_month_sales = model.predict(np.array([last_month_sales])) next_month_sales = scaler.inverse_transform(next_month_sales) print('Next month sales:',next_month_sales[0][0])预测结果不够准确，如何增加准确率

有以下几种方式可以尝试提高准确率： 1. 增加训练数据量：可以尝试寻找更多的数据来进行训练，以提高模型的泛化能力和准确率。 2. 调整模型参数：可以尝试调整GRU模型的参数，如增加或减少GRU层的数量、增加或减少...

如果使用这段代码class SmoothFilter(Conv1D): def init(self, filter_size=3, kwargs): super().init( filters=1, kernel_size=filter_size, padding='same', activation='linear', use_bias=False, kwargs ) self.filter_size = filter_size def build(self, input_shape): super().build(input_shape) # 初始化卷积核 kernel = np.ones((self.filter_size, 1, 1)) kernel /= self.filter_size self.set_weights([kernel]) def call(self, inputs): return super().call(inputs)对dense层的一维度输出处理，编码怎么写呢

import numpy as np class SmoothFilter(Conv1D): def __init__(self, filter_size=3, **kwargs): super().__init__( filters=1, kernel_size=filter_size, padding='same', activation='linear', use_bias=...

请修改如下代码： def LSTNetAttention(trainX1, trainX2, trainY, config): # 定义输入层 input_data1 = Input(shape=(trainX1.shape[1], trainX1.shape[2])) input_data2 = Input(shape=(trainX2.shape[1], trainX2.shape[2])) # 定义 LSTM 模型 ... # 使用 GARCH 模型预测波动率 am = arch.arch_model(trainY, vol='GARCH', p=1, q=1) res = am.fit() volatility = res.forecast(horizon=1).variance.iloc[-1].values # 将预测结果和波动率相乘，得到最终的预测结果 pred = model.predict([trainX1, trainX2]) pred = pred * np.sqrt(volatility) # 将预测结果作为一个新的特征加入到模型中 new_feature = pred.reshape(-1, 1) # 重塑成 (batch_size, 1) 的形状 concat = concatenate([lstm_out1, lstm_out2, new_feature]) res = Activation('sigmoid')(concat) # 定义模型并返回 model = Model(inputs=[input_data1, input_data2], outputs=res) return model

import numpy as np import arch def LSTNetAttention(trainX1, trainX2, trainY, config): # 定义输入层 input_data1 = Input(shape=(trainX1.shape[1], trainX1.shape[2])) input_data2 = Input(shape=(trainX...

import numpy as np import matplotlib.pyplot as plt # 生成sin函数数据 x = np.arange(0, 2np.pi, 0.1) y = np.sin(x) # 可视化sin函数 plt.plot(x, y) plt.show() from keras.models import Sequential from keras.layers import Dense, SimpleRNN # 准备数据 dataX, dataY = [], [] for i in range(len(y)-1): dataX.append(y[i:i+1]) dataY.append(y[i+1]) dataX = np.array(dataX) dataY = np.array(dataY) # 划分训练集和测试集 train_size = int(len(dataY) 0.7) test_size = len(dataY) - train_size trainX, testX = np.array(dataX[0:train_size]), np.array(dataX[train_size:len(dataX)]) trainY, testY = np.array(dataY[0:train_size]), np.array(dataY[train_size:len(dataY)]) # 调整输入数据的形状 trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) # 定义模型结构 model = Sequential() model.add(SimpleRNN(units=10, input_shape=(1, 1))) model.add(Dense(units=1)) # 编译模型 model.compile(optimizer='adam', loss='mse') # 训练模型 history = model.fit(trainX, trainY, epochs=1000, validation_data=(testX, testY)) # 可视化损失函数 plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('Model Loss') plt.ylabel('Loss') plt.xlabel('Epoch') plt.legend(['Train', 'Test'], loc='upper right') plt.show()梯度可视化代码

from keras import backend as K def get_gradients(model, inputs, outputs, layer_name): layer_output = model.get_layer(layer_name).output gradients = K.gradients(outputs, layer_output)[0] gradient_...

# 最佳权重保存路径 BEST_MODEL_PATH = './best_model.h5'

import numpy as np import tensorflow as tf import os # 加载数据集 with open('poems.txt', 'r', encoding='utf-8') as f: data = f.read() # 构建词典 vocab = sorted(set(data)) char2idx = {char: idx for ...

batch_size = 32 num_classes = 2 epochs = 3 model = Sequential() model.add(Dense(512, activation='relu', input_shape=(25653,))) model.add(Dropout(0.2)) model.add(Dense(512, activation='relu')) model.add(Dropout(0.2)) model.add(Dense(num_classes, activation='softmax')) model.summary() model.compile(loss='categorical_crossentropy', optimizer=RMSprop(), metrics=['accuracy']) #模型训练 #verbose:z history = model.fit(X_train,y_train1, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(X_test, y_test1)) #模型打分 score = model.evaluate(X_test, y_test, verbose=0) print('Test loss:', score[0]) print('Test accuracy:', score[1]) 编写一个代码对上述模型调参并绘制正确率变化损失图

import numpy as np import matplotlib.pyplot as plt from keras.models import Sequential from keras.layers import Dense, Dropout from keras.optimizers import RMSprop 接下来，我们需要加载数据集并...

解决AttributeError: module 'tensorflow._api.v2.compat.v1.nn.rnn_cell' has no attribute 'seq2seq'

import tensorflow as tf # 定义编码器 class Encoder(tf.keras.Model): def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz): super(Encoder, self).__init__() self.batch_sz = batch_sz ...

上述代码仍然报错：shapes (2,) and (3,) not aligned: 2 (dim 0) != 3 (dim 0)

def dense(a_in, W, b, g): units = W.shape[0] a_out = np.zeros(units) for j in range(units): w = W[j] z = np.dot(w, a_in.T) + b[j] a_out[j] = g(z) return a_out def sequential(x): W1 = np.array...

上述代码仍然报错：shapes (3,) and (2,) not aligned: 3 (dim 0) != 2 (dim 0

def dense(a_in, W, b, g): units = W.shape[0] a_out = np.zeros(units) for j in range(units): w = W[j] z = np.dot(w, a_in) + b[j] a_out[j] = g(z) return a_out def sequential(x): W1 = np.array(...

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解决AttributeError: module 'tensorflow._api.v2.compat.v1.nn.rnn_cell' has no attribute 'seq2seq'

上述代码仍然报错：shapes (2,) and (3,) not aligned: 2 (dim 0) != 3 (dim 0)

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